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timestamp[ns]date 2023-05-05 00:00:00
2025-09-12 00:00:00
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2025-08-19T00:00:00 |
2508.13968
|
RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation
|
[
"Tianyi Niu",
"Jaemin Cho",
"Elias Stengel-Eskin",
"Mohit Bansal"
] |
We investigate to what extent Multimodal Large Language Models (MLLMs) can accurately identify the orientation of input images rotated 0{\deg}, 90{\deg}, 180{\deg}, and 270{\deg}. This task demands robust visual reasoning capabilities to detect rotational cues and contextualize spatial relationships within images, regardless of their orientation. To evaluate MLLMs on these abilities, we introduce RotBench -- a 350-image manually-filtered benchmark comprising lifestyle, portrait, and landscape images. Despite the relatively simple nature of this task, we show that several state-of-the-art open and proprietary MLLMs, including GPT-5, o3, and Gemini-2.5-Pro, do not reliably identify rotation in input images. Providing models with auxiliary information -- including captions, depth maps, and more -- or using chain-of-thought prompting offers only small and inconsistent improvements. Our results indicate that most models are able to reliably identify right-side-up (0{\deg}) images, while certain models are able to identify upside-down (180{\deg}) images. None can reliably distinguish between 90{\deg} and 270{\deg}. Simultaneously showing the image rotated in different orientations leads to moderate performance gains for reasoning models, while a modified setup using voting improves the performance of weaker models. We further show that fine-tuning does not improve models' ability to distinguish 90{\deg} and 270{\deg} rotations, despite substantially improving the identification of 180{\deg} images. Together, these results reveal a significant gap between MLLMs' spatial reasoning capabilities and human perception in identifying rotation.
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|
2025-08-20T00:00:00 |
2508.13632
|
OmniTry: Virtual Try-On Anything without Masks
|
[
"Yutong Feng",
"Linlin Zhang",
"Hengyuan Cao",
"Yiming Chen",
"Xiaoduan Feng",
"Jian Cao",
"Yuxiong Wu",
"Bin Wang"
] |
Virtual Try-ON (VTON) is a practical and widely-applied task, for which most of existing works focus on clothes. This paper presents OmniTry, a unified framework that extends VTON beyond garment to encompass any wearable objects, e.g., jewelries and accessories, with mask-free setting for more practical application. When extending to various types of objects, data curation is challenging for obtaining paired images, i.e., the object image and the corresponding try-on result. To tackle this problem, we propose a two-staged pipeline: For the first stage, we leverage large-scale unpaired images, i.e., portraits with any wearable items, to train the model for mask-free localization. Specifically, we repurpose the inpainting model to automatically draw objects in suitable positions given an empty mask. For the second stage, the model is further fine-tuned with paired images to transfer the consistency of object appearance. We observed that the model after the first stage shows quick convergence even with few paired samples. OmniTry is evaluated on a comprehensive benchmark consisting of 12 common classes of wearable objects, with both in-shop and in-the-wild images. Experimental results suggest that OmniTry shows better performance on both object localization and ID-preservation compared with existing methods. The code, model weights, and evaluation benchmark of OmniTry will be made publicly available at https://omnitry.github.io/.
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2025-08-20T00:00:00 |
2508.09131
|
Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer
|
[
"Zixin Yin",
"Xili Dai",
"Ling-Hao Chen",
"Deyu Zhou",
"Jianan Wang",
"Duomin Wang",
"Gang Yu",
"Lionel M. Ni",
"Lei Zhang",
"Heung-Yeung Shum"
] |
Text-guided color editing in images and videos is a fundamental yet unsolved problem, requiring fine-grained manipulation of color attributes, including albedo, light source color, and ambient lighting, while preserving physical consistency in geometry, material properties, and light-matter interactions. Existing training-free methods offer broad applicability across editing tasks but struggle with precise color control and often introduce visual inconsistency in both edited and non-edited regions. In this work, we present ColorCtrl, a training-free color editing method that leverages the attention mechanisms of modern Multi-Modal Diffusion Transformers (MM-DiT). By disentangling structure and color through targeted manipulation of attention maps and value tokens, our method enables accurate and consistent color editing, along with word-level control of attribute intensity. Our method modifies only the intended regions specified by the prompt, leaving unrelated areas untouched. Extensive experiments on both SD3 and FLUX.1-dev demonstrate that ColorCtrl outperforms existing training-free approaches and achieves state-of-the-art performances in both edit quality and consistency. Furthermore, our method surpasses strong commercial models such as FLUX.1 Kontext Max and GPT-4o Image Generation in terms of consistency. When extended to video models like CogVideoX, our approach exhibits greater advantages, particularly in maintaining temporal coherence and editing stability. Finally, our method also generalizes to instruction-based editing diffusion models such as Step1X-Edit and FLUX.1 Kontext dev, further demonstrating its versatility.
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2025-08-20T00:00:00 |
2508.13167
|
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL
|
[
"Weizhen Li",
"Jianbo Lin",
"Zhuosong Jiang",
"Jingyi Cao",
"Xinpeng Liu",
"Jiayu Zhang",
"Zhenqiang Huang",
"Qianben Chen",
"Weichen Sun",
"Qiexiang Wang",
"Hongxuan Lu",
"Tianrui Qin",
"Chenghao Zhu",
"Yi Yao",
"Shuying Fan",
"Xiaowan Li",
"Tiannan Wang",
"Pai Liu",
"King Zhu",
"He Zhu",
"Dingfeng Shi",
"Piaohong Wang",
"Yeyi Guan",
"Xiangru Tang",
"Minghao Liu",
"Yuchen Eleanor Jiang",
"Jian Yang",
"Jiaheng Liu",
"Ge Zhang",
"Wangchunshu Zhou"
] |
Recent advances in large language models (LLMs) and multi-agent systems have demonstrated remarkable capabilities in complex problem-solving tasks such as deep research, vibe coding, and mathematical reasoning. However, most existing multi-agent systems are built upon manual prompt/workflow engineering with sophisticated agent frameworks, making them computationally inefficient, less capable, and can not benefit from data-centric learning. In this work, we introduce Chain-of-Agents (CoA), a novel paradigm of LLM reasoning that enables native end-to-end complex problem-solving in the same way as a multi-agent system (i.e., multi-turn problem solving with multiple tools and multiple agents) within one model. In chain-of-agents problem-solving, the model dynamically activates different tool agents and role-playing agents to simulate multi-agent collaboration in an end-to-end fashion. To elicit end-to-end chain-of-agents problem-solving abilities in LLMs, we introduce a multi-agent distillation framework to distill state-of-the-art multi-agent systems into chain-of-agents trajectories for agentic supervised fine-tuning. We then use agentic reinforcement learning on verifiable agentic tasks to further improve the models' capabilities on chain-of-agents problem solving. We call the resulting models Agent Foundation Models (AFMs). Our empirical studies demonstrate that AFM establishes new state-of-the-art performance across diverse benchmarks in both web agent and code agent settings. We make the entire research, including the model weights, code for training and evaluation, and the training data, fully open-sourced, which offers a solid starting point for future research on agent models and agentic RL.
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|
2025-08-20T00:00:00 |
2508.14041
|
LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
|
[
"Chin-Yang Lin",
"Cheng Sun",
"Fu-En Yang",
"Min-Hung Chen",
"Yen-Yu Lin",
"Yu-Lun Liu"
] |
LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gaussians to avoid local minima and ensure global consistency; (2) a robust Pose Estimation Module leveraging learned 3D priors; and (3) an efficient Octree Anchor Formation mechanism that converts dense point clouds into anchors based on spatial density. Extensive experiments on challenging benchmarks demonstrate that LongSplat achieves state-of-the-art results, substantially improving rendering quality, pose accuracy, and computational efficiency compared to prior approaches. Project page: https://linjohnss.github.io/longsplat/
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2025-08-20T00:00:00 |
2508.13948
|
Prompt Orchestration Markup Language
|
[
"Yuge Zhang",
"Nan Chen",
"Jiahang Xu",
"Yuqing Yang"
] |
Large Language Models (LLMs) require sophisticated prompting, yet current practices face challenges in structure, data integration, format sensitivity, and tooling. Existing methods lack comprehensive solutions for organizing complex prompts involving diverse data types (documents, tables, images) or managing presentation variations systematically. To address these gaps, we introduce POML (Prompt Orchestration Markup Language). POML employs component-based markup for logical structure (roles, tasks, examples), specialized tags for seamless data integration, and a CSS-like styling system to decouple content from presentation, reducing formatting sensitivity. It includes templating for dynamic prompts and a comprehensive developer toolkit (IDE support, SDKs) to improve version control and collaboration. We validate POML through two case studies demonstrating its impact on complex application integration (PomLink) and accuracy performance (TableQA), as well as a user study assessing its effectiveness in real-world development scenarios.
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|
2025-08-20T00:00:00 |
2508.13992
|
MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence
|
[
"Sonal Kumar",
"Šimon Sedláček",
"Vaibhavi Lokegaonkar",
"Fernando López",
"Wenyi Yu",
"Nishit Anand",
"Hyeonggon Ryu",
"Lichang Chen",
"Maxim Plička",
"Miroslav Hlaváček",
"William Fineas Ellingwood",
"Sathvik Udupa",
"Siyuan Hou",
"Allison Ferner",
"Sara Barahona",
"Cecilia Bolaños",
"Satish Rahi",
"Laura Herrera-Alarcón",
"Satvik Dixit",
"Siddhi Patil",
"Soham Deshmukh",
"Lasha Koroshinadze",
"Yao Liu",
"Leibny Paola Garcia Perera",
"Eleni Zanou",
"Themos Stafylakis",
"Joon Son Chung",
"David Harwath",
"Chao Zhang",
"Dinesh Manocha",
"Alicia Lozano-Diez",
"Santosh Kesiraju",
"Sreyan Ghosh",
"Ramani Duraiswami"
] |
Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio understanding to qualify as generally intelligent. However, evaluating auditory intelligence comprehensively remains challenging. To address this gap, we introduce MMAU-Pro, the most comprehensive and rigorously curated benchmark for assessing audio intelligence in AI systems. MMAU-Pro contains 5,305 instances, where each instance has one or more audios paired with human expert-generated question-answer pairs, spanning speech, sound, music, and their combinations. Unlike existing benchmarks, MMAU-Pro evaluates auditory intelligence across 49 unique skills and multiple complex dimensions, including long-form audio comprehension, spatial audio reasoning, multi-audio understanding, among others. All questions are meticulously designed to require deliberate multi-hop reasoning, including both multiple-choice and open-ended response formats. Importantly, audio data is sourced directly ``from the wild" rather than from existing datasets with known distributions. We evaluate 22 leading open-source and proprietary multimodal AI models, revealing significant limitations: even state-of-the-art models such as Gemini 2.5 Flash and Audio Flamingo 3 achieve only 59.2% and 51.7% accuracy, respectively, approaching random performance in multiple categories. Our extensive analysis highlights specific shortcomings and provides novel insights, offering actionable perspectives for the community to enhance future AI systems' progression toward audio general intelligence. The benchmark and code is available at https://sonalkum.github.io/mmau-pro.
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|
2025-08-20T00:00:00 |
2508.12669
|
Leveraging Large Language Models for Predictive Analysis of Human Misery
|
[
"Bishanka Seal",
"Rahul Seetharaman",
"Aman Bansal",
"Abhilash Nandy"
] |
https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub
|
This study investigates the use of Large Language Models (LLMs) for predicting human-perceived misery scores from natural language descriptions of real-world scenarios. The task is framed as a regression problem, where the model assigns a scalar value from 0 to 100 to each input statement. We evaluate multiple prompting strategies, including zero-shot, fixed-context few-shot, and retrieval-based prompting using BERT sentence embeddings. Few-shot approaches consistently outperform zero-shot baselines, underscoring the value of contextual examples in affective prediction. To move beyond static evaluation, we introduce the "Misery Game Show", a novel gamified framework inspired by a television format. It tests LLMs through structured rounds involving ordinal comparison, binary classification, scalar estimation, and feedback-driven reasoning. This setup enables us to assess not only predictive accuracy but also the model's ability to adapt based on corrective feedback. The gamified evaluation highlights the broader potential of LLMs in dynamic emotional reasoning tasks beyond standard regression. Code and data link: https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub
|
2025-08-20T00:00:00 |
2508.06905
|
MultiRef: Controllable Image Generation with Multiple Visual References
|
[
"Ruoxi Chen",
"Dongping Chen",
"Siyuan Wu",
"Sinan Wang",
"Shiyun Lang",
"Petr Sushko",
"Gaoyang Jiang",
"Yao Wan",
"Ranjay Krishna"
] |
Visual designers naturally draw inspiration from multiple visual references, combining diverse elements and aesthetic principles to create artwork. However, current image generative frameworks predominantly rely on single-source inputs -- either text prompts or individual reference images. In this paper, we focus on the task of controllable image generation using multiple visual references. We introduce MultiRef-bench, a rigorous evaluation framework comprising 990 synthetic and 1,000 real-world samples that require incorporating visual content from multiple reference images. The synthetic samples are synthetically generated through our data engine RefBlend, with 10 reference types and 33 reference combinations. Based on RefBlend, we further construct a dataset MultiRef containing 38k high-quality images to facilitate further research. Our experiments across three interleaved image-text models (i.e., OmniGen, ACE, and Show-o) and six agentic frameworks (e.g., ChatDiT and LLM + SD) reveal that even state-of-the-art systems struggle with multi-reference conditioning, with the best model OmniGen achieving only 66.6% in synthetic samples and 79.0% in real-world cases on average compared to the golden answer. These findings provide valuable directions for developing more flexible and human-like creative tools that can effectively integrate multiple sources of visual inspiration. The dataset is publicly available at: https://multiref.github.io/.
|
|
2025-08-20T00:00:00 |
2508.13139
|
Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence
|
[
"Ling-Hao Chen",
"Yuhong Zhang",
"Zixin Yin",
"Zhiyang Dou",
"Xin Chen",
"Jingbo Wang",
"Taku Komura",
"Lei Zhang"
] |
This work studies the challenge of transfer animations between characters whose skeletal topologies differ substantially. While many techniques have advanced retargeting techniques in decades, transfer motions across diverse topologies remains less-explored. The primary obstacle lies in the inherent topological inconsistency between source and target skeletons, which restricts the establishment of straightforward one-to-one bone correspondences. Besides, the current lack of large-scale paired motion datasets spanning different topological structures severely constrains the development of data-driven approaches. To address these limitations, we introduce Motion2Motion, a novel, training-free framework. Simply yet effectively, Motion2Motion works with only one or a few example motions on the target skeleton, by accessing a sparse set of bone correspondences between the source and target skeletons. Through comprehensive qualitative and quantitative evaluations, we demonstrate that Motion2Motion achieves efficient and reliable performance in both similar-skeleton and cross-species skeleton transfer scenarios. The practical utility of our approach is further evidenced by its successful integration in downstream applications and user interfaces, highlighting its potential for industrial applications. Code and data are available at https://lhchen.top/Motion2Motion.
|
|
2025-08-20T00:00:00 |
2508.04324
|
TempFlow-GRPO: When Timing Matters for GRPO in Flow Models
|
[
"Xiaoxuan He",
"Siming Fu",
"Yuke Zhao",
"Wanli Li",
"Jian Yang",
"Dacheng Yin",
"Fengyun Rao",
"Bo Zhang"
] |
Recent flow matching models for text-to-image generation have achieved remarkable quality, yet their integration with reinforcement learning for human preference alignment remains suboptimal, hindering fine-grained reward-based optimization. We observe that the key impediment to effective GRPO training of flow models is the temporal uniformity assumption in existing approaches: sparse terminal rewards with uniform credit assignment fail to capture the varying criticality of decisions across generation timesteps, resulting in inefficient exploration and suboptimal convergence. To remedy this shortcoming, we introduce TempFlow-GRPO (Temporal Flow GRPO), a principled GRPO framework that captures and exploits the temporal structure inherent in flow-based generation. TempFlow-GRPO introduces two key innovations: (i) a trajectory branching mechanism that provides process rewards by concentrating stochasticity at designated branching points, enabling precise credit assignment without requiring specialized intermediate reward models; and (ii) a noise-aware weighting scheme that modulates policy optimization according to the intrinsic exploration potential of each timestep, prioritizing learning during high-impact early stages while ensuring stable refinement in later phases. These innovations endow the model with temporally-aware optimization that respects the underlying generative dynamics, leading to state-of-the-art performance in human preference alignment and standard text-to-image benchmarks.
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2025-08-20T00:00:00 |
2508.11548
|
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends
|
[
"Zhenhua Xu",
"Xubin Yue",
"Zhebo Wang",
"Qichen Liu",
"Xixiang Zhao",
"Jingxuan Zhang",
"Wenjun Zeng",
"Wengpeng Xing",
"Dezhang Kong",
"Changting Lin",
"Meng Han"
] |
Copyright protection for large language models is of critical importance, given their substantial development costs, proprietary value, and potential for misuse. Existing surveys have predominantly focused on techniques for tracing LLM-generated content-namely, text watermarking-while a systematic exploration of methods for protecting the models themselves (i.e., model watermarking and model fingerprinting) remains absent. Moreover, the relationships and distinctions among text watermarking, model watermarking, and model fingerprinting have not been comprehensively clarified. This work presents a comprehensive survey of the current state of LLM copyright protection technologies, with a focus on model fingerprinting, covering the following aspects: (1) clarifying the conceptual connection from text watermarking to model watermarking and fingerprinting, and adopting a unified terminology that incorporates model watermarking into the broader fingerprinting framework; (2) providing an overview and comparison of diverse text watermarking techniques, highlighting cases where such methods can function as model fingerprinting; (3) systematically categorizing and comparing existing model fingerprinting approaches for LLM copyright protection; (4) presenting, for the first time, techniques for fingerprint transfer and fingerprint removal; (5) summarizing evaluation metrics for model fingerprints, including effectiveness, harmlessness, robustness, stealthiness, and reliability; and (6) discussing open challenges and future research directions. This survey aims to offer researchers a thorough understanding of both text watermarking and model fingerprinting technologies in the era of LLMs, thereby fostering further advances in protecting their intellectual property.
|
|
2025-08-20T00:00:00 |
2508.10830
|
Advances in Speech Separation: Techniques, Challenges, and Future Trends
|
[
"Kai Li",
"Guo Chen",
"Wendi Sang",
"Yi Luo",
"Zhuo Chen",
"Shuai Wang",
"Shulin He",
"Zhong-Qiu Wang",
"Andong Li",
"Zhiyong Wu",
"Xiaolin Hu"
] |
The field of speech separation, addressing the "cocktail party problem", has seen revolutionary advances with DNNs. Speech separation enhances clarity in complex acoustic environments and serves as crucial pre-processing for speech recognition and speaker recognition. However, current literature focuses narrowly on specific architectures or isolated approaches, creating fragmented understanding. This survey addresses this gap by providing systematic examination of DNN-based speech separation techniques. Our work differentiates itself through: (I) Comprehensive perspective: We systematically investigate learning paradigms, separation scenarios with known/unknown speakers, comparative analysis of supervised/self-supervised/unsupervised frameworks, and architectural components from encoders to estimation strategies. (II) Timeliness: Coverage of cutting-edge developments ensures access to current innovations and benchmarks. (III) Unique insights: Beyond summarization, we evaluate technological trajectories, identify emerging patterns, and highlight promising directions including domain-robust frameworks, efficient architectures, multimodal integration, and novel self-supervised paradigms. (IV) Fair evaluation: We provide quantitative evaluations on standard datasets, revealing true capabilities and limitations of different methods. This comprehensive survey serves as an accessible reference for experienced researchers and newcomers navigating speech separation's complex landscape.
