date
timestamp[ns]date 2023-05-05 00:00:00
2025-06-25 00:00:00
| arxiv_id
stringlengths 10
10
| title
stringlengths 8
202
| authors
sequencelengths 1
942
| github
stringlengths 0
116
| abstract
stringlengths 165
1.92k
|
|---|---|---|---|---|---|
2025-06-12T00:00:00 | 2506.09113 | Seedance 1.0: Exploring the Boundaries of Video Generation Models | [
"Yu Gao",
"Haoyuan Guo",
"Tuyen Hoang",
"Weilin Huang",
"Lu Jiang",
"Fangyuan Kong",
"Huixia Li",
"Jiashi Li",
"Liang Li",
"Xiaojie Li",
"Xunsong Li",
"Yifu Li",
"Shanchuan Lin",
"Zhijie Lin",
"Jiawei Liu",
"Shu Liu",
"Xiaonan Nie",
"Zhiwu Qing",
"Yuxi Ren",
"Li Sun",
"Zhi Tian",
"Rui Wang",
"Sen Wang",
"Guoqiang Wei",
"Guohong Wu",
"Jie Wu",
"Ruiqi Xia",
"Fei Xiao",
"Xuefeng Xiao",
"Jiangqiao Yan",
"Ceyuan Yang",
"Jianchao Yang",
"Runkai Yang",
"Tao Yang",
"Yihang Yang",
"Zilyu Ye",
"Xuejiao Zeng",
"Yan Zeng",
"Heng Zhang",
"Yang Zhao",
"Xiaozheng Zheng",
"Peihao Zhu",
"Jiaxin Zou",
"Feilong Zuo"
] | Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously balancing prompt following, motion plausibility, and visual quality. In this report, we introduce Seedance 1.0, a high-performance and inference-efficient video foundation generation model that integrates several core technical improvements: (i) multi-source data curation augmented with precision and meaningful video captioning, enabling comprehensive learning across diverse scenarios; (ii) an efficient architecture design with proposed training paradigm, which allows for natively supporting multi-shot generation and jointly learning of both text-to-video and image-to-video tasks. (iii) carefully-optimized post-training approaches leveraging fine-grained supervised fine-tuning, and video-specific RLHF with multi-dimensional reward mechanisms for comprehensive performance improvements; (iv) excellent model acceleration achieving ~10x inference speedup through multi-stage distillation strategies and system-level optimizations. Seedance 1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds (NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation. |
|
2025-06-12T00:00:00 | 2506.08889 | SeerAttention-R: Sparse Attention Adaptation for Long Reasoning | [
"Yizhao Gao",
"Shuming Guo",
"Shijie Cao",
"Yuqing Xia",
"Yu Cheng",
"Lei Wang",
"Lingxiao Ma",
"Yutao Sun",
"Tianzhu Ye",
"Li Dong",
"Hayden Kwok-Hay So",
"Yu Hua",
"Ting Cao",
"Fan Yang",
"Mao Yang"
] | https://github.com/microsoft/SeerAttention | We introduce SeerAttention-R, a sparse attention framework specifically tailored for the long decoding of reasoning models. Extended from SeerAttention, SeerAttention-R retains the design of learning attention sparsity through a self-distilled gating mechanism, while removing query pooling to accommodate auto-regressive decoding. With a lightweight plug-in gating, SeerAttention-R is flexible and can be easily integrated into existing pretrained model without modifying the original parameters. We demonstrate that SeerAttention-R, trained on just 0.4B tokens, maintains near-lossless reasoning accuracy with 4K token budget in AIME benchmark under large sparse attention block sizes (64/128). Using TileLang, we develop a highly optimized sparse decoding kernel that achieves near-theoretical speedups of up to 9x over FlashAttention-3 on H100 GPU at 90% sparsity. Code is available at: https://github.com/microsoft/SeerAttention. |
2025-06-12T00:00:00 | 2506.09937 | SAFE: Multitask Failure Detection for Vision-Language-Action Models | [
"Qiao Gu",
"Yuanliang Ju",
"Shengxiang Sun",
"Igor Gilitschenski",
"Haruki Nishimura",
"Masha Itkina",
"Florian Shkurti"
] | While vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks, they achieve limited success rates when deployed on novel tasks out-of-the-box. To allow these policies to safely interact with their environments, we need a failure detector that gives a timely alert such that the robot can stop, backtrack, or ask for help. However, existing failure detectors are trained and tested only on one or a few specific tasks, while VLAs require the detector to generalize and detect failures also in unseen tasks and novel environments. In this paper, we introduce the multitask failure detection problem and propose SAFE, a failure detector for generalist robot policies such as VLAs. We analyze the VLA feature space and find that VLAs have sufficient high-level knowledge about task success and failure, which is generic across different tasks. Based on this insight, we design SAFE to learn from VLA internal features and predict a single scalar indicating the likelihood of task failure. SAFE is trained on both successful and failed rollouts, and is evaluated on unseen tasks. SAFE is compatible with different policy architectures. We test it on OpenVLA, pi_0, and pi_0-FAST in both simulated and real-world environments extensively. We compare SAFE with diverse baselines and show that SAFE achieves state-of-the-art failure detection performance and the best trade-off between accuracy and detection time using conformal prediction. More qualitative results can be found at https://vla-safe.github.io/. |
|
2025-06-12T00:00:00 | 2506.09984 | InterActHuman: Multi-Concept Human Animation with Layout-Aligned Audio Conditions | [
"Zhenzhi Wang",
"Jiaqi Yang",
"Jianwen Jiang",
"Chao Liang",