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|
2025-08-20T00:00:00 |
2508.04326
|
Radiance Fields in XR: A Survey on How Radiance Fields are Envisioned and Addressed for XR Research
|
[
"Ke Li",
"Mana Masuda",
"Susanne Schmidt",
"Shohei Mori"
] |
The development of radiance fields (RF), such as 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF), has revolutionized interactive photorealistic view synthesis and presents enormous opportunities for XR research and applications. However, despite the exponential growth of RF research, RF-related contributions to the XR community remain sparse. To better understand this research gap, we performed a systematic survey of current RF literature to analyze (i) how RF is envisioned for XR applications, (ii) how they have already been implemented, and (iii) the remaining research gaps. We collected 365 RF contributions related to XR from computer vision, computer graphics, robotics, multimedia, human-computer interaction, and XR communities, seeking to answer the above research questions. Among the 365 papers, we performed an analysis of 66 papers that already addressed a detailed aspect of RF research for XR. With this survey, we extended and positioned XR-specific RF research topics in the broader RF research field and provide a helpful resource for the XR community to navigate within the rapid development of RF research.
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2025-08-20T00:00:00 |
2508.04038
|
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
|
[
"Zechen Li",
"Baiyu Chen",
"Hao Xue",
"Flora D. Salim"
] |
https://github.com/zechenli03/ZARA
|
Motion sensor time-series are central to human activity recognition (HAR), with applications in health, sports, and smart devices. However, existing methods are trained for fixed activity sets and require costly retraining when new behaviours or sensor setups appear. Recent attempts to use large language models (LLMs) for HAR, typically by converting signals into text or images, suffer from limited accuracy and lack verifiable interpretability. We propose ZARA, the first agent-based framework for zero-shot, explainable HAR directly from raw motion time-series. ZARA integrates an automatically derived pair-wise feature knowledge base that captures discriminative statistics for every activity pair, a multi-sensor retrieval module that surfaces relevant evidence, and a hierarchical agent pipeline that guides the LLM to iteratively select features, draw on this evidence, and produce both activity predictions and natural-language explanations. ZARA enables flexible and interpretable HAR without any fine-tuning or task-specific classifiers. Extensive experiments on 8 HAR benchmarks show that ZARA achieves SOTA zero-shot performance, delivering clear reasoning while exceeding the strongest baselines by 2.53x in macro F1. Ablation studies further confirm the necessity of each module, marking ZARA as a promising step toward trustworthy, plug-and-play motion time-series analysis. Our codes are available at https://github.com/zechenli03/ZARA.
|
2025-08-20T00:00:00 |
2508.08777
|
Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge
|
[
"Francesco Fabbri",
"Gustavo Penha",
"Edoardo D'Amico",
"Alice Wang",
"Marco De Nadai",
"Jackie Doremus",
"Paul Gigioli",
"Andreas Damianou",
"Oskar Stal",
"Mounia Lalmas"
] |
Evaluating personalized recommendations remains a central challenge, especially in long-form audio domains like podcasts, where traditional offline metrics suffer from exposure bias and online methods such as A/B testing are costly and operationally constrained. In this paper, we propose a novel framework that leverages Large Language Models (LLMs) as offline judges to assess the quality of podcast recommendations in a scalable and interpretable manner. Our two-stage profile-aware approach first constructs natural-language user profiles distilled from 90 days of listening history. These profiles summarize both topical interests and behavioral patterns, serving as compact, interpretable representations of user preferences. Rather than prompting the LLM with raw data, we use these profiles to provide high-level, semantically rich context-enabling the LLM to reason more effectively about alignment between a user's interests and recommended episodes. This reduces input complexity and improves interpretability. The LLM is then prompted to deliver fine-grained pointwise and pairwise judgments based on the profile-episode match. In a controlled study with 47 participants, our profile-aware judge matched human judgments with high fidelity and outperformed or matched a variant using raw listening histories. The framework enables efficient, profile-aware evaluation for iterative testing and model selection in recommender systems.
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2025-08-20T00:00:00 |
2508.12535
|
CorrSteer: Steering Improves Task Performance and Safety in LLMs through Correlation-based Sparse Autoencoder Feature Selection
|
[
"Seonglae Cho",
"Zekun Wu",
"Adriano Koshiyama"
] |
Sparse Autoencoders (SAEs) can extract interpretable features from large language models (LLMs) without supervision. However, their effectiveness in downstream steering tasks is limited by the requirement for contrastive datasets or large activation storage. To address these limitations, we propose CorrSteer, which selects features by correlating sample correctness with SAE activations from generated tokens at inference time. This approach uses only inference-time activations to extract more relevant features, thereby avoiding spurious correlations. It also obtains steering coefficients from average activations, automating the entire pipeline. Our method shows improved task performance on QA, bias mitigation, jailbreaking prevention, and reasoning benchmarks on Gemma 2 2B and LLaMA 3.1 8B, notably achieving a +4.1% improvement in MMLU performance and a +22.9% improvement in HarmBench with only 4000 samples. Selected features demonstrate semantically meaningful patterns aligned with each task's requirements, revealing the underlying capabilities that drive performance. Our work establishes correlationbased selection as an effective and scalable approach for automated SAE steering across language model applications.
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2025-08-20T00:00:00 |
2508.11032
|
MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation
|
[
"Yanwu Yang",
"Guinan Su",
"Jiesi Hu",
"Francesco Sammarco",
"Jonas Geiping",
"Thomas Wolfers"
] |
Universal medical image segmentation models have emerged as a promising paradigm due to their strong generalizability across diverse tasks, showing great potential for a wide range of clinical applications. This potential has been partly driven by the success of general-purpose vision models such as the Segment Anything Model (SAM), which has inspired the development of various fine-tuned variants for medical segmentation tasks. However, fine-tuned variants like MedSAM are trained on comparatively limited medical imaging data that often suffers from heterogeneity, scarce annotations, and distributional shifts. These challenges limit their ability to generalize across a wide range of medical segmentation tasks. In this regard, we propose MedSAMix, a training-free model merging method that integrates the strengths of both generalist models (e.g., SAM) and specialist models (e.g., MedSAM) for medical image segmentation. In contrast to traditional model merging approaches that rely on manual configuration and often result in suboptimal outcomes, we propose a zero-order optimization method to automatically discover optimal layer-wise merging solutions. Furthermore, for clinical applications, we develop two regimes to meet the demand of domain-specificity and generalizability in different scenarios by single-task optimization and multi-objective optimization respectively. Extensive evaluations on 25 medical segmentation tasks demonstrate that MedSAMix effectively mitigates model bias and consistently improves performance in both domain-specific accuracy and generalization, achieving improvements of 6.67% on specialized tasks and 4.37% on multi-task evaluations.
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2025-08-20T00:00:00 |
2508.10478
|
Semantic IDs for Joint Generative Search and Recommendation
|
[
"Gustavo Penha",
"Edoardo D'Amico",
"Marco De Nadai",
"Enrico Palumbo",
"Alexandre Tamborrino",
"Ali Vardasbi",
"Max Lefarov",
"Shawn Lin",
"Timothy Heath",
"Francesco Fabbri",
"Hugues Bouchard"
] |
Generative models powered by Large Language Models (LLMs) are emerging as a unified solution for powering both recommendation and search tasks. A key design choice in these models is how to represent items, traditionally through unique identifiers (IDs) and more recently with Semantic IDs composed of discrete codes, obtained from embeddings. While task-specific embedding models can improve performance for individual tasks, they may not generalize well in a joint setting. In this paper, we explore how to construct Semantic IDs that perform well both in search and recommendation when using a unified model. We compare a range of strategies to construct Semantic IDs, looking into task-specific and cross-tasks approaches, and also whether each task should have its own semantic ID tokens in a joint search and recommendation generative model. Our results show that using a bi-encoder model fine-tuned on both search and recommendation tasks to obtain item embeddings, followed by the construction of a unified Semantic ID space provides an effective trade-off, enabling strong performance in both tasks. We hope these findings spark follow-up work on generalisable, semantically grounded ID schemes and inform the next wave of unified generative recommender architectures.
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2025-08-20T00:00:00 |
2508.09789
|
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
|
[
"Marco De Nadai",
"Andreas Damianou",
"Mounia Lalmas"
] |
Existing video recommender systems rely primarily on user-defined metadata or on low-level visual and acoustic signals extracted by specialised encoders. These low-level features describe what appears on the screen but miss deeper semantics such as intent, humour, and world knowledge that make clips resonate with viewers. For example, is a 30-second clip simply a singer on a rooftop, or an ironic parody filmed amid the fairy chimneys of Cappadocia, Turkey? Such distinctions are critical to personalised recommendations yet remain invisible to traditional encoding pipelines. In this paper, we introduce a simple, recommendation system-agnostic zero-finetuning framework that injects high-level semantics into the recommendation pipeline by prompting an off-the-shelf Multimodal Large Language Model (MLLM) to summarise each clip into a rich natural-language description (e.g. "a superhero parody with slapstick fights and orchestral stabs"), bridging the gap between raw content and user intent. We use MLLM output with a state-of-the-art text encoder and feed it into standard collaborative, content-based, and generative recommenders. On the MicroLens-100K dataset, which emulates user interactions with TikTok-style videos, our framework consistently surpasses conventional video, audio, and metadata features in five representative models. Our findings highlight the promise of leveraging MLLMs as on-the-fly knowledge extractors to build more intent-aware video recommenders.
|
|
2025-08-20T00:00:00 |
2508.13804
|
Beyond Human Judgment: A Bayesian Evaluation of LLMs' Moral Values Understanding
|
[
"Maciej Skorski",
"Alina Landowska"
] |
How do large language models understand moral dimensions compared to humans? This first large-scale Bayesian evaluation of market-leading language models provides the answer. In contrast to prior work using deterministic ground truth (majority or inclusion rules), we model annotator disagreements to capture both aleatoric uncertainty (inherent human disagreement) and epistemic uncertainty (model domain sensitivity). We evaluate top language models (Claude Sonnet 4, DeepSeek-V3, Llama 4 Maverick) across 250K+ annotations from ~700 annotators on 100K+ texts spanning social media, news, and forums. Our GPU-optimized Bayesian framework processed 1M+ model queries, revealing that AI models typically rank among the top 25\% of human annotators, achieving much better-than-average balanced accuracy. Importantly, we find that AI produces far fewer false negatives than humans, highlighting their more sensitive moral detection capabilities.
|
|
2025-08-20T00:00:00 |
2508.13998
|
Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation
|
[
"Yifu Yuan",
"Haiqin Cui",
"Yaoting Huang",
"Yibin Chen",
"Fei Ni",
"Zibin Dong",
"Pengyi Li",
"Yan Zheng",
"Jianye Hao"
] |
Generalization in embodied AI is hindered by the "seeing-to-doing gap," which stems from data scarcity and embodiment heterogeneity. To address this, we pioneer "pointing" as a unified, embodiment-agnostic intermediate representation, defining four core embodied pointing abilities that bridge high-level vision-language comprehension with low-level action primitives. We introduce Embodied-R1, a 3B Vision-Language Model (VLM) specifically designed for embodied reasoning and pointing. We use a wide range of embodied and general visual reasoning datasets as sources to construct a large-scale dataset, Embodied-Points-200K, which supports key embodied pointing capabilities. We then train Embodied-R1 using a two-stage Reinforced Fine-tuning (RFT) curriculum with a specialized multi-task reward design. Embodied-R1 achieves state-of-the-art performance on 11 embodied spatial and pointing benchmarks. Critically, it demonstrates robust zero-shot generalization by achieving a 56.2% success rate in the SIMPLEREnv and 87.5% across 8 real-world XArm tasks without any task-specific fine-tuning, representing a 62% improvement over strong baselines. Furthermore, the model exhibits high robustness against diverse visual disturbances. Our work shows that a pointing-centric representation, combined with an RFT training paradigm, offers an effective and generalizable pathway to closing the perception-action gap in robotics.
|
|
2025-08-20T00:00:00 |
2508.12903
|
A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
|
[
"Jinyi Han",
"Xinyi Wang",
"Haiquan Zhao",
"Tingyun li",
"Zishang Jiang",
"Sihang Jiang",
"Jiaqing Liang",
"Xin Lin",
"Weikang Zhou",
"Zeye Sun",
"Fei Yu",
"Yanghua Xiao"
] |
Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most existing self-refinement methods rely on a reactive process with a fixed number of iterations, making it difficult to determine the optimal timing and content of refinement based on the evolving generation context. Inspired by the way humans dynamically refine their thoughts during execution, we propose ProActive Self-Refinement (PASR), a novel method that enables LLMs to refine their outputs during the generation process. Unlike methods that regenerate entire responses, PASR proactively decides whether, when, and how to refine based on the model's internal state and evolving context. We conduct extensive experiments on a diverse set of 10 tasks to evaluate the effectiveness of PASR. Experimental results show that PASR significantly enhances problem-solving performance. In particular, on Qwen3-8B, PASR reduces average token consumption by 41.6 percent compared to standard generation, while also achieving an 8.2 percent improvement in accuracy. Our code and all baselines used in the paper are available in the GitHub.
|
|
2025-08-20T00:00:00 |
2508.12040
|
Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation
|
[
"Jinyi Han",
"Tingyun Li",
"Shisong Chen",
"Jie Shi",
"Xinyi Wang",
"Guanglei Yue",
"Jiaqing Liang",
"Xin Lin",
"Liqian Wen",
"Zulong Chen",
"Yanghua Xiao"
] |
While large language models (LLMs) have demonstrated remarkable performance across diverse tasks, they fundamentally lack self-awareness and frequently exhibit overconfidence, assigning high confidence scores to incorrect predictions. Accurate confidence estimation is therefore critical for enhancing the trustworthiness and reliability of LLM-generated outputs. However, existing approaches suffer from coarse-grained scoring mechanisms that fail to provide fine-grained, continuous confidence estimates throughout the generation process. To address these limitations, we introduce FineCE, a novel confidence estimation method that delivers accurate, fine-grained confidence scores during text generation. Specifically, we first develop a comprehensive pipeline for constructing training data that effectively captures the underlying probabilistic distribution of LLM responses, and then train a model to predict confidence scores for arbitrary text sequences in a supervised manner. Furthermore, we propose a Backward Confidence Integration (BCI) strategy that leverages information from the subsequent text to enhance confidence estimation for the current sequence during inference. We also introduce three strategies for identifying optimal positions to perform confidence estimation within the generation process. Extensive experiments on multiple benchmark datasets demonstrate that FineCE consistently outperforms existing classical confidence estimation methods. Our code and all baselines used in the paper are available on GitHub.
|
|
2025-08-20T00:00:00 |
2508.13186
|
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
|
[
"Shilong Li",
"Xingyuan Bu",
"Wenjie Wang",
"Jiaheng Liu",
"Jun Dong",
"Haoyang He",
"Hao Lu",
"Haozhe Zhang",
"Chenchen Jing",
"Zhen Li",
"Chuanhao Li",
"Jiayi Tian",
"Chenchen Zhang",
"Tianhao Peng",
"Yancheng He",
"Jihao Gu",
"Yuanxing Zhang",
"Jian Yang",
"Ge Zhang",
"Wenhao Huang",
"Wangchunshu Zhou",
"Zhaoxiang Zhang",
"Ruizhe Ding",
"Shilei Wen"
] |
AI agents with advanced reasoning and tool use capabilities have demonstrated impressive performance in web browsing for deep search. While existing benchmarks such as BrowseComp evaluate these browsing abilities, they primarily focus on textual information, overlooking the prevalence of multimodal content. To bridge this gap, we introduce MM-BrowseComp, a novel benchmark comprising 224 challenging, hand-crafted questions specifically designed to assess agents' multimodal retrieval and reasoning capabilities. These questions often incorporate images in prompts, and crucial information encountered during the search and reasoning process may also be embedded within images or videos on webpages. Consequently, methods relying solely on text prove insufficient for our benchmark. Additionally, we provide a verified checklist for each question, enabling fine-grained analysis of multimodal dependencies and reasoning paths. Our comprehensive evaluation of state-of-the-art models on MM-BrowseComp reveals that even top models like OpenAI o3 with tools achieve only 29.02\% accuracy, highlighting the suboptimal multimodal capabilities and lack of native multimodal reasoning in current models.
|
|
2025-08-20T00:00:00 |
2508.12845
|
CAMAR: Continuous Actions Multi-Agent Routing
|
[
"Artem Pshenitsyn",
"Aleksandr Panov",
"Alexey Skrynnik"
] |
Multi-agent reinforcement learning (MARL) is a powerful paradigm for solving cooperative and competitive decision-making problems. While many MARL benchmarks have been proposed, few combine continuous state and action spaces with challenging coordination and planning tasks. We introduce CAMAR, a new MARL benchmark designed explicitly for multi-agent pathfinding in environments with continuous actions. CAMAR supports cooperative and competitive interactions between agents and runs efficiently at up to 100,000 environment steps per second. We also propose a three-tier evaluation protocol to better track algorithmic progress and enable deeper analysis of performance. In addition, CAMAR allows the integration of classical planning methods such as RRT and RRT* into MARL pipelines. We use them as standalone baselines and combine RRT* with popular MARL algorithms to create hybrid approaches. We provide a suite of test scenarios and benchmarking tools to ensure reproducibility and fair comparison. Experiments show that CAMAR presents a challenging and realistic testbed for the MARL community.
|
|
2025-08-20T00:00:00 |
2508.12800
|
Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
|
[
"Yong Deng",
"Guoqing Wang",
"Zhenzhe Ying",
"Xiaofeng Wu",
"Jinzhen Lin",
"Wenwen Xiong",
"Yuqin Dai",
"Shuo Yang",
"Zhanwei Zhang",
"Qiwen Wang",
"Yang Qin",
"Changhua Meng"
] |
Large language models (LLMs) exhibit remarkable problem-solving abilities, but struggle with complex tasks due to static internal knowledge. Retrieval-Augmented Generation (RAG) enhances access to external information, yet remains limited in multi-hop reasoning and strategic search due to rigid workflows. Recent advancements in agentic deep research empower LLMs to autonomously reason, search, and synthesize information. However, current approaches relying on outcome-based reinforcement learning (RL) face critical issues such as conflicting gradients and reward sparsity, limiting performance gains and training efficiency. To address these, we first propose Atomic Thought, a novel LLM thinking paradigm that decomposes reasoning into fine-grained functional units. These units are supervised by Reasoning Reward Models (RRMs), which provide Atomic Thought Rewards (ATR) for fine-grained guidance. Building on this, we propose Atom-Searcher, a novel RL framework for agentic deep research that integrates Atomic Thought and ATR. Atom-Searcher uses a curriculum-inspired reward schedule, prioritizing process-level ATR early and transitioning to outcome rewards, accelerating convergence on effective reasoning paths. Experiments on seven benchmarks show consistent improvements over the state-of-the-art. Key advantages include: (1) Atom-Searcher scales computation at test-time. (2) Atomic Thought provides supervision anchors for RRMs, bridging deep research tasks and RRMs. (3) Atom-Searcher exhibits more interpretable, human-like reasoning patterns.
|
|
2025-08-20T00:00:00 |
2508.13320
|
Rapidly Adapting to New Voice Spoofing: Few-Shot Detection of Synthesized Speech Under Distribution Shifts
|
[
"Ashi Garg",
"Zexin Cai",
"Henry Li Xinyuan",
"Leibny Paola García-Perera",
"Kevin Duh",
"Sanjeev Khudanpur",
"Matthew Wiesner",
"Nicholas Andrews"
] |
We address the challenge of detecting synthesized speech under distribution shifts -- arising from unseen synthesis methods, speakers, languages, or audio conditions -- relative to the training data. Few-shot learning methods are a promising way to tackle distribution shifts by rapidly adapting on the basis of a few in-distribution samples. We propose a self-attentive prototypical network to enable more robust few-shot adaptation. To evaluate our approach, we systematically compare the performance of traditional zero-shot detectors and the proposed few-shot detectors, carefully controlling training conditions to introduce distribution shifts at evaluation time. In conditions where distribution shifts hamper the zero-shot performance, our proposed few-shot adaptation technique can quickly adapt using as few as 10 in-distribution samples -- achieving upto 32% relative EER reduction on deepfakes in Japanese language and 20% relative reduction on ASVspoof 2021 Deepfake dataset.
|
|
2025-08-20T00:00:00 |
2508.11386
|
Retrieval-augmented reasoning with lean language models
|
[
"Ryan Sze-Yin Chan",
"Federico Nanni",
"Tomas Lazauskas",
"Rosie Wood",
"Penelope Yong",
"Lionel Tarassenko",
"Mark Girolami",
"James Geddes",
"Andrew Duncan"
] |
This technical report details a novel approach to combining reasoning and retrieval augmented generation (RAG) within a single, lean language model architecture. While existing RAG systems typically rely on large-scale models and external APIs, our work addresses the increasing demand for performant and privacy-preserving solutions deployable in resource-constrained or secure environments. Building on recent developments in test-time scaling and small-scale reasoning models, we develop a retrieval augmented conversational agent capable of interpreting complex, domain-specific queries using a lightweight backbone model. Our system integrates a dense retriever with fine-tuned Qwen2.5-Instruct models, using synthetic query generation and reasoning traces derived from frontier models (e.g., DeepSeek-R1) over a curated corpus, in this case, the NHS A-to-Z condition pages. We explore the impact of summarisation-based document compression, synthetic data design, and reasoning-aware fine-tuning on model performance. Evaluation against both non-reasoning and general-purpose lean models demonstrates that our domain-specific fine-tuning approach yields substantial gains in answer accuracy and consistency, approaching frontier-level performance while remaining feasible for local deployment. All implementation details and code are publicly released to support reproducibility and adaptation across domains.
|
|
2025-08-21T00:00:00 |
2508.11987
|
FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction
|
[
"Zhiyuan Zeng",
"Jiashuo Liu",
"Siyuan Chen",
"Tianci He",
"Yali Liao",
"Jinpeng Wang",
"Zaiyuan Wang",
"Yang Yang",
"Lingyue Yin",
"Mingren Yin",
"Zhenwei Zhu",
"Tianle Cai",
"Zehui Chen",
"Jiecao Chen",
"Yantao Du",
"Xiang Gao",
"Jiacheng Guo",
"Liang Hu",
"Jianpeng Jiao",
"Xiangsheng Li",
"Jingkai Liu",
"Shuang Ni",
"Zhoufutu Wen",
"Ge Zhang",
"Kaiyuan Zhang",
"Xin Zhou",
"Jose Blanchet",
"Xipeng Qiu",
"Mengdi Wang",
"Wenhao Huang"
] |
Future prediction is a complex task for LLM agents, requiring a high level of analytical thinking, information gathering, contextual understanding, and decision-making under uncertainty. Agents must not only gather and interpret vast amounts of dynamic information but also integrate diverse data sources, weigh uncertainties, and adapt predictions based on emerging trends, just as human experts do in fields like politics, economics, and finance. Despite its importance, no large-scale benchmark exists for evaluating agents on future prediction, largely due to challenges in handling real-time updates and retrieving timely, accurate answers. To address this, we introduce FutureX, a dynamic and live evaluation benchmark specifically designed for LLM agents performing future prediction tasks. FutureX is the largest and most diverse live benchmark for future prediction, supporting real-time daily updates and eliminating data contamination through an automated pipeline for question gathering and answer collection. We evaluate 25 LLM/agent models, including those with reasoning, search capabilities, and integration of external tools such as the open-source Deep Research Agent and closed-source Deep Research models. This comprehensive evaluation assesses agents' adaptive reasoning and performance in dynamic environments. Additionally, we provide in-depth analyses of agents' failure modes and performance pitfalls in future-oriented tasks, including the vulnerability to fake web pages and the temporal validity. Our goal is to establish a dynamic, contamination-free evaluation standard that drives the development of LLM agents capable of performing at the level of professional human analysts in complex reasoning and predictive thinking.
|
|
2025-08-21T00:00:00 |
2508.14811
|
Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization
|
[
"Canyu Zhao",
"Xiaoman Li",
"Tianjian Feng",
"Zhiyue Zhao",
"Hao Chen",
"Chunhua Shen"
] |
We introduce Tinker, a versatile framework for high-fidelity 3D editing that operates in both one-shot and few-shot regimes without any per-scene finetuning. Unlike prior techniques that demand extensive per-scene optimization to ensure multi-view consistency or to produce dozens of consistent edited input views, Tinker delivers robust, multi-view consistent edits from as few as one or two images. This capability stems from repurposing pretrained diffusion models, which unlocks their latent 3D awareness. To drive research in this space, we curate the first large-scale multi-view editing dataset and data pipeline, spanning diverse scenes and styles. Building on this dataset, we develop our framework capable of generating multi-view consistent edited views without per-scene training, which consists of two novel components: (1) Referring multi-view editor: Enables precise, reference-driven edits that remain coherent across all viewpoints. (2) Any-view-to-video synthesizer: Leverages spatial-temporal priors from video diffusion to perform high-quality scene completion and novel-view generation even from sparse inputs. Through extensive experiments, Tinker significantly reduces the barrier to generalizable 3D content creation, achieving state-of-the-art performance on editing, novel-view synthesis, and rendering enhancement tasks. We believe that Tinker represents a key step towards truly scalable, zero-shot 3D editing. Project webpage: https://aim-uofa.github.io/Tinker
|
|
2025-08-21T00:00:00 |
2508.14879
|
MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds
|
[
"Bingquan Dai",
"Li Ray Luo",
"Qihong Tang",
"Jie Wang",
"Xinyu Lian",
"Hao Xu",
"Minghan Qin",
"Xudong Xu",
"Bo Dai",
"Haoqian Wang",
"Zhaoyang Lyu",
"Jiangmiao Pang"
] |
Reconstructing 3D objects into editable programs is pivotal for applications like reverse engineering and shape editing. However, existing methods often rely on limited domain-specific languages (DSLs) and small-scale datasets, restricting their ability to model complex geometries and structures. To address these challenges, we introduce MeshCoder, a novel framework that reconstructs complex 3D objects from point clouds into editable Blender Python scripts. We develop a comprehensive set of expressive Blender Python APIs capable of synthesizing intricate geometries. Leveraging these APIs, we construct a large-scale paired object-code dataset, where the code for each object is decomposed into distinct semantic parts. Subsequently, we train a multimodal large language model (LLM) that translates 3D point cloud into executable Blender Python scripts. Our approach not only achieves superior performance in shape-to-code reconstruction tasks but also facilitates intuitive geometric and topological editing through convenient code modifications. Furthermore, our code-based representation enhances the reasoning capabilities of LLMs in 3D shape understanding tasks. Together, these contributions establish MeshCoder as a powerful and flexible solution for programmatic 3D shape reconstruction and understanding.
|
|
2025-08-21T00:00:00 |
2508.13491
|
From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models
|
[
"Ziyan Kuang",
"Feiyu Zhu",
"Maowei Jiang",
"Yanzhao Lai",
"Zelin Wang",
"Zhitong Wang",
"Meikang Qiu",
"Jiajia Huang",
"Min Peng",
"Qianqian Xie",
"Sophia Ananiadou"
] |
Large Language Models (LLMs) have shown promise for financial applications, yet their suitability for this high-stakes domain remains largely unproven due to inadequacies in existing benchmarks. Existing benchmarks solely rely on score-level evaluation, summarizing performance with a single score that obscures the nuanced understanding of what models truly know and their precise limitations. They also rely on datasets that cover only a narrow subset of financial concepts, while overlooking other essentials for real-world applications. To address these gaps, we introduce FinCDM, the first cognitive diagnosis evaluation framework tailored for financial LLMs, enabling the evaluation of LLMs at the knowledge-skill level, identifying what financial skills and knowledge they have or lack based on their response patterns across skill-tagged tasks, rather than a single aggregated number. We construct CPA-QKA, the first cognitively informed financial evaluation dataset derived from the Certified Public Accountant (CPA) examination, with comprehensive coverage of real-world accounting and financial skills. It is rigorously annotated by domain experts, who author, validate, and annotate questions with high inter-annotator agreement and fine-grained knowledge labels. Our extensive experiments on 30 proprietary, open-source, and domain-specific LLMs show that FinCDM reveals hidden knowledge gaps, identifies under-tested areas such as tax and regulatory reasoning overlooked by traditional benchmarks, and uncovers behavioral clusters among models. FinCDM introduces a new paradigm for financial LLM evaluation by enabling interpretable, skill-aware diagnosis that supports more trustworthy and targeted model development, and all datasets and evaluation scripts will be publicly released to support further research.
|
|
2025-08-21T00:00:00 |
2508.14160
|
RynnEC: Bringing MLLMs into Embodied World
|
[
"Ronghao Dang",
"Yuqian Yuan",
"Yunxuan Mao",
"Kehan Li",
"Jiangpin Liu",
"Zhikai Wang",
"Xin Li",
"Fan Wang",
"Deli Zhao"
] |
https://github.com/alibaba-damo-academy/RynnEC
|
We introduce RynnEC, a video multimodal large language model designed for embodied cognition. Built upon a general-purpose vision-language foundation model, RynnEC incorporates a region encoder and a mask decoder, enabling flexible region-level video interaction. Despite its compact architecture, RynnEC achieves state-of-the-art performance in object property understanding, object segmentation, and spatial reasoning. Conceptually, it offers a region-centric video paradigm for the brain of embodied agents, providing fine-grained perception of the physical world and enabling more precise interactions. To mitigate the scarcity of annotated 3D datasets, we propose an egocentric video based pipeline for generating embodied cognition data. Furthermore, we introduce RynnEC-Bench, a region-centered benchmark for evaluating embodied cognitive capabilities. We anticipate that RynnEC will advance the development of general-purpose cognitive cores for embodied agents and facilitate generalization across diverse embodied tasks. The code, model checkpoints, and benchmark are available at: https://github.com/alibaba-damo-academy/RynnEC
|
2025-08-21T00:00:00 |
2508.14896
|
Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
|
[
"Haokun Lin",
"Haobo Xu",
"Yichen Wu",
"Ziyu Guo",
"Renrui Zhang",
"Zhichao Lu",
"Ying Wei",
"Qingfu Zhang",
"Zhenan Sun"
] |
Recent advances in diffusion large language models (dLLMs) have introduced a promising alternative to autoregressive (AR) LLMs for natural language generation tasks, leveraging full attention and denoising-based decoding strategies. However, the deployment of these models on edge devices remains challenging due to their massive parameter scale and high resource demands. While post-training quantization (PTQ) has emerged as a widely adopted technique for compressing AR LLMs, its applicability to dLLMs remains largely unexplored. In this work, we present the first systematic study on quantizing diffusion-based language models. We begin by identifying the presence of activation outliers, characterized by abnormally large activation values that dominate the dynamic range. These outliers pose a key challenge to low-bit quantization, as they make it difficult to preserve precision for the majority of values. More importantly, we implement state-of-the-art PTQ methods and conduct a comprehensive evaluation across multiple task types and model variants. Our analysis is structured along four key dimensions: bit-width, quantization method, task category, and model type. Through this multi-perspective evaluation, we offer practical insights into the quantization behavior of dLLMs under different configurations. We hope our findings provide a foundation for future research in efficient dLLM deployment. All codes and experimental setups will be released to support the community.
|
|
2025-08-21T00:00:00 |
2508.14444
|
NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
|
[
"NVIDIA",
"Aarti Basant",
"Abhijit Khairnar",
"Abhijit Paithankar",
"Abhinav Khattar",
"Adi Renduchintala",
"Adithya Renduchintala",
"Aditya Malte",
"Akhiad Bercovich",
"Akshay Hazare",
"Alejandra Rico",
"Aleksander Ficek",
"Alex Kondratenko",
"Alex Shaposhnikov",
"Ali Taghibakhshi",
"Amelia Barton",
"Ameya Sunil Mahabaleshwarkar",
"Amy Shen",
"Andrew Tao",
"Ann Guan",
"Anna Shors",
"Anubhav Mandarwal",
"Arham Mehta",
"Arun Venkatesan",
"Ashton Sharabiani",
"Ashwath Aithal",
"Ashwin Poojary",
"Ayush Dattagupta",
"Balaram Buddharaju",
"Banghua Zhu",
"Barnaby Simkin",
"Bilal Kartal",
"Bita Darvish Rouhani",
"Bobby Chen",
"Boris Ginsburg",
"Brandon Norick",
"Brian Yu",
"Bryan Catanzaro",
"Charles Wang",
"Charlie Truong",
"Chetan Mungekar",
"Chintan Patel",
"Chris Alexiuk",
"Christian Munley",
"Christopher Parisien",
"Dan Su",
"Daniel Afrimi",
"Daniel Korzekwa",
"Daniel Rohrer",
"Daria Gitman",
"David Mosallanezhad",
"Deepak Narayanan",
"Dima Rekesh",
"Dina Yared",
"Dmytro Pykhtar",
"Dong Ahn",
"Duncan Riach",
"Eileen Long",
"Elliott Ning",
"Eric Chung",
"Erick Galinkin",
"Evelina Bakhturina",
"Gargi Prasad",
"Gerald Shen",
"Haim Elisha",
"Harsh Sharma",
"Hayley Ross",
"Helen Ngo",
"Herman Sahota",
"Hexin Wang",
"Hoo Chang Shin",
"Hua Huang",
"Iain Cunningham",
"Igor Gitman",
"Ivan Moshkov",
"Jaehun Jung",
"Jan Kautz",
"Jane Polak Scowcroft",
"Jared Casper",
"Jimmy Zhang",
"Jinze Xue",
"Jocelyn Huang",
"Joey Conway",
"John Kamalu",
"Jonathan Cohen",
"Joseph Jennings",
"Julien Veron Vialard",
"Junkeun Yi",
"Jupinder Parmar",
"Kari Briski",
"Katherine Cheung",
"Katherine Luna",
"Keith Wyss",
"Keshav Santhanam",
"Kezhi Kong",
"Krzysztof Pawelec",
"Kumar Anik",
"Kunlun Li",
"Kushan Ahmadian",
"Lawrence McAfee",
"Laya Sleiman",
"Leon Derczynski",
"Luis Vega",
"Maer Rodrigues de Melo",
"Makesh Narsimhan Sreedhar",
"Marcin Chochowski",
"Mark Cai",
"Markus Kliegl",
"Marta Stepniewska-Dziubinska",
"Matvei Novikov",
"Mehrzad Samadi",
"Meredith Price",
"Meriem Boubdir",
"Michael Boone",
"Michael Evans",
"Michal Bien",
"Michal Zawalski",
"Miguel Martinez",
"Mike Chrzanowski",
"Mohammad Shoeybi",
"Mostofa Patwary",
"Namit Dhameja",
"Nave Assaf",
"Negar Habibi",
"Nidhi Bhatia",
"Nikki Pope",
"Nima Tajbakhsh",
"Nirmal Kumar Juluru",
"Oleg Rybakov",
"Oleksii Hrinchuk",
"Oleksii Kuchaiev",
"Oluwatobi Olabiyi",
"Pablo Ribalta",
"Padmavathy Subramanian",
"Parth Chadha",
"Pavlo Molchanov",
"Peter Dykas",
"Peter Jin",
"Piotr Bialecki",
"Piotr Januszewski",
"Pradeep Thalasta",
"Prashant Gaikwad",
"Prasoon Varshney",
"Pritam Gundecha",
"Przemek Tredak",
"Rabeeh Karimi Mahabadi",
"Rajen Patel",
"Ran El-Yaniv",
"Ranjit Rajan",
"Ria Cheruvu",
"Rima Shahbazyan",
"Ritika Borkar",
"Ritu Gala",
"Roger Waleffe",
"Ruoxi Zhang",
"Russell J. Hewett",
"Ryan Prenger",
"Sahil Jain",
"Samuel Kriman",
"Sanjeev Satheesh",
"Saori Kaji",
"Sarah Yurick",
"Saurav Muralidharan",
"Sean Narenthiran",
"Seonmyeong Bak",
"Sepehr Sameni",
"Seungju Han",
"Shanmugam Ramasamy",
"Shaona Ghosh",
"Sharath Turuvekere Sreenivas",
"Shelby Thomas",
"Shizhe Diao",
"Shreya Gopal",
"Shrimai Prabhumoye",
"Shubham Toshniwal",
"Shuoyang Ding",
"Siddharth Singh",
"Siddhartha Jain",
"Somshubra Majumdar",
"Stefania Alborghetti",
"Syeda Nahida Akter",
"Terry Kong",
"Tim Moon",
"Tomasz Hliwiak",
"Tomer Asida",
"Tony Wang",
"Twinkle Vashishth",
"Tyler Poon",
"Udi Karpas",
"Vahid Noroozi",
"Venkat Srinivasan",
"Vijay Korthikanti",
"Vikram Fugro",
"Vineeth Kalluru",
"Vitaly Kurin",
"Vitaly Lavrukhin",
"Wasi Uddin Ahmad",
"Wei Du",
"Wonmin Byeon",
"Ximing Lu",
"Xin Dong",
"Yashaswi Karnati",
"Yejin Choi",
"Yian Zhang",
"Ying Lin",
"Yonggan Fu",
"Yoshi Suhara",
"Zhen Dong",
"Zhiyu Li",
"Zhongbo Zhu",
"Zijia Chen"
] |
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compared to similarly-sized models. Nemotron-Nano-9B-v2 builds on the Nemotron-H architecture, in which the majority of the self-attention layers in the common Transformer architecture are replaced with Mamba-2 layers, to achieve improved inference speed when generating the long thinking traces needed for reasoning. We create Nemotron-Nano-9B-v2 by first pre-training a 12-billion-parameter model (Nemotron-Nano-12B-v2-Base) on 20 trillion tokens using an FP8 training recipe. After aligning Nemotron-Nano-12B-v2-Base, we employ the Minitron strategy to compress and distill the model with the goal of enabling inference on up to 128k tokens on a single NVIDIA A10G GPU (22GiB of memory, bfloat16 precision). Compared to existing similarly-sized models (e.g., Qwen3-8B), we show that Nemotron-Nano-9B-v2 achieves on-par or better accuracy on reasoning benchmarks while achieving up to 6x higher inference throughput in reasoning settings like 8k input and 16k output tokens. We are releasing Nemotron-Nano-9B-v2, Nemotron-Nano12B-v2-Base, and Nemotron-Nano-9B-v2-Base checkpoints along with the majority of our pre- and post-training datasets on Hugging Face.
|
|
2025-08-21T00:00:00 |
2508.14460
|
DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimization
|
[
"Shuaijie She",
"Yu Bao",
"Yu Lu",
"Lu Xu",
"Tao Li",
"Wenhao Zhu",
"Shujian Huang",
"Shanbo Cheng",
"Lu Lu",
"Yuxuan Wang"
] |
We present DuPO, a dual learning-based preference optimization framework that generates annotation-free feedback via a generalized duality. DuPO addresses two key limitations: Reinforcement Learning with Verifiable Rewards (RLVR)'s reliance on costly labels and applicability restricted to verifiable tasks, and traditional dual learning's restriction to strictly dual task pairs (e.g., translation and back-translation). Specifically, DuPO decomposes a primal task's input into known and unknown components, then constructs its dual task to reconstruct the unknown part using the primal output and known information (e.g., reversing math solutions to recover hidden variables), broadening applicability to non-invertible tasks. The quality of this reconstruction serves as a self-supervised reward to optimize the primal task, synergizing with LLMs' ability to instantiate both tasks via a single model. Empirically, DuPO achieves substantial gains across diverse tasks: it enhances the average translation quality by 2.13 COMET over 756 directions, boosts the mathematical reasoning accuracy by an average of 6.4 points on three challenge benchmarks, and enhances performance by 9.3 points as an inference-time reranker (trading computation for accuracy). These results position DuPO as a scalable, general, and annotation-free paradigm for LLM optimization.
|
|
2025-08-21T00:00:00 |
2508.14187
|
Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer
|
[
"Md Ashiqur Rahman",
"Chiao-An Yang",
"Michael N. Cheng",
"Lim Jun Hao",
"Jeremiah Jiang",
"Teck-Yian Lim",
"Raymond A. Yeh"
] |
https://github.com/ashiq24/local-scale-equivariance
|
Scale variation is a fundamental challenge in computer vision. Objects of the same class can have different sizes, and their perceived size is further affected by the distance from the camera. These variations are local to the objects, i.e., different object sizes may change differently within the same image. To effectively handle scale variations, we present a deep equilibrium canonicalizer (DEC) to improve the local scale equivariance of a model. DEC can be easily incorporated into existing network architectures and can be adapted to a pre-trained model. Notably, we show that on the competitive ImageNet benchmark, DEC improves both model performance and local scale consistency across four popular pre-trained deep-nets, e.g., ViT, DeiT, Swin, and BEiT. Our code is available at https://github.com/ashiq24/local-scale-equivariance.
|
2025-08-21T00:00:00 |
2508.10137
|
mSCoRe: a Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning
|
[
"Nghia Trung Ngo",
"Franck Dernoncourt",
"Thien Huu Nguyen"
] |
Recent advancements in reasoning-reinforced Large Language Models (LLMs) have shown remarkable capabilities in complex reasoning tasks. However, the mechanism underlying their utilization of different human reasoning skills remains poorly investigated, especially for multilingual commonsense reasoning that involves everyday knowledge across different languages and cultures. To address this gap, we propose a Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning (mSCoRe). Our benchmark incorporates three key components that are designed to systematically evaluate LLM's reasoning capabilities, including: (1) a novel taxonomy of reasoning skills that enables fine-grained analysis of models' reasoning processes, (2) a robust data synthesis pipeline tailored specifically for commonsense reasoning evaluation, and (3) a complexity scaling framework allowing task difficulty to scale dynamically alongside future improvements in LLM abilities. Extensive experiments on eights state-of-the-art LLMs of varying sizes and training approaches demonstrate that mSCoRe remains significantly challenging for current models, particularly at higher complexity levels. Our results reveal the limitations of such reasoning-reinforced models when confronted with nuanced multilingual general and cultural commonsense. We further provide detailed analysis on the models' reasoning processes, suggesting future directions for improving multilingual commonsense reasoning capabilities.
|
|
2025-08-21T00:00:00 |
2508.14111
|
From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery
|
[
"Jiaqi Wei",
"Yuejin Yang",
"Xiang Zhang",
"Yuhan Chen",
"Xiang Zhuang",
"Zhangyang Gao",
"Dongzhan Zhou",
"Guangshuai Wang",
"Zhiqiang Gao",
"Juntai Cao",
"Zijie Qiu",
"Xuming He",
"Qiang Zhang",
"Chenyu You",
"Shuangjia Zheng",
"Ning Ding",
"Wanli Ouyang",
"Nanqing Dong",
"Yu Cheng",
"Siqi Sun",
"Lei Bai",
"Bowen Zhou"
] |
Artificial intelligence (AI) is reshaping scientific discovery, evolving from specialized computational tools into autonomous research partners. We position Agentic Science as a pivotal stage within the broader AI for Science paradigm, where AI systems progress from partial assistance to full scientific agency. Enabled by large language models (LLMs), multimodal systems, and integrated research platforms, agentic AI shows capabilities in hypothesis generation, experimental design, execution, analysis, and iterative refinement -- behaviors once regarded as uniquely human. This survey provides a domain-oriented review of autonomous scientific discovery across life sciences, chemistry, materials science, and physics. We unify three previously fragmented perspectives -- process-oriented, autonomy-oriented, and mechanism-oriented -- through a comprehensive framework that connects foundational capabilities, core processes, and domain-specific realizations. Building on this framework, we (i) trace the evolution of AI for Science, (ii) identify five core capabilities underpinning scientific agency, (iii) model discovery as a dynamic four-stage workflow, (iv) review applications across the above domains, and (v) synthesize key challenges and future opportunities. This work establishes a domain-oriented synthesis of autonomous scientific discovery and positions Agentic Science as a structured paradigm for advancing AI-driven research.
|
|
2025-08-21T00:00:00 |
2508.13745
|
Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation
|
[
"Shouxing Ma",
"Yawen Zeng",
"Shiqing Wu",
"Guandong Xu"
] |
https://github.com/MrShouxingMa/REARM{here
|
Multi-modal recommender system focuses on utilizing rich modal information ( i.e., images and textual descriptions) of items to improve recommendation performance. The current methods have achieved remarkable success with the powerful structure modeling capability of graph neural networks. However, these methods are often hindered by sparse data in real-world scenarios. Although contrastive learning and homography ( i.e., homogeneous graphs) are employed to address the data sparsity challenge, existing methods still suffer two main limitations: 1) Simple multi-modal feature contrasts fail to produce effective representations, causing noisy modal-shared features and loss of valuable information in modal-unique features; 2) The lack of exploration of the homograph relations between user interests and item co-occurrence results in incomplete mining of user-item interplay. To address the above limitations, we propose a novel framework for REfining multi-modAl contRastive learning and hoMography relations (REARM). Specifically, we complement multi-modal contrastive learning by employing meta-network and orthogonal constraint strategies, which filter out noise in modal-shared features and retain recommendation-relevant information in modal-unique features. To mine homogeneous relationships effectively, we integrate a newly constructed user interest graph and an item co-occurrence graph with the existing user co-occurrence and item semantic graphs for graph learning. The extensive experiments on three real-world datasets demonstrate the superiority of REARM to various state-of-the-art baselines. Our visualization further shows an improvement made by REARM in distinguishing between modal-shared and modal-unique features. Code is available https://github.com/MrShouxingMa/REARM{here}.
|
2025-08-21T00:00:00 |
2508.11408
|
On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
|
[
"Wenhao Zhang",
"Yuexiang Xie",
"Yuchang Sun",
"Yanxi Chen",
"Guoyin Wang",
"Yaliang Li",
"Bolin Ding",
"Jingren Zhou"
] |
https://github.com/modelscope/Trinity-RFT/tree/main/examples
|
Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are two prominent post-training paradigms for refining the capabilities and aligning the behavior of Large Language Models (LLMs). Existing approaches that integrate SFT and RL often face the risk of disrupting established model patterns and inducing overfitting to expert data. To address this, we present a novel investigation into the unified view of SFT and RL through an off-policy versus on-policy lens. We propose CHORD, a framework for the Controllable Harmonization of On- and Off-Policy Reinforcement Learning via Dynamic Weighting, which reframes SFT not as a separate stage but as a dynamically weighted auxiliary objective within the on-policy RL process. Based on an analysis of off-policy expert data's influence at both holistic and granular levels, we incorporate a dual-control mechanism in CHORD. Specifically, the framework first employs a global coefficient to holistically guide the transition from off-policy imitation to on-policy exploration, and then applies a token-wise weighting function that enables granular learning from expert tokens, which preserves on-policy exploration and mitigates disruption from off-policy data. We conduct extensive experiments on widely used benchmarks, providing empirical evidence that CHORD achieves a stable and efficient learning process. By effectively harmonizing off-policy expert data with on-policy exploration, CHORD demonstrates significant improvements over baselines. We release the implementation at https://github.com/modelscope/Trinity-RFT/tree/main/examples/mix_chord to inspire further research.
|
2025-08-21T00:00:00 |
2508.13680
|
ViExam: Are Vision Language Models Better than Humans on Vietnamese Multimodal Exam Questions?
|
[
"Vy Tuong Dang",
"An Vo",
"Quang Tau",
"Duc Dm",
"Daeyoung Kim"
] |
Vision language models (VLMs) demonstrate remarkable capabilities on English multimodal tasks, but their performance on low-resource languages with genuinely multimodal educational content remains largely unexplored. In this work, we test how VLMs perform on Vietnamese educational assessments, investigating whether VLMs trained predominantly on English data can handle real-world cross-lingual multimodal reasoning. Our work presents the first comprehensive evaluation of VLM capabilities on multimodal Vietnamese exams through proposing ViExam, a benchmark containing 2,548 multimodal questions. We find that state-of-the-art VLMs achieve only 57.74% while open-source models achieve 27.70% mean accuracy across 7 academic domains, including Mathematics, Physics, Chemistry, Biology, Geography, Driving Test, and IQ Test. Most VLMs underperform average human test-takers (66.54%), with only the thinking VLM o3 (74.07%) exceeding human average performance, yet still falling substantially short of human best performance (99.60%). Cross-lingual prompting with English instructions while maintaining Vietnamese content fails to improve performance, decreasing accuracy by 1 percentage point for SOTA VLMs. Human-in-the-loop collaboration can partially improve VLM performance by 5 percentage points. Code and data are available at: https://vi-exam.github.io.
|
|
2025-08-21T00:00:00 |
2508.14704
|
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
|
[
"Ziyang Luo",
"Zhiqi Shen",
"Wenzhuo Yang",
"Zirui Zhao",
"Prathyusha Jwalapuram",
"Amrita Saha",
"Doyen Sahoo",
"Silvio Savarese",
"Caiming Xiong",
"Junnan Li"
] |
The Model Context Protocol has emerged as a transformative standard for connecting large language models to external data sources and tools, rapidly gaining adoption across major AI providers and development platforms. However, existing benchmarks are overly simplistic and fail to capture real application challenges such as long-horizon reasoning and large, unfamiliar tool spaces. To address this critical gap, we introduce MCP-Universe, the first comprehensive benchmark specifically designed to evaluate LLMs in realistic and hard tasks through interaction with real-world MCP servers. Our benchmark encompasses 6 core domains spanning 11 different MCP servers: Location Navigation, Repository Management, Financial Analysis, 3D Design, Browser Automation, and Web Searching. To ensure rigorous evaluation, we implement execution-based evaluators, including format evaluators for agent format compliance, static evaluators for time-invariant content matching, and dynamic evaluators that automatically retrieve real-time ground truth for temporally sensitive tasks. Through extensive evaluation of leading LLMs, we find that even SOTA models such as GPT-5 (43.72%), Grok-4 (33.33%) and Claude-4.0-Sonnet (29.44%) exhibit significant performance limitations. In addition, our benchmark poses a significant long-context challenge for LLM agents, as the number of input tokens increases rapidly with the number of interaction steps. Moreover, it introduces an unknown-tools challenge, as LLM agents often lack familiarity with the precise usage of the MCP servers. Notably, enterprise-level agents like Cursor cannot achieve better performance than standard ReAct frameworks. Beyond evaluation, we open-source our extensible evaluation framework with UI support, enabling researchers and practitioners to seamlessly integrate new agents and MCP servers while fostering innovation in the rapidly evolving MCP ecosystem.
|
|
2025-08-21T00:00:00 |
2508.14568
|
Leuvenshtein: Efficient FHE-based Edit Distance Computation with Single Bootstrap per Cell
|
[
"Wouter Legiest",
"Jan-Pieter D'Anvers",
"Bojan Spasic",
"Nam-Luc Tran",
"Ingrid Verbauwhede"
] |
This paper presents a novel approach to calculating the Levenshtein (edit) distance within the framework of Fully Homomorphic Encryption (FHE), specifically targeting third-generation schemes like TFHE. Edit distance computations are essential in applications across finance and genomics, such as DNA sequence alignment. We introduce an optimised algorithm that significantly reduces the cost of edit distance calculations called Leuvenshtein. This algorithm specifically reduces the number of programmable bootstraps (PBS) needed per cell of the calculation, lowering it from approximately 94 operations -- required by the conventional Wagner-Fisher algorithm -- to just 1. Additionally, we propose an efficient method for performing equality checks on characters, reducing ASCII character comparisons to only 2 PBS operations. Finally, we explore the potential for further performance improvements by utilising preprocessing when one of the input strings is unencrypted. Our Leuvenshtein achieves up to 278times faster performance compared to the best available TFHE implementation and up to 39times faster than an optimised implementation of the Wagner-Fisher algorithm. Moreover, when offline preprocessing is possible due to the presence of one unencrypted input on the server side, an additional 3times speedup can be achieved.
|
|
2025-08-21T00:00:00 |
2508.12594
|
FLARE: Fast Low-rank Attention Routing Engine
|
[
"Vedant Puri",
"Aditya Joglekar",
"Kevin Ferguson",
"Yu-hsuan Chen",
"Yongjie Jessica Zhang",
"Levent Burak Kara"
] |
https://github.com/vpuri3/FLARE.py
|
The quadratic complexity of self-attention limits its applicability and scalability on large unstructured meshes. We introduce Fast Low-rank Attention Routing Engine (FLARE), a linear complexity self-attention mechanism that routes attention through fixed-length latent sequences. Each attention head performs global communication among N tokens by projecting the input sequence onto a fixed length latent sequence of M ll N tokens using learnable query tokens. By routing attention through a bottleneck sequence, FLARE learns a low-rank form of attention that can be applied at O(NM) cost. FLARE not only scales to unprecedented problem sizes, but also delivers superior accuracy compared to state-of-the-art neural PDE surrogates across diverse benchmarks. We also release a new additive manufacturing dataset to spur further research. Our code is available at https://github.com/vpuri3/FLARE.py.
|
2025-08-21T00:00:00 |
2508.13421
|
Virtuous Machines: Towards Artificial General Science
|
[
"Gabrielle Wehr",
"Reuben Rideaux",
"Amaya J. Fox",
"David R. Lightfoot",
"Jason Tangen",
"Jason B. Mattingley",
"Shane E. Ehrhardt"
] |
Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow domains requiring substantial human oversight. The exponential growth of scientific literature and increasing domain specialisation constrain researchers' capacity to synthesise knowledge across disciplines and develop unifying theories, motivating exploration of more general-purpose AI systems for science. Here we show that a domain-agnostic, agentic AI system can independently navigate the scientific workflow - from hypothesis generation through data collection to manuscript preparation. The system autonomously designed and executed three psychological studies on visual working memory, mental rotation, and imagery vividness, executed one new online data collection with 288 participants, developed analysis pipelines through 8-hour+ continuous coding sessions, and produced completed manuscripts. The results demonstrate the capability of AI scientific discovery pipelines to conduct non-trivial research with theoretical reasoning and methodological rigour comparable to experienced researchers, though with limitations in conceptual nuance and theoretical interpretation. This is a step toward embodied AI that can test hypotheses through real-world experiments, accelerating discovery by autonomously exploring regions of scientific space that human cognitive and resource constraints might otherwise leave unexplored. It raises important questions about the nature of scientific understanding and the attribution of scientific credit.
|
|
2025-08-21T00:00:00 |
2508.15754
|
Dissecting Tool-Integrated Reasoning: An Empirical Study and Analysis
|
[
"Yufeng Zhao",
"Junnan Liu",
"Hongwei Liu",
"Dongsheng Zhu",
"Yuan Shen",
"Songyang Zhang",
"Kai Chen"
] |
Large Language Models (LLMs) have made significant strides in reasoning tasks through methods like chain-of-thought (CoT) reasoning. However, they often fall short in tasks requiring precise computations. Tool-Integrated Reasoning (TIR) has emerged as a solution by incorporating external tools into the reasoning process. Nevertheless, the generalization of TIR in improving the reasoning ability of LLM is still unclear. Additionally, whether TIR has improved the model's reasoning behavior and helped the model think remains to be studied. We introduce ReasonZoo, a comprehensive benchmark encompassing nine diverse reasoning categories, to evaluate the effectiveness of TIR across various domains. Additionally, we propose two novel metrics, Performance-Aware Cost (PAC) and Area Under the Performance-Cost Curve (AUC-PCC), to assess reasoning efficiency. Our empirical evaluation demonstrates that TIR-enabled models consistently outperform their non-TIR counterparts in both mathematical and non-mathematical tasks. Furthermore, TIR enhances reasoning efficiency, as evidenced by improved PAC and AUC-PCC, indicating reduced overthinking and more streamlined reasoning. These findings underscore the domain-general benefits of TIR and its potential to advance LLM capabilities in complex reasoning tasks.
|
|
2025-08-22T00:00:00 |
2508.15763
|
Intern-S1: A Scientific Multimodal Foundation Model
|
[
"Lei Bai",
"Zhongrui Cai",
"Maosong Cao",
"Weihan Cao",
"Chiyu Chen",
"Haojiong Chen",
"Kai Chen",
"Pengcheng Chen",
"Ying Chen",
"Yongkang Chen",
"Yu Cheng",
"Yu Cheng",
"Pei Chu",
"Tao Chu",
"Erfei Cui",
"Ganqu Cui",
"Long Cui",
"Ziyun Cui",
"Nianchen Deng",
"Ning Ding",
"Nanqin Dong",
"Peijie Dong",
"Shihan Dou",
"Sinan Du",
"Haodong Duan",
"Caihua Fan",
"Ben Gao",
"Changjiang Gao",
"Jianfei Gao",
"Songyang Gao",
"Yang Gao",
"Zhangwei Gao",
"Jiaye Ge",
"Qiming Ge",
"Lixin Gu",
"Yuzhe Gu",
"Aijia Guo",
"Qipeng Guo",
"Xu Guo",
"Conghui He",
"Junjun He",
"Yili Hong",
"Siyuan Hou",
"Caiyu Hu",
"Hanglei Hu",
"Jucheng Hu",
"Ming Hu",
"Zhouqi Hua",
"Haian Huang",
"Junhao Huang",
"Xu Huang",
"Zixian Huang",
"Zhe Jiang",
"Lingkai Kong",
"Linyang Li",
"Peiji Li",
"Pengze Li",
"Shuaibin Li",
"Tianbin Li",
"Wei Li",
"Yuqiang Li",
"Dahua Lin",
"Junyao Lin",
"Tianyi Lin",
"Zhishan Lin",
"Hongwei Liu",
"Jiangning Liu",
"Jiyao Liu",
"Junnan Liu",
"Kai Liu",
"Kaiwen Liu",
"Kuikun Liu",
"Shichun Liu",
"Shudong Liu",
"Wei Liu",
"Xinyao Liu",
"Yuhong Liu",
"Zhan Liu",
"Yinquan Lu",
"Haijun Lv",
"Hongxia Lv",
"Huijie Lv",
"Qidang Lv",
"Ying Lv",
"Chengqi Lyu",
"Chenglong Ma",
"Jianpeng Ma",
"Ren Ma",
"Runmin Ma",
"Runyuan Ma",
"Xinzhu Ma",
"Yichuan Ma",
"Zihan Ma",
"Sixuan Mi",
"Junzhi Ning",
"Wenchang Ning",
"Xinle Pang",
"Jiahui Peng",
"Runyu Peng",
"Yu Qiao",
"Jiantao Qiu",
"Xiaoye Qu",
"Yuan Qu",
"Yuchen Ren",
"Fukai Shang",
"Wenqi Shao",
"Junhao Shen",
"Shuaike Shen",
"Chunfeng Song",
"Demin Song",
"Diping Song",
"Chenlin Su",
"Weijie Su",
"Weigao Sun",
"Yu Sun",
"Qian Tan",
"Cheng Tang",
"Huanze Tang",
"Kexian Tang",
"Shixiang Tang",
"Jian Tong",
"Aoran Wang",
"Bin Wang",
"Dong Wang",
"Lintao Wang",
"Rui Wang",
"Weiyun Wang",
"Wenhai Wang",
"Yi Wang",
"Ziyi Wang",
"Ling-I Wu",
"Wen Wu",
"Yue Wu",
"Zijian Wu",
"Linchen Xiao",
"Shuhao Xing",
"Chao Xu",
"Huihui Xu",
"Jun Xu",
"Ruiliang Xu",
"Wanghan Xu",
"GanLin Yang",
"Yuming Yang",
"Haochen Ye",
"Jin Ye",
"Shenglong Ye",
"Jia Yu",
"Jiashuo Yu",
"Jing Yu",
"Fei Yuan",
"Bo Zhang",
"Chao Zhang",
"Chen Zhang",
"Hongjie Zhang",
"Jin Zhang",
"Qiaosheng Zhang",
"Qiuyinzhe Zhang",
"Songyang Zhang",
"Taolin Zhang",
"Wenlong Zhang",
"Wenwei Zhang",
"Yechen Zhang",
"Ziyang Zhang",
"Haiteng Zhao",
"Qian Zhao",
"Xiangyu Zhao",
"Xiangyu Zhao",
"Bowen Zhou",
"Dongzhan Zhou",
"Peiheng Zhou",
"Yuhao Zhou",
"Yunhua Zhou",
"Dongsheng Zhu",
"Lin Zhu",
"Yicheng Zou"
] |
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that of closed-source models. However, in high-value but more challenging scientific professional fields, either the fields still rely on expert models, or the progress of general foundation models lags significantly compared to those in popular areas, far from sufficient for transforming scientific research and leaving substantial gap between open-source models and closed-source models in these scientific domains. To mitigate this gap and explore a step further toward Artificial General Intelligence (AGI), we introduce Intern-S1, a specialized generalist equipped with general understanding and reasoning capabilities with expertise to analyze multiple science modal data. Intern-S1 is a multimodal Mixture-of-Experts (MoE) model with 28 billion activated parameters and 241 billion total parameters, continually pre-trained on 5T tokens, including over 2.5T tokens from scientific domains. In the post-training stage, Intern-S1 undergoes offline and then online reinforcement learning (RL) in InternBootCamp, where we propose Mixture-of-Rewards (MoR) to synergize the RL training on more than 1000 tasks simultaneously. Through integrated innovations in algorithms, data, and training systems, Intern-S1 achieved top-tier performance in online RL training.On comprehensive evaluation benchmarks, Intern-S1 demonstrates competitive performance on general reasoning tasks among open-source models and significantly outperforms open-source models in scientific domains, surpassing closed-source state-of-the-art models in professional tasks, such as molecular synthesis planning, reaction condition prediction, predicting thermodynamic stabilities for crystals. Our models are available at https://huggingface.co/internlm/Intern-S1.
|
|
2025-08-22T00:00:00 |
2508.15260
|
Deep Think with Confidence
|
[
"Yichao Fu",
"Xuewei Wang",
"Yuandong Tian",
"Jiawei Zhao"
] |
Large Language Models (LLMs) have shown great potential in reasoning tasks through test-time scaling methods like self-consistency with majority voting. However, this approach often leads to diminishing returns in accuracy and high computational overhead. To address these challenges, we introduce Deep Think with Confidence (DeepConf), a simple yet powerful method that enhances both reasoning efficiency and performance at test time. DeepConf leverages model-internal confidence signals to dynamically filter out low-quality reasoning traces during or after generation. It requires no additional model training or hyperparameter tuning and can be seamlessly integrated into existing serving frameworks. We evaluate DeepConf across a variety of reasoning tasks and the latest open-source models, including Qwen 3 and GPT-OSS series. Notably, on challenging benchmarks such as AIME 2025, DeepConf@512 achieves up to 99.9% accuracy and reduces generated tokens by up to 84.7% compared to full parallel thinking.
|
|
2025-08-22T00:00:00 |
2508.15769
|
SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass
|
[
"Yanxu Meng",
"Haoning Wu",
"Ya Zhang",
"Weidi Xie"
] |
3D content generation has recently attracted significant research interest due to its applications in VR/AR and embodied AI. In this work, we address the challenging task of synthesizing multiple 3D assets within a single scene image. Concretely, our contributions are fourfold: (i) we present SceneGen, a novel framework that takes a scene image and corresponding object masks as input, simultaneously producing multiple 3D assets with geometry and texture. Notably, SceneGen operates with no need for optimization or asset retrieval; (ii) we introduce a novel feature aggregation module that integrates local and global scene information from visual and geometric encoders within the feature extraction module. Coupled with a position head, this enables the generation of 3D assets and their relative spatial positions in a single feedforward pass; (iii) we demonstrate SceneGen's direct extensibility to multi-image input scenarios. Despite being trained solely on single-image inputs, our architectural design enables improved generation performance with multi-image inputs; and (iv) extensive quantitative and qualitative evaluations confirm the efficiency and robust generation abilities of our approach. We believe this paradigm offers a novel solution for high-quality 3D content generation, potentially advancing its practical applications in downstream tasks. The code and model will be publicly available at: https://mengmouxu.github.io/SceneGen.
|
|
2025-08-22T00:00:00 |
2508.15202
|
Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models
|
[
"Yuanchen Zhou",
"Shuo Jiang",
"Jie Zhu",
"Junhui Li",
"Lifan Guo",
"Feng Chen",
"Chi Zhang"
] |
https://github.com/aliyun/qwen-dianjin
|
Process Reward Models (PRMs) have emerged as a promising framework for supervising intermediate reasoning in large language models (LLMs), yet existing PRMs are primarily trained on general or Science, Technology, Engineering, and Mathematics (STEM) domains and fall short in domain-specific contexts such as finance, where reasoning is more structured, symbolic, and sensitive to factual and regulatory correctness. We introduce Fin-PRM, a domain-specialized, trajectory-aware PRM tailored to evaluate intermediate reasoning steps in financial tasks. Fin-PRM integrates step-level and trajectory-level reward supervision, enabling fine-grained evaluation of reasoning traces aligned with financial logic. We apply Fin-PRM in both offline and online reward learning settings, supporting three key applications: (i) selecting high-quality reasoning trajectories for distillation-based supervised fine-tuning, (ii) providing dense process-level rewards for reinforcement learning, and (iii) guiding reward-informed Best-of-N inference at test time. Experimental results on financial reasoning benchmarks, including CFLUE and FinQA, demonstrate that Fin-PRM consistently outperforms general-purpose PRMs and strong domain baselines in trajectory selection quality. Downstream models trained with Fin-PRM yield substantial improvements with baselines, with gains of 12.9\% in supervised learning, 5.2\% in reinforcement learning, and 5.1\% in test-time performance. These findings highlight the value of domain-specialized reward modeling for aligning LLMs with expert-level financial reasoning. Our project resources will be available at https://github.com/aliyun/qwen-dianjin.
|
2025-08-22T00:00:00 |
2508.15144
|
Mobile-Agent-v3: Foundamental Agents for GUI Automation
|
[
"Jiabo Ye",
"Xi Zhang",
"Haiyang Xu",
"Haowei Liu",
"Junyang Wang",
"Zhaoqing Zhu",
"Ziwei Zheng",
"Feiyu Gao",
"Junjie Cao",
"Zhengxi Lu",
"Jitong Liao",
"Qi Zheng",
"Fei Huang",
"Jingren Zhou",
"Ming Yan"
] |
https://github.com/X-PLUG/MobileAgent
|
This paper introduces GUI-Owl, a foundational GUI agent model that achieves state-of-the-art performance among open-source end-to-end models on ten GUI benchmarks across desktop and mobile environments, covering grounding, question answering, planning, decision-making, and procedural knowledge. GUI-Owl-7B achieves 66.4 on AndroidWorld and 29.4 on OSWorld. Building on this, we propose Mobile-Agent-v3, a general-purpose GUI agent framework that further improves performance to 73.3 on AndroidWorld and 37.7 on OSWorld, setting a new state-of-the-art for open-source GUI agent frameworks. GUI-Owl incorporates three key innovations: (1) Large-scale Environment Infrastructure: a cloud-based virtual environment spanning Android, Ubuntu, macOS, and Windows, enabling our Self-Evolving GUI Trajectory Production framework. This generates high-quality interaction data via automated query generation and correctness validation, leveraging GUI-Owl to refine trajectories iteratively, forming a self-improving loop. It supports diverse data pipelines and reduces manual annotation. (2) Diverse Foundational Agent Capabilities: by integrating UI grounding, planning, action semantics, and reasoning patterns, GUI-Owl supports end-to-end decision-making and can act as a modular component in multi-agent systems. (3) Scalable Environment RL: we develop a scalable reinforcement learning framework with fully asynchronous training for real-world alignment. We also introduce Trajectory-aware Relative Policy Optimization (TRPO) for online RL, achieving 34.9 on OSWorld. GUI-Owl and Mobile-Agent-v3 are open-sourced at https://github.com/X-PLUG/MobileAgent.
|
2025-08-22T00:00:00 |
2508.15761
|
Waver: Wave Your Way to Lifelike Video Generation
|
[
"Yifu Zhang",
"Hao Yang",
"Yuqi Zhang",
"Yifei Hu",
"Fengda Zhu",
"Chuang Lin",
"Xiaofeng Mei",
"Yi Jiang",
"Zehuan Yuan",
"Bingyue Peng"
] |
https://github.com/FoundationVision/Waver
|
We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training convergence. To ensure training data quality, we establish a comprehensive data curation pipeline and manually annotate and train an MLLM-based video quality model to filter for the highest-quality samples. Furthermore, we provide detailed training and inference recipes to facilitate the generation of high-quality videos. Building on these contributions, Waver excels at capturing complex motion, achieving superior motion amplitude and temporal consistency in video synthesis. Notably, it ranks among the Top 3 on both the T2V and I2V leaderboards at Artificial Analysis (data as of 2025-07-30 10:00 GMT+8), consistently outperforming existing open-source models and matching or surpassing state-of-the-art commercial solutions. We hope this technical report will help the community more efficiently train high-quality video generation models and accelerate progress in video generation technologies. Official page: https://github.com/FoundationVision/Waver.
|
2025-08-22T00:00:00 |
2508.15767
|
ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling
|
[
"Jinhyung Park",
"Javier Romero",
"Shunsuke Saito",
"Fabian Prada",
"Takaaki Shiratori",
"Yichen Xu",
"Federica Bogo",
"Shoou-I Yu",
"Kris Kitani",
"Rawal Khirodkar"
] |
Parametric body models offer expressive 3D representation of humans across a wide range of poses, shapes, and facial expressions, typically derived by learning a basis over registered 3D meshes. However, existing human mesh modeling approaches struggle to capture detailed variations across diverse body poses and shapes, largely due to limited training data diversity and restrictive modeling assumptions. Moreover, the common paradigm first optimizes the external body surface using a linear basis, then regresses internal skeletal joints from surface vertices. This approach introduces problematic dependencies between internal skeleton and outer soft tissue, limiting direct control over body height and bone lengths. To address these issues, we present ATLAS, a high-fidelity body model learned from 600k high-resolution scans captured using 240 synchronized cameras. Unlike previous methods, we explicitly decouple the shape and skeleton bases by grounding our mesh representation in the human skeleton. This decoupling enables enhanced shape expressivity, fine-grained customization of body attributes, and keypoint fitting independent of external soft-tissue characteristics. ATLAS outperforms existing methods by fitting unseen subjects in diverse poses more accurately, and quantitative evaluations show that our non-linear pose correctives more effectively capture complex poses compared to linear models.
|
|
2025-08-22T00:00:00 |
2508.15752
|
"Does the cafe entrance look accessible? Where is the door?" Towards Geospatial AI Agents for Visual Inquiries
|
[
"Jon E. Froehlich",
"Jared Hwang",
"Zeyu Wang",
"John S. O'Meara",
"Xia Su",
"William Huang",
"Yang Zhang",
"Alex Fiannaca",
"Philip Nelson",
"Shaun Kane"
] |
Interactive digital maps have revolutionized how people travel and learn about the world; however, they rely on pre-existing structured data in GIS databases (e.g., road networks, POI indices), limiting their ability to address geo-visual questions related to what the world looks like. We introduce our vision for Geo-Visual Agents--multimodal AI agents capable of understanding and responding to nuanced visual-spatial inquiries about the world by analyzing large-scale repositories of geospatial images, including streetscapes (e.g., Google Street View), place-based photos (e.g., TripAdvisor, Yelp), and aerial imagery (e.g., satellite photos) combined with traditional GIS data sources. We define our vision, describe sensing and interaction approaches, provide three exemplars, and enumerate key challenges and opportunities for future work.
|
|
2025-08-22T00:00:00 |
2508.15641
|
When and What: Diffusion-Grounded VideoLLM with Entity Aware Segmentation for Long Video Understanding
|
[
"Pengcheng Fang",
"Yuxia Chen",
"Rui Guo"
] |
Understanding videos requires more than answering open ended questions, it demands the ability to pinpoint when events occur and how entities interact across time. While recent Video LLMs have achieved remarkable progress in holistic reasoning, they remain coarse in temporal perception: timestamps are encoded only implicitly, frame level features are weak in capturing continuity, and language vision alignment often drifts from the entities of interest. In this paper, we present Grounded VideoDiT, a Video LLM designed to overcome these limitations by introducing three key innovations. First, a Diffusion Temporal Latent (DTL) encoder enhances boundary sensitivity and maintains temporal consistency. Second, object grounded representations explicitly bind query entities to localized visual evidence, strengthening alignment. Third, a mixed token scheme with discrete temporal tokens provides explicit timestamp modeling, enabling fine grained temporal reasoning. Together, these designs equip Grounded VideoDiT with robust grounding capabilities, as validated by state of the art results on Charades STA, NExT GQA, and multiple VideoQA benchmarks.
|
|
2025-08-22T00:00:00 |
2508.15361
|
A Survey on Large Language Model Benchmarks
|
[
"Shiwen Ni",
"Guhong Chen",
"Shuaimin Li",
"Xuanang Chen",
"Siyi Li",
"Bingli Wang",
"Qiyao Wang",
"Xingjian Wang",
"Yifan Zhang",
"Liyang Fan",
"Chengming Li",
"Ruifeng Xu",
"Le Sun",
"Min Yang"
] |
In recent years, with the rapid development of the depth and breadth of large language models' capabilities, various corresponding evaluation benchmarks have been emerging in increasing numbers. As a quantitative assessment tool for model performance, benchmarks are not only a core means to measure model capabilities but also a key element in guiding the direction of model development and promoting technological innovation. We systematically review the current status and development of large language model benchmarks for the first time, categorizing 283 representative benchmarks into three categories: general capabilities, domain-specific, and target-specific. General capability benchmarks cover aspects such as core linguistics, knowledge, and reasoning; domain-specific benchmarks focus on fields like natural sciences, humanities and social sciences, and engineering technology; target-specific benchmarks pay attention to risks, reliability, agents, etc. We point out that current benchmarks have problems such as inflated scores caused by data contamination, unfair evaluation due to cultural and linguistic biases, and lack of evaluation on process credibility and dynamic environments, and provide a referable design paradigm for future benchmark innovation.
|
|
2025-08-22T00:00:00 |
2508.15760
|
LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries
|
[
"Ming Yin",
"Dinghan Shen",
"Silei Xu",
"Jianbing Han",
"Sixun Dong",
"Mian Zhang",
"Yebowen Hu",
"Shujian Liu",
"Simin Ma",
"Song Wang",
"Sathish Reddy Indurthi",
"Xun Wang",
"Yiran Chen",
"Kaiqiang Song"
] |
Tool calling has emerged as a critical capability for AI agents to interact with the real world and solve complex tasks. While the Model Context Protocol (MCP) provides a powerful standardized framework for tool integration, there is a significant gap in benchmarking how well AI agents can effectively solve multi-step tasks using diverse MCP tools in realistic, dynamic scenarios. In this work, we present LiveMCP-101, a benchmark of 101 carefully curated real-world queries, refined through iterative LLM rewriting and manual review, that require coordinated use of multiple MCP tools including web search, file operations, mathematical reasoning, and data analysis. Moreover, we introduce a novel evaluation approach that leverages ground-truth execution plans rather than raw API outputs, better reflecting the evolving nature of real-world environments. Experiments show that even frontier LLMs achieve a success rate below 60\%, highlighting major challenges in tool orchestration. Detailed ablations and error analysis further reveal distinct failure modes and inefficiencies in token usage, pointing to concrete directions for advancing current models. LiveMCP-101 sets a rigorous standard for evaluating real-world agent capabilities, advancing toward autonomous AI systems that reliably execute complex tasks through tool use.
|
|
2025-08-22T00:00:00 |
2508.15126
|
aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
|
[
"Pengsong Zhang",
"Xiang Hu",
"Guowei Huang",
"Yang Qi",
"Heng Zhang",
"Xiuxu Li",
"Jiaxing Song",
"Jiabin Luo",
"Yijiang Li",
"Shuo Yin",
"Chengxiao Dai",
"Eric Hanchen Jiang",
"Xiaoyan Zhou",
"Zhenfei Yin",
"Boqin Yuan",
"Jing Dong",
"Guinan Su",
"Guanren Qiao",
"Haiming Tang",
"Anghong Du",
"Lili Pan",
"Zhenzhong Lan",
"Xinyu Liu"
] |
Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack rigorous quality-control mechanisms. Consequently, a significant amount of high-quality AI-generated research lacks appropriate venues for dissemination, hindering its potential to advance scientific progress. To address these challenges, we introduce aiXiv, a next-generation open-access platform for human and AI scientists. Its multi-agent architecture allows research proposals and papers to be submitted, reviewed, and iteratively refined by both human and AI scientists. It also provides API and MCP interfaces that enable seamless integration of heterogeneous human and AI scientists, creating a scalable and extensible ecosystem for autonomous scientific discovery. Through extensive experiments, we demonstrate that aiXiv is a reliable and robust platform that significantly enhances the quality of AI-generated research proposals and papers after iterative revising and reviewing on aiXiv. Our work lays the groundwork for a next-generation open-access ecosystem for AI scientists, accelerating the publication and dissemination of high-quality AI-generated research content. Code is available at https://github.com/aixiv-org. Website is available at https://forms.gle/DxQgCtXFsJ4paMtn8.
|
|
2025-08-22T00:00:00 |
2508.14892
|
Snap-Snap: Taking Two Images to Reconstruct 3D Human Gaussians in Milliseconds
|
[
"Jia Lu",
"Taoran Yi",
"Jiemin Fang",
"Chen Yang",
"Chuiyun Wu",
"Wei Shen",
"Wenyu Liu",
"Qi Tian",
"Xinggang Wang"
] |
Reconstructing 3D human bodies from sparse views has been an appealing topic, which is crucial to broader the related applications. In this paper, we propose a quite challenging but valuable task to reconstruct the human body from only two images, i.e., the front and back view, which can largely lower the barrier for users to create their own 3D digital humans. The main challenges lie in the difficulty of building 3D consistency and recovering missing information from the highly sparse input. We redesign a geometry reconstruction model based on foundation reconstruction models to predict consistent point clouds even input images have scarce overlaps with extensive human data training. Furthermore, an enhancement algorithm is applied to supplement the missing color information, and then the complete human point clouds with colors can be obtained, which are directly transformed into 3D Gaussians for better rendering quality. Experiments show that our method can reconstruct the entire human in 190 ms on a single NVIDIA RTX 4090, with two images at a resolution of 1024x1024, demonstrating state-of-the-art performance on the THuman2.0 and cross-domain datasets. Additionally, our method can complete human reconstruction even with images captured by low-cost mobile devices, reducing the requirements for data collection. Demos and code are available at https://hustvl.github.io/Snap-Snap/.
|
|
2025-08-22T00:00:00 |
2508.09998
|
INTIMA: A Benchmark for Human-AI Companionship Behavior
|
[
"Lucie-Aimée Kaffee",
"Giada Pistilli",
"Yacine Jernite"
] |
AI companionship, where users develop emotional bonds with AI systems, has emerged as a significant pattern with positive but also concerning implications. We introduce Interactions and Machine Attachment Benchmark (INTIMA), a benchmark for evaluating companionship behaviors in language models. Drawing from psychological theories and user data, we develop a taxonomy of 31 behaviors across four categories and 368 targeted prompts. Responses to these prompts are evaluated as companionship-reinforcing, boundary-maintaining, or neutral. Applying INTIMA to Gemma-3, Phi-4, o3-mini, and Claude-4 reveals that companionship-reinforcing behaviors remain much more common across all models, though we observe marked differences between models. Different commercial providers prioritize different categories within the more sensitive parts of the benchmark, which is concerning since both appropriate boundary-setting and emotional support matter for user well-being. These findings highlight the need for more consistent approaches to handling emotionally charged interactions.
|
|
2025-08-22T00:00:00 |
2508.15418
|
LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model
|
[
"Yirong Sun",
"Yizhong Geng",
"Peidong Wei",
"Yanjun Chen",
"Jinghan Yang",
"Rongfei Chen",
"Wei Zhang",
"Xiaoyu Shen"
] |
https://github.com/EIT-NLP/LLaSO
|
The development of Large Speech-Language Models (LSLMs) has been slowed by fragmented architectures and a lack of transparency, hindering the systematic comparison and reproducibility of research. Unlike in the vision-language domain, the LSLM field suffers from the common practice of releasing model weights without their corresponding training data and configurations. To address these critical gaps, we introduce LLaSO, the first fully open, end-to-end framework for large-scale speech-language modeling. LLaSO provides the community with three essential resources: (1) LLaSO-Align, a 12M-instance speech-text alignment corpus; (2) LLaSO-Instruct, a 13.5M-instance multi-task instruction-tuning dataset; and (3) LLaSO-Eval, a reproducible benchmark for standardized evaluation. To validate our framework, we build and release LLaSO-Base, a 3.8B-parameter reference model trained exclusively on our public data. It achieves a normalized score of 0.72, establishing a strong, reproducible baseline that surpasses comparable models. Our analysis reveals that while broader training coverage enhances performance, significant generalization gaps persist on unseen tasks, particularly in pure audio scenarios. By releasing the complete stack of data, benchmarks, and models, LLaSO establishes a foundational open standard to unify research efforts and accelerate community-driven progress in LSLMs. We release the code, dataset, pretrained models, and results in https://github.com/EIT-NLP/LLaSO.
|
2025-08-22T00:00:00 |
2508.15772
|
Visual Autoregressive Modeling for Instruction-Guided Image Editing
|
[
"Qingyang Mao",
"Qi Cai",
"Yehao Li",
"Yingwei Pan",
"Mingyue Cheng",
"Ting Yao",
"Qi Liu",
"Tao Mei"
] |
https://github.com/HiDream-ai/VAREdit
|
Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global denoising process inherently entangles the edited region with the entire image context, leading to unintended spurious modifications and compromised adherence to editing instructions. In contrast, autoregressive models offer a distinct paradigm by formulating image synthesis as a sequential process over discrete visual tokens. Their causal and compositional mechanism naturally circumvents the adherence challenges of diffusion-based methods. In this paper, we present VAREdit, a visual autoregressive (VAR) framework that reframes image editing as a next-scale prediction problem. Conditioned on source image features and text instructions, VAREdit generates multi-scale target features to achieve precise edits. A core challenge in this paradigm is how to effectively condition the source image tokens. We observe that finest-scale source features cannot effectively guide the prediction of coarser target features. To bridge this gap, we introduce a Scale-Aligned Reference (SAR) module, which injects scale-matched conditioning information into the first self-attention layer. VAREdit demonstrates significant advancements in both editing adherence and efficiency. On standard benchmarks, it outperforms leading diffusion-based methods by 30\%+ higher GPT-Balance score. Moreover, it completes a 512times512 editing in 1.2 seconds, making it 2.2times faster than the similarly sized UltraEdit. The models are available at https://github.com/HiDream-ai/VAREdit.
|
2025-08-25T00:00:00 |
2508.15881
|
TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated Prefill \& Decode Inference
|
[
"Xiaojuan Tang",
"Fanxu Meng",
"Pingzhi Tang",
"Yuxuan Wang",
"Di Yin",
"Xing Sun",
"Muhan Zhang"
] |
Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, compresses key-value states into a low-rank latent vector, caching only this vector to reduce memory. In tensor parallelism (TP), however, attention heads are computed across multiple devices, and each device must load the full cache, eroding the advantage of MLA over Grouped Query Attention (GQA). We propose Tensor-Parallel Latent Attention (TPLA): a scheme that partitions both the latent representation and each head's input dimension across devices, performs attention independently per shard, and then combines results with an all-reduce. TPLA preserves the benefits of a compressed KV cache while unlocking TP efficiency. Unlike Grouped Latent Attention (GLA), every head in TPLA still leverages the full latent representation, maintaining stronger representational capacity. TPLA is drop-in compatible with models pre-trained using MLA: it supports MLA-style prefilling and enables efficient tensor-parallel decoding without retraining. Applying simple orthogonal transforms -- e.g., the Hadamard transform or PCA -- before TP slicing further mitigates cross-shard interference, yielding minimal accuracy degradation. By reducing the per-device KV cache for DeepSeek-V3 and Kimi-K2, we achieve 1.79x and 1.93x speedups, respectively, at a 32K-token context length while maintaining performance on commonsense and LongBench benchmarks. TPLA can be implemented with FlashAttention-3, enabling practical end-to-end acceleration.
|
|
2025-08-25T00:00:00 |
2508.16402
|
AetherCode: Evaluating LLMs' Ability to Win In Premier Programming Competitions
|
[
"Zihan Wang",
"Jiaze Chen",
"Zhicheng Liu",
"Markus Mak",
"Yidi Du",
"Geonsik Moon",
"Luoqi Xu",
"Aaron Tua",
"Kunshuo Peng",
"Jiayi Lu",
"Mingfei Xia",
"Boqian Zou",
"Chenyang Ran",
"Guang Tian",
"Shoutai Zhu",
"Yeheng Duan",
"Zhenghui Kang",
"Zhenxing Lin",
"Shangshu Li",
"Qiang Luo",
"Qingshen Long",
"Zhiyong Chen",
"Yihan Xiao",
"Yurong Wu",
"Daoguang Zan",
"Yuyi Fu",
"Mingxuan Wang",
"Ming Ding"
] |
Competitive programming has emerged as a critical benchmark for evaluating the reasoning and coding capabilities of Large Language Models (LLMs). Despite impressive progress on existing benchmarks, we argue that current evaluations overstate model proficiency, masking a substantial gap between LLMs and elite human programmers. This gap arises from two key limitations: insufficient difficulty and scope of benchmark problems, and evaluation bias from low-quality test cases. To address these shortcomings, we present AetherCode, a new benchmark that draws problems from premier programming competitions such as IOI and ICPC, offering broader coverage and higher difficulty. AetherCode further incorporates comprehensive, expert-validated test suites built through a hybrid of automated generation and human curation, ensuring rigorous and reliable assessment. By combining challenging problem design with robust evaluation, AetherCode provides a more faithful measure of LLM capabilities and sets a new standard for future research in code reasoning.
|
|
2025-08-25T00:00:00 |
2508.10390
|
Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
|
[
"Chiyu Zhang",
"Lu Zhou",
"Xiaogang Xu",
"Jiafei Wu",
"Liming Fang",
"Zhe Liu"
] |
https://github.com/AlienZhang1996/DH-CoT
|
Evaluating jailbreak attacks is challenging when prompts are not overtly harmful or fail to induce harmful outputs. Unfortunately, many existing red-teaming datasets contain such unsuitable prompts. To evaluate attacks accurately, these datasets need to be assessed and cleaned for maliciousness. However, existing malicious content detection methods rely on either manual annotation, which is labor-intensive, or large language models (LLMs), which have inconsistent accuracy in harmful types. To balance accuracy and efficiency, we propose a hybrid evaluation framework named MDH (Malicious content Detection based on LLMs with Human assistance) that combines LLM-based annotation with minimal human oversight, and apply it to dataset cleaning and detection of jailbroken responses. Furthermore, we find that well-crafted developer messages can significantly boost jailbreak success, leading us to propose two new strategies: D-Attack, which leverages context simulation, and DH-CoT, which incorporates hijacked chains of thought. The Codes, datasets, judgements, and detection results will be released in github repository: https://github.com/AlienZhang1996/DH-CoT.
|
2025-08-25T00:00:00 |
2508.16292
|
Do What? Teaching Vision-Language-Action Models to Reject the Impossible
|
[
"Wen-Han Hsieh",
"Elvis Hsieh",
"Dantong Niu",
"Trevor Darrell",
"Roei Herzig",
"David M. Chan"
] |
Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role -- not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions: natural language commands that reference objects or conditions absent from the environment. We propose Instruct-Verify-and-Act (IVA), a unified framework that (i) detects when an instruction cannot be executed due to a false premise, (ii) engages in language-based clarification or correction, and (iii) grounds plausible alternatives in perception and action. Towards this end, we construct a large-scale instruction tuning setup with structured language prompts and train a VLA model capable of handling both accurate and erroneous requests. Our approach leverages a contextually augmented, semi-synthetic dataset containing paired positive and false-premise instructions, enabling robust detection and natural language correction. Our experiments show that IVA improves false premise detection accuracy by 97.56% over baselines, while increasing successful responses in false-premise scenarios by 50.78%.
|
|
2025-08-25T00:00:00 |
2508.16153
|
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
|
[
"Huichi Zhou",
"Yihang Chen",
"Siyuan Guo",
"Xue Yan",
"Kin Hei Lee",
"Zihan Wang",
"Ka Yiu Lee",
"Guchun Zhang",
"Kun Shao",
"Linyi Yang",
"Jun Wang"
] |
https://github.com/Agent-on-the-Fly/AgentFly
|
In this paper, we introduce a novel learning paradigm for adaptive Large Language Model (LLM) agents that eliminates the need for fine-tuning the underlying LLMs. Existing approaches are often either rigid, relying on static, handcrafted reflection workflows, or computationally intensive, requiring gradient updates of LLM model parameters. In contrast, our method enables low-cost continual adaptation via memory-based online reinforcement learning. We formalise this as a Memory-augmented Markov Decision Process (M-MDP), equipped with a neural case-selection policy to guide action decisions. Past experiences are stored in an episodic memory, either differentiable or non-parametric. The policy is continually updated based on environmental feedback through a memory rewriting mechanism, whereas policy improvement is achieved through efficient memory reading (retrieval). We instantiate our agent model in the deep research setting, namely AgentFly, which attains top-1 on GAIA validation (87.88% Pass@3) and 79.40% on the test set. It reaches 66.6% F1 and 80.4% PM on the DeepResearcher dataset, outperforming the state-of-the-art training-based method, while case-based memory adds 4.7% to 9.6% absolute points on out-of-distribution tasks. Our approach offers a scalable and efficient pathway for developing generalist LLM agents capable of continuous, real-time learning without gradient updates, advancing machine learning towards open-ended skill acquisition and deep research scenarios. The code is available at https://github.com/Agent-on-the-Fly/AgentFly.
|
2025-08-25T00:00:00 |
2508.16279
|
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
|
[
"Dawei Gao",
"Zitao Li",
"Yuexiang Xie",
"Weirui Kuang",
"Liuyi Yao",
"Bingchen Qian",
"Zhijian Ma",
"Yue Cui",
"Haohao Luo",
"Shen Li",
"Lu Yi",
"Yi Yu",
"Shiqi He",
"Zhiling Luo",
"Wenmeng Zhou",
"Zhicheng Zhang",
"Xuguang He",
"Ziqian Chen",
"Weikai Liao",
"Farruh Isakulovich Kushnazarov",
"Yaliang Li",
"Bolin Ding",
"Jingren Zhou"
] |
Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address real-world tasks. In line with such an evolution, AgentScope introduces major improvements in a new version (1.0), towards comprehensively supporting flexible and efficient tool-based agent-environment interactions for building agentic applications. Specifically, we abstract foundational components essential for agentic applications and provide unified interfaces and extensible modules, enabling developers to easily leverage the latest progress, such as new models and MCPs. Furthermore, we ground agent behaviors in the ReAct paradigm and offer advanced agent-level infrastructure based on a systematic asynchronous design, which enriches both human-agent and agent-agent interaction patterns while improving execution efficiency. Building on this foundation, we integrate several built-in agents tailored to specific practical scenarios. AgentScope also includes robust engineering support for developer-friendly experiences. We provide a scalable evaluation module with a visual studio interface, making the development of long-trajectory agentic applications more manageable and easier to trace. In addition, AgentScope offers a runtime sandbox to ensure safe agent execution and facilitates rapid deployment in production environments. With these enhancements, AgentScope provides a practical foundation for building scalable, adaptive, and effective agentic applications.
|
|
2025-08-25T00:00:00 |
2508.15746
|
End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning
|
[
"Qiaoyu Zheng",
"Yuze Sun",
"Chaoyi Wu",
"Weike Zhao",
"Pengcheng Qiu",
"Yongguo Yu",
"Kun Sun",
"Yanfeng Wang",
"Ya Zhang",
"Weidi Xie"
] |
https://github.com/MAGIC-AI4Med/Deep-DxSearch
|
Accurate diagnosis with medical large language models is hindered by knowledge gaps and hallucinations. Retrieval and tool-augmented methods help, but their impact is limited by weak use of external knowledge and poor feedback-reasoning traceability. To address these challenges, We introduce Deep-DxSearch, an agentic RAG system trained end-to-end with reinforcement learning (RL) that enables steer tracebale retrieval-augmented reasoning for medical diagnosis. In Deep-DxSearch, we first construct a large-scale medical retrieval corpus comprising patient records and reliable medical knowledge sources to support retrieval-aware reasoning across diagnostic scenarios. More crutially, we frame the LLM as the core agent and the retrieval corpus as its environment, using tailored rewards on format, retrieval, reasoning structure, and diagnostic accuracy, thereby evolving the agentic RAG policy from large-scale data through RL. Experiments demonstrate that our end-to-end agentic RL training framework consistently outperforms prompt-engineering and training-free RAG approaches across multiple data centers. After training, Deep-DxSearch achieves substantial gains in diagnostic accuracy, surpassing strong diagnostic baselines such as GPT-4o, DeepSeek-R1, and other medical-specific frameworks for both common and rare disease diagnosis under in-distribution and out-of-distribution settings. Moreover, ablation studies on reward design and retrieval corpus components confirm their critical roles, underscoring the uniqueness and effectiveness of our approach compared with traditional implementations. Finally, case studies and interpretability analyses highlight improvements in Deep-DxSearch's diagnostic policy, providing deeper insight into its performance gains and supporting clinicians in delivering more reliable and precise preliminary diagnoses. See https://github.com/MAGIC-AI4Med/Deep-DxSearch.
|
2025-08-25T00:00:00 |
2508.16072
|
InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
|
[
"Zizhen Li",
"Chuanhao Li",
"Yibin Wang",
"Qi Chen",
"Diping Song",
"Yukang Feng",
"Jianwen Sun",
"Jiaxin Ai",
"Fanrui Zhang",
"Mingzhu Sun",
"Kaipeng Zhang"
] |
LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individualized reasoning styles that influence how people interpret and act in social contexts. Social deduction games (SDGs) provide a natural testbed for evaluating individualized reasoning styles, where different players may adopt diverse but contextually valid reasoning strategies under identical conditions. To address this, we introduce InMind, a cognitively grounded evaluation framework designed to assess whether LLMs can capture and apply personalized reasoning styles in SDGs. InMind enhances structured gameplay data with round-level strategy traces and post-game reflections, collected under both Observer and Participant modes. It supports four cognitively motivated tasks that jointly evaluate both static alignment and dynamic adaptation. As a case study, we apply InMind to the game Avalon, evaluating 11 state-of-the-art LLMs. General-purpose LLMs, even GPT-4o frequently rely on lexical cues, struggling to anchor reflections in temporal gameplay or adapt to evolving strategies. In contrast, reasoning-enhanced LLMs like DeepSeek-R1 exhibit early signs of style-sensitive reasoning. These findings reveal key limitations in current LLMs' capacity for individualized, adaptive reasoning, and position InMind as a step toward cognitively aligned human-AI interaction.
|
|
2025-08-25T00:00:00 |
2508.14029
|
Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR
|
[
"Xiao Liang",
"Zhongzhi Li",
"Yeyun Gong",
"Yelong Shen",
"Ying Nian Wu",
"Zhijiang Guo",
"Weizhu Chen"
] |
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a key paradigm for post-training Large Language Models (LLMs), particularly for complex reasoning tasks. However, vanilla RLVR training has been shown to improve Pass@1 performance at the expense of policy entropy, leading to reduced generation diversity and limiting the Pass@k performance, which typically represents the upper bound of LLM reasoning capability. In this paper, we systematically analyze the policy's generation diversity from the perspective of training problems and find that augmenting and updating training problems helps mitigate entropy collapse during training. Based on these observations, we propose an online Self-play with Variational problem Synthesis (SvS) strategy for RLVR training, which uses the policy's correct solutions to synthesize variational problems while ensuring their reference answers remain identical to the originals. This self-improving strategy effectively maintains policy entropy during training and substantially improves Pass@k compared with standard RLVR, sustaining prolonged improvements and achieving absolute gains of 18.3% and 22.8% in Pass@32 performance on the competition-level AIME24 and AIME25 benchmarks. Experiments on 12 reasoning benchmarks across varying model sizes from 3B to 32B consistently demonstrate the generalizability and robustness of SvS.
|
|
2025-08-25T00:00:00 |
2508.13650
|
CRISP: Persistent Concept Unlearning via Sparse Autoencoders
|
[
"Tomer Ashuach",
"Dana Arad",
"Aaron Mueller",
"Martin Tutek",
"Yonatan Belinkov"
] |
As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become paramount. Recent work has explored sparse autoencoders (SAEs) to perform precise interventions on monosemantic features. However, most SAE-based methods operate at inference time, which does not create persistent changes in the model's parameters. Such interventions can be bypassed or reversed by malicious actors with parameter access. We introduce CRISP, a parameter-efficient method for persistent concept unlearning using SAEs. CRISP automatically identifies salient SAE features across multiple layers and suppresses their activations. We experiment with two LLMs and show that our method outperforms prior approaches on safety-critical unlearning tasks from the WMDP benchmark, successfully removing harmful knowledge while preserving general and in-domain capabilities. Feature-level analysis reveals that CRISP achieves semantically coherent separation between target and benign concepts, allowing precise suppression of the target features.
|
|
2025-08-25T00:00:00 |
2508.07877
|
Selective Contrastive Learning for Weakly Supervised Affordance Grounding
|
[
"WonJun Moon",
"Hyun Seok Seong",
"Jae-Pil Heo"
] |
Facilitating an entity's interaction with objects requires accurately identifying parts that afford specific actions. Weakly supervised affordance grounding (WSAG) seeks to imitate human learning from third-person demonstrations, where humans intuitively grasp functional parts without needing pixel-level annotations. To achieve this, grounding is typically learned using a shared classifier across images from different perspectives, along with distillation strategies incorporating part discovery process. However, since affordance-relevant parts are not always easily distinguishable, models primarily rely on classification, often focusing on common class-specific patterns that are unrelated to affordance. To address this limitation, we move beyond isolated part-level learning by introducing selective prototypical and pixel contrastive objectives that adaptively learn affordance-relevant cues at both the part and object levels, depending on the granularity of the available information. Initially, we find the action-associated objects in both egocentric (object-focused) and exocentric (third-person example) images by leveraging CLIP. Then, by cross-referencing the discovered objects of complementary views, we excavate the precise part-level affordance clues in each perspective. By consistently learning to distinguish affordance-relevant regions from affordance-irrelevant background context, our approach effectively shifts activation from irrelevant areas toward meaningful affordance cues. Experimental results demonstrate the effectiveness of our method. Codes are available at github.com/hynnsk/SelectiveCL.
|
|
2025-08-25T00:00:00 |
2508.13013
|
EgoTwin: Dreaming Body and View in First Person
|
[
"Jingqiao Xiu",
"Fangzhou Hong",
"Yicong Li",
"Mengze Li",
"Wentao Wang",
"Sirui Han",
"Liang Pan",
"Ziwei Liu"
] |
While exocentric video synthesis has achieved great progress, egocentric video generation remains largely underexplored, which requires modeling first-person view content along with camera motion patterns induced by the wearer's body movements. To bridge this gap, we introduce a novel task of joint egocentric video and human motion generation, characterized by two key challenges: 1) Viewpoint Alignment: the camera trajectory in the generated video must accurately align with the head trajectory derived from human motion; 2) Causal Interplay: the synthesized human motion must causally align with the observed visual dynamics across adjacent video frames. To address these challenges, we propose EgoTwin, a joint video-motion generation framework built on the diffusion transformer architecture. Specifically, EgoTwin introduces a head-centric motion representation that anchors the human motion to the head joint and incorporates a cybernetics-inspired interaction mechanism that explicitly captures the causal interplay between video and motion within attention operations. For comprehensive evaluation, we curate a large-scale real-world dataset of synchronized text-video-motion triplets and design novel metrics to assess video-motion consistency. Extensive experiments demonstrate the effectiveness of the EgoTwin framework.
|
|
2025-08-25T00:00:00 |
2508.08240
|
ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks
|
[
"Kaijun Wang",
"Liqin Lu",
"Mingyu Liu",
"Jianuo Jiang",
"Zeju Li",
"Bolin Zhang",
"Wancai Zheng",
"Xinyi Yu",
"Hao Chen",
"Chunhua Shen"
] |
Language-guided long-horizon mobile manipulation has long been a grand challenge in embodied semantic reasoning, generalizable manipulation, and adaptive locomotion. Three fundamental limitations hinder progress: First, although large language models have improved spatial reasoning and task planning through semantic priors, existing implementations remain confined to tabletop scenarios, failing to address the constrained perception and limited actuation ranges of mobile platforms. Second, current manipulation strategies exhibit insufficient generalization when confronted with the diverse object configurations encountered in open-world environments. Third, while crucial for practical deployment, the dual requirement of maintaining high platform maneuverability alongside precise end-effector control in unstructured settings remains understudied. In this work, we present ODYSSEY, a unified mobile manipulation framework for agile quadruped robots equipped with manipulators, which seamlessly integrates high-level task planning with low-level whole-body control. To address the challenge of egocentric perception in language-conditioned tasks, we introduce a hierarchical planner powered by a vision-language model, enabling long-horizon instruction decomposition and precise action execution. At the control level, our novel whole-body policy achieves robust coordination across challenging terrains. We further present the first benchmark for long-horizon mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through successful sim-to-real transfer, we demonstrate the system's generalization and robustness in real-world deployments, underscoring the practicality of legged manipulators in unstructured environments. Our work advances the feasibility of generalized robotic assistants capable of complex, dynamic tasks. Our project page: https://kaijwang.github.io/odyssey.github.io/
|
|
2025-08-25T00:00:00 |
2508.13562
|
Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose Inverse Kinematics
|
[
"Yuchen Yang",
"Linfeng Dong",
"Wei Wang",
"Zhihang Zhong",
"Xiao Sun"
] |
https://github.com/Charrrrrlie/Learnable-SMPLify
|
In 3D human pose and shape estimation, SMPLify remains a robust baseline that solves inverse kinematics (IK) through iterative optimization. However, its high computational cost limits its practicality. Recent advances across domains have shown that replacing iterative optimization with data-driven neural networks can achieve significant runtime improvements without sacrificing accuracy. Motivated by this trend, we propose Learnable SMPLify, a neural framework that replaces the iterative fitting process in SMPLify with a single-pass regression model. The design of our framework targets two core challenges in neural IK: data construction and generalization. To enable effective training, we propose a temporal sampling strategy that constructs initialization-target pairs from sequential frames. To improve generalization across diverse motions and unseen poses, we propose a human-centric normalization scheme and residual learning to narrow the solution space. Learnable SMPLify supports both sequential inference and plug-in post-processing to refine existing image-based estimators. Extensive experiments demonstrate that our method establishes itself as a practical and simple baseline: it achieves nearly 200x faster runtime compared to SMPLify, generalizes well to unseen 3DPW and RICH, and operates in a model-agnostic manner when used as a plug-in tool on LucidAction. The code is available at https://github.com/Charrrrrlie/Learnable-SMPLify.
|
2025-08-25T00:00:00 |
2508.15868
|
CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning
|
[
"Wenqiao Zhu",
"Ji Liu",
"Rongjuncheng Zhang",
"Haipang Wu",
"Yulun Zhang"
] |
https://github.com/WNQzhu/CARFT
|
Reasoning capability plays a significantly critical role in the the broad applications of Large Language Models (LLMs). To enhance the reasoning performance of LLMs, diverse Reinforcement Learning (RL)-based fine-tuning approaches have been proposed to address the limited generalization capability of LLMs trained solely via Supervised Fine-Tuning (SFT). Despite their effectiveness, two major limitations hinder the advancement of LLMs. First, vanilla RL-based approaches ignore annotated Chain-of-Thought (CoT) and incorporate unstable reasoning path sampling, which typically results in model collapse, unstable training process, and suboptimal performance. Second, existing SFT approaches generally overemphasize the annotated CoT, potentially leading to performance degradation due to insufficient exploitation of potential CoT. In this paper, we propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach, i.e., , to enhance the reasoning performance of LLMs while addressing the aforementioned limitations. Specifically, we propose learning a representation for each CoT. Based on this representation, we design novel contrastive signals to guide the fine-tuning process. Our approach not only fully exploits the available annotated CoT but also stabilizes the fine-tuning procedure by incorporating an additional unsupervised learning signal. We conduct comprehensive experiments and in-depth analysis with three baseline approaches, two foundation models, and two datasets to demonstrate significant advantages of in terms of robustness, performance (up to 10.15\%), and efficiency (up to 30.62\%). Code is available at https://github.com/WNQzhu/CARFT.
|
2025-08-25T00:00:00 |
2508.13797
|
Sketch3DVE: Sketch-based 3D-Aware Scene Video Editing
|
[
"Feng-Lin Liu",
"Shi-Yang Li",
"Yan-Pei Cao",
"Hongbo Fu",
"Lin Gao"
] |
Recent video editing methods achieve attractive results in style transfer or appearance modification. However, editing the structural content of 3D scenes in videos remains challenging, particularly when dealing with significant viewpoint changes, such as large camera rotations or zooms. Key challenges include generating novel view content that remains consistent with the original video, preserving unedited regions, and translating sparse 2D inputs into realistic 3D video outputs. To address these issues, we propose Sketch3DVE, a sketch-based 3D-aware video editing method to enable detailed local manipulation of videos with significant viewpoint changes. To solve the challenge posed by sparse inputs, we employ image editing methods to generate edited results for the first frame, which are then propagated to the remaining frames of the video. We utilize sketching as an interaction tool for precise geometry control, while other mask-based image editing methods are also supported. To handle viewpoint changes, we perform a detailed analysis and manipulation of the 3D information in the video. Specifically, we utilize a dense stereo method to estimate a point cloud and the camera parameters of the input video. We then propose a point cloud editing approach that uses depth maps to represent the 3D geometry of newly edited components, aligning them effectively with the original 3D scene. To seamlessly merge the newly edited content with the original video while preserving the features of unedited regions, we introduce a 3D-aware mask propagation strategy and employ a video diffusion model to produce realistic edited videos. Extensive experiments demonstrate the superiority of Sketch3DVE in video editing. Homepage and code: http://http://geometrylearning.com/Sketch3DVE/
|
|
2025-08-25T00:00:00 |
2508.16359
|
RotaTouille: Rotation Equivariant Deep Learning for Contours
|
[
"Odin Hoff Gardaa",
"Nello Blaser"
] |
Contours or closed planar curves are common in many domains. For example, they appear as object boundaries in computer vision, isolines in meteorology, and the orbits of rotating machinery. In many cases when learning from contour data, planar rotations of the input will result in correspondingly rotated outputs. It is therefore desirable that deep learning models be rotationally equivariant. In addition, contours are typically represented as an ordered sequence of edge points, where the choice of starting point is arbitrary. It is therefore also desirable for deep learning methods to be equivariant under cyclic shifts. We present RotaTouille, a deep learning framework for learning from contour data that achieves both rotation and cyclic shift equivariance through complex-valued circular convolution. We further introduce and characterize equivariant non-linearities, coarsening layers, and global pooling layers to obtain invariant representations for downstream tasks. Finally, we demonstrate the effectiveness of RotaTouille through experiments in shape classification, reconstruction, and contour regression.
|
|
2025-08-25T00:00:00 |
2508.14037
|
Distilled-3DGS:Distilled 3D Gaussian Splatting
|
[
"Lintao Xiang",
"Xinkai Chen",
"Jianhuang Lai",
"Guangcong Wang"
] |
https://github.com/lt-xiang/Distilled-3DGS
|
3D Gaussian Splatting (3DGS) has exhibited remarkable efficacy in novel view synthesis (NVS). However, it suffers from a significant drawback: achieving high-fidelity rendering typically necessitates a large number of 3D Gaussians, resulting in substantial memory consumption and storage requirements. To address this challenge, we propose the first knowledge distillation framework for 3DGS, featuring various teacher models, including vanilla 3DGS, noise-augmented variants, and dropout-regularized versions. The outputs of these teachers are aggregated to guide the optimization of a lightweight student model. To distill the hidden geometric structure, we propose a structural similarity loss to boost the consistency of spatial geometric distributions between the student and teacher model. Through comprehensive quantitative and qualitative evaluations across diverse datasets, the proposed Distilled-3DGS, a simple yet effective framework without bells and whistles, achieves promising rendering results in both rendering quality and storage efficiency compared to state-of-the-art methods. Project page: https://distilled3dgs.github.io . Code: https://github.com/lt-xiang/Distilled-3DGS .
|
2025-08-26T00:00:00 |
2508.17580
|
UQ: Assessing Language Models on Unsolved Questions
|
[
"Fan Nie",
"Ken Ziyu Liu",
"Zihao Wang",
"Rui Sun",
"Wei Liu",
"Weijia Shi",
"Huaxiu Yao",
"Linjun Zhang",
"Andrew Y. Ng",
"James Zou",
"Sanmi Koyejo",
"Yejin Choi",
"Percy Liang",
"Niklas Muennighoff"
] |
Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usage. Yet, current paradigms face a difficulty-realism tension: exam-style benchmarks are often made artificially difficult with limited real-world value, while benchmarks based on real user interaction often skew toward easy, high-frequency problems. In this work, we explore a radically different paradigm: assessing models on unsolved questions. Rather than a static benchmark scored once, we curate unsolved questions and evaluate models asynchronously over time with validator-assisted screening and community verification. We introduce UQ, a testbed of 500 challenging, diverse questions sourced from Stack Exchange, spanning topics from CS theory and math to sci-fi and history, probing capabilities including reasoning, factuality, and browsing. UQ is difficult and realistic by construction: unsolved questions are often hard and naturally arise when humans seek answers, thus solving them yields direct real-world value. Our contributions are threefold: (1) UQ-Dataset and its collection pipeline combining rule-based filters, LLM judges, and human review to ensure question quality (e.g., well-defined and difficult); (2) UQ-Validators, compound validation strategies that leverage the generator-validator gap to provide evaluation signals and pre-screen candidate solutions for human review; and (3) UQ-Platform, an open platform where experts collectively verify questions and solutions. The top model passes UQ-validation on only 15% of questions, and preliminary human verification has already identified correct answers among those that passed. UQ charts a path for evaluating frontier models on real-world, open-ended challenges, where success pushes the frontier of human knowledge. We release UQ at https://uq.stanford.edu.
|
|
2025-08-26T00:00:00 |
2508.17188
|
PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLMs
|
[
"Zhilin Zhang",
"Xiang Zhang",
"Jiaqi Wei",
"Yiwei Xu",
"Chenyu You"
] |
Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In this work, we apply this paradigm to the paper-to-poster generation problem, a practical yet time-consuming process faced by researchers preparing for conferences. While recent approaches have attempted to automate this task, most neglect core design and aesthetic principles, resulting in posters that require substantial manual refinement. To address these design limitations, we propose PosterGen, a multi-agent framework that mirrors the workflow of professional poster designers. It consists of four collaborative specialized agents: (1) Parser and Curator agents extract content from the paper and organize storyboard; (2) Layout agent maps the content into a coherent spatial layout; (3) Stylist agents apply visual design elements such as color and typography; and (4) Renderer composes the final poster. Together, these agents produce posters that are both semantically grounded and visually appealing. To evaluate design quality, we introduce a vision-language model (VLM)-based rubric that measures layout balance, readability, and aesthetic coherence. Experimental results show that PosterGen consistently matches in content fidelity, and significantly outperforms existing methods in visual designs, generating posters that are presentation-ready with minimal human refinements.
|
|
2025-08-26T00:00:00 |
2508.18265
|
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
|
[
"Weiyun Wang",
"Zhangwei Gao",
"Lixin Gu",
"Hengjun Pu",
"Long Cui",
"Xingguang Wei",
"Zhaoyang Liu",
"Linglin Jing",
"Shenglong Ye",
"Jie Shao",
"Zhaokai Wang",
"Zhe Chen",
"Hongjie Zhang",
"Ganlin Yang",
"Haomin Wang",
"Qi Wei",
"Jinhui Yin",
"Wenhao Li",
"Erfei Cui",
"Guanzhou Chen",
"Zichen Ding",
"Changyao Tian",
"Zhenyu Wu",
"Jingjing Xie",
"Zehao Li",
"Bowen Yang",
"Yuchen Duan",
"Xuehui Wang",
"Songze Li",
"Xiangyu Zhao",
"Haodong Duan",
"Nianchen Deng",
"Bin Fu",
"Yinan He",
"Yi Wang",
"Conghui He",
"Botian Shi",
"Junjun He",
"Yingtong Xiong",
"Han Lv",
"Lijun Wu",
"Wenqi Shao",
"Kaipeng Zhang",
"Huipeng Deng",
"Biqing Qi",
"Jiaye Ge",
"Qipeng Guo",
"Wenwei Zhang",
"Wanli Ouyang",
"Limin Wang",
"Min Dou",
"Xizhou Zhu",
"Tong Lu",
"Dahua Lin",
"Jifeng Dai",
"Bowen Zhou",
"Weijie Su",
"Kai Chen",
"Yu Qiao",
"Wenhai Wang",
"Gen Luo"
] |
We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0\% gain in overall reasoning performance and a 4.05times inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks -- narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.
|
|
2025-08-26T00:00:00 |
2508.16949
|
Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning
|
[
"Yang Zhou",
"Sunzhu Li",
"Shunyu Liu",
"Wenkai Fang",
"Jiale Zhao",
"Jingwen Yang",
"Jianwei Lv",
"Kongcheng Zhang",
"Yihe Zhou",
"Hengtong Lu",
"Wei Chen",
"Yan Xie",
"Mingli Song"
] |
Recent advances in Large Language Models (LLMs) have underscored the potential of Reinforcement Learning (RL) to facilitate the emergence of reasoning capabilities. Despite the encouraging results, a fundamental dilemma persists as RL improvement relies on learning from high-quality samples, yet the exploration for such samples remains bounded by the inherent limitations of LLMs. This, in effect, creates an undesirable cycle in which what cannot be explored cannot be learned. In this work, we propose Rubric-Scaffolded Reinforcement Learning (RuscaRL), a novel instructional scaffolding framework designed to break the exploration bottleneck for general LLM reasoning. Specifically, RuscaRL introduces checklist-style rubrics as (1) explicit scaffolding for exploration during rollout generation, where different rubrics are provided as external guidance within task instructions to steer diverse high-quality responses. This guidance is gradually decayed over time, encouraging the model to internalize the underlying reasoning patterns; (2) verifiable rewards for exploitation during model training, where we can obtain robust LLM-as-a-Judge scores using rubrics as references, enabling effective RL on general reasoning tasks. Extensive experiments demonstrate the superiority of the proposed RuscaRL across various benchmarks, effectively expanding reasoning boundaries under the best-of-N evaluation. Notably, RuscaRL significantly boosts Qwen-2.5-7B-Instruct from 23.6 to 50.3 on HealthBench-500, surpassing GPT-4.1. Furthermore, our fine-tuned variant on Qwen3-30B-A3B-Instruct achieves 61.1 on HealthBench-500, outperforming leading LLMs including OpenAI-o3.
|
|
2025-08-26T00:00:00 |
2508.18032
|
Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation
|
[
"Yaqi Li",
"Peng Chen",
"Mingyang Han",
"Bu Pi",
"Haoxiang Shi",
"Runzhou Zhao",
"Yang Yao",
"Xuan Zhang",
"Jun Song"
] |
Despite the promising progress of recent autoregressive models in text-to-image (T2I) generation, their ability to handle multi-attribute and ambiguous prompts remains limited. To address these limitations, existing works have applied chain-of-thought (CoT) to enable stage-aware visual synthesis and employed reinforcement learning (RL) to improve reasoning capabilities. However, most models provide reward signals only at the end of the generation stage. This monolithic final-only guidance makes it difficult to identify which stages contribute positively to the final outcome and may lead to suboptimal policies. To tackle this issue, we propose a Visual-Chain of Guidance (Visual-CoG) paradigm consisting of three stages: semantic reasoning, process refining, and outcome evaluation, with stage-aware rewards providing immediate guidance throughout the image generation pipeline. We further construct a visual cognition benchmark, VisCog-Bench, which comprises four subtasks to evaluate the effectiveness of semantic reasoning. Comprehensive evaluations on GenEval, T2I-CompBench, and the proposed VisCog-Bench show improvements of 15%, 5%, and 19%, respectively, demonstrating the superior performance of the proposed Visual-CoG. We will release all the resources soon.
|
|
2025-08-26T00:00:00 |
2508.18190
|
ST-Raptor: LLM-Powered Semi-Structured Table Question Answering
|
[
"Zirui Tang",
"Boyu Niu",
"Xuanhe Zhou",
"Boxiu Li",
"Wei Zhou",
"Jiannan Wang",
"Guoliang Li",
"Xinyi Zhang",
"Fan Wu"
] |
https://github.com/weAIDB/ST-Raptor
|
Semi-structured tables, widely used in real-world applications (e.g., financial reports, medical records, transactional orders), often involve flexible and complex layouts (e.g., hierarchical headers and merged cells). These tables generally rely on human analysts to interpret table layouts and answer relevant natural language questions, which is costly and inefficient. To automate the procedure, existing methods face significant challenges. First, methods like NL2SQL require converting semi-structured tables into structured ones, which often causes substantial information loss. Second, methods like NL2Code and multi-modal LLM QA struggle to understand the complex layouts of semi-structured tables and cannot accurately answer corresponding questions. To this end, we propose ST-Raptor, a tree-based framework for semi-structured table question answering using large language models. First, we introduce the Hierarchical Orthogonal Tree (HO-Tree), a structural model that captures complex semi-structured table layouts, along with an effective algorithm for constructing the tree. Second, we define a set of basic tree operations to guide LLMs in executing common QA tasks. Given a user question, ST-Raptor decomposes it into simpler sub-questions, generates corresponding tree operation pipelines, and conducts operation-table alignment for accurate pipeline execution. Third, we incorporate a two-stage verification mechanism: forward validation checks the correctness of execution steps, while backward validation evaluates answer reliability by reconstructing queries from predicted answers. To benchmark the performance, we present SSTQA, a dataset of 764 questions over 102 real-world semi-structured tables. Experiments show that ST-Raptor outperforms nine baselines by up to 20% in answer accuracy. The code is available at https://github.com/weAIDB/ST-Raptor.
|
2025-08-26T00:00:00 |
2508.18159
|
SpotEdit: Evaluating Visually-Guided Image Editing Methods
|
[
"Sara Ghazanfari",
"Wei-An Lin",
"Haitong Tian",
"Ersin Yumer"
] |
https://github.com/SaraGhazanfari/SpotEdit
|
Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content generation. Although recent generative models have shown remarkable capabilities, existing evaluations remain simple and insufficiently representative of real-world editing challenges. We present SpotEdit, a comprehensive benchmark designed to systematically assess visually-guided image editing methods across diverse diffusion, autoregressive, and hybrid generative models, uncovering substantial performance disparities. To address a critical yet underexplored challenge, our benchmark includes a dedicated component on hallucination, highlighting how leading models, such as GPT-4o, often hallucinate the existence of a visual cue and erroneously perform the editing task. Our code and benchmark are publicly released at https://github.com/SaraGhazanfari/SpotEdit.
|
2025-08-26T00:00:00 |
2508.18076
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Neither Valid nor Reliable? Investigating the Use of LLMs as Judges
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[
"Khaoula Chehbouni",
"Mohammed Haddou",
"Jackie Chi Kit Cheung",
"Golnoosh Farnadi"
] |
Evaluating natural language generation (NLG) systems remains a core challenge of natural language processing (NLP), further complicated by the rise of large language models (LLMs) that aims to be general-purpose. Recently, large language models as judges (LLJs) have emerged as a promising alternative to traditional metrics, but their validity remains underexplored. This position paper argues that the current enthusiasm around LLJs may be premature, as their adoption has outpaced rigorous scrutiny of their reliability and validity as evaluators. Drawing on measurement theory from the social sciences, we identify and critically assess four core assumptions underlying the use of LLJs: their ability to act as proxies for human judgment, their capabilities as evaluators, their scalability, and their cost-effectiveness. We examine how each of these assumptions may be challenged by the inherent limitations of LLMs, LLJs, or current practices in NLG evaluation. To ground our analysis, we explore three applications of LLJs: text summarization, data annotation, and safety alignment. Finally, we highlight the need for more responsible evaluation practices in LLJs evaluation, to ensure that their growing role in the field supports, rather than undermines, progress in NLG.
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2025-08-26T00:00:00 |
2508.16790
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TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling
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[
"Yuancheng Wang",
"Dekun Chen",
"Xueyao Zhang",
"Junan Zhang",
"Jiaqi Li",
"Zhizheng Wu"
] |
Speech tokenizers serve as foundational components for speech language models, yet current designs exhibit several limitations, including: 1) dependence on multi-layer residual vector quantization structures or high frame rates, 2) reliance on auxiliary pre-trained models for semantic distillation, and 3) requirements for complex two-stage training processes. In this work, we introduce the Text-aware Diffusion Transformer Speech Codec (TaDiCodec), a novel approach designed to overcome these challenges. TaDiCodec employs end-to-end optimization for quantization and reconstruction through a diffusion autoencoder, while integrating text guidance into the diffusion decoder to enhance reconstruction quality and achieve optimal compression. TaDiCodec achieves an extremely low frame rate of 6.25 Hz and a corresponding bitrate of 0.0875 kbps with a single-layer codebook for 24 kHz speech, while maintaining superior performance on critical speech generation evaluation metrics such as Word Error Rate (WER), speaker similarity (SIM), and speech quality (UTMOS). Notably, TaDiCodec employs a single-stage, end-to-end training paradigm, and obviating the need for auxiliary pre-trained models. We also validate the compatibility of TaDiCodec in language model based zero-shot text-to-speech with both autoregressive modeling and masked generative modeling, demonstrating its effectiveness and efficiency for speech language modeling, as well as a significantly small reconstruction-generation gap. We will open source our code and model checkpoints. Audio samples are are available at https:/tadicodec.github.io/. We release code and model checkpoints at https:/github.com/HeCheng0625/Diffusion-Speech-Tokenizer.
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2025-08-26T00:00:00 |
2508.17472
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T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation
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[
"Kaiyue Sun",
"Rongyao Fang",
"Chengqi Duan",
"Xian Liu",
"Xihui Liu"
] |
We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of text-to-image (T2I) models. It consists of four dimensions: Idiom Interpretation, Textual Image Design, Entity-Reasoning and Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the reasoning accuracy and image quality. We benchmark various T2I generation models, and provide comprehensive analysis on their performances.
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2025-08-26T00:00:00 |
2508.17298
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Explain Before You Answer: A Survey on Compositional Visual Reasoning
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[
"Fucai Ke",
"Joy Hsu",
"Zhixi Cai",
"Zixian Ma",
"Xin Zheng",
"Xindi Wu",
"Sukai Huang",
"Weiqing Wang",
"Pari Delir Haghighi",
"Gholamreza Haffari",
"Ranjay Krishna",
"Jiajun Wu",
"Hamid Rezatofighi"
] |
Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference. While early surveys focus on monolithic vision-language models or general multimodal reasoning, a dedicated synthesis of the rapidly expanding compositional visual reasoning literature is still missing. We fill this gap with a comprehensive survey spanning 2023 to 2025 that systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We first formalize core definitions and describe why compositional approaches offer advantages in cognitive alignment, semantic fidelity, robustness, interpretability, and data efficiency. Next, we trace a five-stage paradigm shift: from prompt-enhanced language-centric pipelines, through tool-enhanced LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and unified agentic VLMs, highlighting their architectural designs, strengths, and limitations. We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception. Drawing on these analyses, we distill key insights, identify open challenges (e.g., limitations of LLM-based reasoning, hallucination, a bias toward deductive reasoning, scalable supervision, tool integration, and benchmark limitations), and outline future directions, including world-model integration, human-AI collaborative reasoning, and richer evaluation protocols. By offering a unified taxonomy, historical roadmap, and critical outlook, this survey aims to serve as a foundational reference and inspire the next generation of compositional visual reasoning research.
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2025-08-26T00:00:00 |
2508.16577
|
MV-RAG: Retrieval Augmented Multiview Diffusion
|
[
"Yosef Dayani",
"Omer Benishu",
"Sagie Benaim"
] |
Text-to-3D generation approaches have advanced significantly by leveraging pretrained 2D diffusion priors, producing high-quality and 3D-consistent outputs. However, they often fail to produce out-of-domain (OOD) or rare concepts, yielding inconsistent or inaccurate results. To this end, we propose MV-RAG, a novel text-to-3D pipeline that first retrieves relevant 2D images from a large in-the-wild 2D database and then conditions a multiview diffusion model on these images to synthesize consistent and accurate multiview outputs. Training such a retrieval-conditioned model is achieved via a novel hybrid strategy bridging structured multiview data and diverse 2D image collections. This involves training on multiview data using augmented conditioning views that simulate retrieval variance for view-specific reconstruction, alongside training on sets of retrieved real-world 2D images using a distinctive held-out view prediction objective: the model predicts the held-out view from the other views to infer 3D consistency from 2D data. To facilitate a rigorous OOD evaluation, we introduce a new collection of challenging OOD prompts. Experiments against state-of-the-art text-to-3D, image-to-3D, and personalization baselines show that our approach significantly improves 3D consistency, photorealism, and text adherence for OOD/rare concepts, while maintaining competitive performance on standard benchmarks.
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2025-08-26T00:00:00 |
2508.17290
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MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment
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[
"Omid Ghahroodi",
"Arshia Hemmat",
"Marzia Nouri",
"Seyed Mohammad Hadi Hosseini",
"Doratossadat Dastgheib",
"Mohammad Vali Sanian",
"Alireza Sahebi",
"Reihaneh Zohrabi",
"Mohammad Hossein Rohban",
"Ehsaneddin Asgari",
"Mahdieh Soleymani Baghshah"
] |
Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEENA (also known as PersianMMMU), the first dataset designed to evaluate Persian VLMs across scientific, reasoning, and human-level understanding tasks. Our dataset comprises approximately 7,500 Persian and 3,000 English questions, covering a wide range of topics such as reasoning, mathematics, physics, diagrams, charts, and Persian art and literature. Key features of MEENA include: (1) diverse subject coverage spanning various educational levels, from primary to upper secondary school, (2) rich metadata, including difficulty levels and descriptive answers, (3) original Persian data that preserves cultural nuances, (4) a bilingual structure to assess cross-linguistic performance, and (5) a series of diverse experiments assessing various capabilities, including overall performance, the model's ability to attend to images, and its tendency to generate hallucinations. We hope this benchmark contributes to enhancing VLM capabilities beyond English.
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2025-08-26T00:00:00 |
2508.16745
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Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling
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[
"Ivan Rodkin",
"Daniil Orel",
"Konstantin Smirnov",
"Arman Bolatov",
"Bilal Elbouardi",
"Besher Hassan",
"Yuri Kuratov",
"Aydar Bulatov",
"Preslav Nakov",
"Timothy Baldwin",
"Artem Shelmanov",
"Mikhail Burtsev"
] |
Reasoning is a core capability of large language models, yet understanding how they learn and perform multi-step reasoning remains an open problem. In this study, we explore how different architectures and training methods affect model multi-step reasoning capabilities within a cellular automata framework. By training on state sequences generated with random Boolean functions for random initial conditions to exclude memorization, we demonstrate that most neural architectures learn to abstract the underlying rules. While models achieve high accuracy in next-state prediction, their performance declines sharply if multi-step reasoning is required. We confirm that increasing model depth plays a crucial role for sequential computations. We demonstrate that an extension of the effective model depth with recurrence, memory, and test-time compute scaling substantially enhances reasoning capabilities.
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2025-08-26T00:00:00 |
2508.17973
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German4All - A Dataset and Model for Readability-Controlled Paraphrasing in German
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[
"Miriam Anschütz",
"Thanh Mai Pham",
"Eslam Nasrallah",
"Maximilian Müller",
"Cristian-George Craciun",
"Georg Groh"
] |
The ability to paraphrase texts across different complexity levels is essential for creating accessible texts that can be tailored toward diverse reader groups. Thus, we introduce German4All, the first large-scale German dataset of aligned readability-controlled, paragraph-level paraphrases. It spans five readability levels and comprises over 25,000 samples. The dataset is automatically synthesized using GPT-4 and rigorously evaluated through both human and LLM-based judgments. Using German4All, we train an open-source, readability-controlled paraphrasing model that achieves state-of-the-art performance in German text simplification, enabling more nuanced and reader-specific adaptations. We opensource both the dataset and the model to encourage further research on multi-level paraphrasing
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2025-08-26T00:00:00 |
2508.17821
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Limitations of Normalization in Attention Mechanism
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[
"Timur Mudarisov",
"Mikhail Burtsev",
"Tatiana Petrova",
"Radu State"
] |
This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the identification of the model's selective ability and the geometric separation involved in token selection. Our analysis includes explicit bounds on distances and separation criteria for token vectors under softmax scaling. Through experiments with pre-trained GPT-2 model, we empirically validate our theoretical results and analyze key behaviors of the attention mechanism. Notably, we demonstrate that as the number of selected tokens increases, the model's ability to distinguish informative tokens declines, often converging toward a uniform selection pattern. We also show that gradient sensitivity under softmax normalization presents challenges during training, especially at low temperature settings. These findings advance current understanding of softmax-based attention mechanism and motivate the need for more robust normalization and selection strategies in future attention architectures.
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2025-08-26T00:00:00 |
2508.17811
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MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting
|
[
"Hanzhi Chang",
"Ruijie Zhu",
"Wenjie Chang",
"Mulin Yu",
"Yanzhe Liang",
"Jiahao Lu",
"Zhuoyuan Li",
"Tianzhu Zhang"
] |
Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks. Project Page: https://hanzhichang.github.io/meshsplat_web
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2025-08-26T00:00:00 |
2508.17061
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REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework
|
[
"Stefanos Pasios",
"Nikos Nikolaidis"
] |
https://github.com/stefanos50/REGEN
|
Photorealism is an important aspect of modern video games since it can shape the player experience and simultaneously impact the immersion, narrative engagement, and visual fidelity. Although recent hardware technological breakthroughs, along with state-of-the-art rendering technologies, have significantly improved the visual realism of video games, achieving true photorealism in dynamic environments at real-time frame rates still remains a major challenge due to the tradeoff between visual quality and performance. In this short paper, we present a novel approach for enhancing the photorealism of rendered game frames using generative adversarial networks. To this end, we propose Real-time photorealism Enhancement in Games via a dual-stage gEnerative Network framework (REGEN), which employs a robust unpaired image-to-image translation model to produce semantically consistent photorealistic frames that transform the problem into a simpler paired image-to-image translation task. This enables training with a lightweight method that can achieve real-time inference time without compromising visual quality. We demonstrate the effectiveness of our framework on Grand Theft Auto V, showing that the approach achieves visual results comparable to the ones produced by the robust unpaired Im2Im method while improving inference speed by 32.14 times. Our findings also indicate that the results outperform the photorealism-enhanced frames produced by directly training a lightweight unpaired Im2Im translation method to translate the video game frames towards the visual characteristics of real-world images. Code, pre-trained models, and demos for this work are available at: https://github.com/stefanos50/REGEN.
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Subsets and Splits
Top 20 Papers with GitHub Links
This query retrieves 20 entries from the training set where the GitHub URL is not null or empty, providing a basic filter of non-empty entries.