"Gaojie Lin",
"Zerong Zheng",
"Ceyuan Yang",
"Dahua Lin"
] | End-to-end human animation with rich multi-modal conditions, e.g., text, image and audio has achieved remarkable advancements in recent years. However, most existing methods could only animate a single subject and inject conditions in a global manner, ignoring scenarios that multiple concepts could appears in the same video with rich human-human interactions and human-object interactions. Such global assumption prevents precise and per-identity control of multiple concepts including humans and objects, therefore hinders applications. In this work, we discard the single-entity assumption and introduce a novel framework that enforces strong, region-specific binding of conditions from modalities to each identity's spatiotemporal footprint. Given reference images of multiple concepts, our method could automatically infer layout information by leveraging a mask predictor to match appearance cues between the denoised video and each reference appearance. Furthermore, we inject local audio condition into its corresponding region to ensure layout-aligned modality matching in a iterative manner. This design enables the high-quality generation of controllable multi-concept human-centric videos. Empirical results and ablation studies validate the effectiveness of our explicit layout control for multi-modal conditions compared to implicit counterparts and other existing methods. |
|
2025-06-12T00:00:00 | 2506.09007 | Branched Schrödinger Bridge Matching | [
"Sophia Tang",
"Yinuo Zhang",
"Alexander Tong",
"Pranam Chatterjee"
] | Predicting the intermediate trajectories between an initial and target distribution is a central problem in generative modeling. Existing approaches, such as flow matching and Schr\"odinger Bridge Matching, effectively learn mappings between two distributions by modeling a single stochastic path. However, these methods are inherently limited to unimodal transitions and cannot capture branched or divergent evolution from a common origin to multiple distinct outcomes. To address this, we introduce Branched Schr\"odinger Bridge Matching (BranchSBM), a novel framework that learns branched Schr\"odinger bridges. BranchSBM parameterizes multiple time-dependent velocity fields and growth processes, enabling the representation of population-level divergence into multiple terminal distributions. We show that BranchSBM is not only more expressive but also essential for tasks involving multi-path surface navigation, modeling cell fate bifurcations from homogeneous progenitor states, and simulating diverging cellular responses to perturbations. |
|
2025-06-12T00:00:00 | 2506.08001 | Reparameterized LLM Training via Orthogonal Equivalence Transformation | [
"Zeju Qiu",
"Simon Buchholz",
"Tim Z. Xiao",
"Maximilian Dax",
"Bernhard Schölkopf",
"Weiyang Liu"
] | While large language models (LLMs) are driving the rapid advancement of artificial intelligence, effectively and reliably training these large models remains one of the field's most significant challenges. To address this challenge, we propose POET, a novel reParameterized training algorithm that uses Orthogonal Equivalence Transformation to optimize neurons. Specifically, POET reparameterizes each neuron with two learnable orthogonal matrices and a fixed random weight matrix. Because of its provable preservation of spectral properties of weight matrices, POET can stably optimize the objective function with improved generalization. We further develop efficient approximations that make POET flexible and scalable for training large-scale neural networks. Extensive experiments validate the effectiveness and scalability of POET in training LLMs. |
|
2025-06-12T00:00:00 | 2506.09350 | Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation | [
"Shanchuan Lin",
"Ceyuan Yang",
"Hao He",
"Jianwen Jiang",
"Yuxi Ren",
"Xin Xia",
"Yang Zhao",
"Xuefeng Xiao",
"Lu Jiang"
] | Existing large-scale video generation models are computationally intensive, preventing adoption in real-time and interactive applications. In this work, we propose autoregressive adversarial post-training (AAPT) to transform a pre-trained latent video diffusion model into a real-time, interactive video generator. Our model autoregressively generates a latent frame at a time using a single neural function evaluation (1NFE). The model can stream the result to the user in real time and receive interactive responses as controls to generate the next latent frame. Unlike existing approaches, our method explores adversarial training as an effective paradigm for autoregressive generation. This not only allows us to design an architecture that is more efficient for one-step generation while fully utilizing the KV cache, but also enables training the model in a student-forcing manner that proves to be effective in reducing error accumulation during long video generation. Our experiments demonstrate that our 8B model achieves real-time, 24fps, streaming video generation at 736x416 resolution on a single H100, or 1280x720 on 8xH100 up to a minute long (1440 frames). Visit our research website at https://seaweed-apt.com/2 |
|
2025-06-12T00:00:00 | 2506.06395 | Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models | [
"Pengyi Li",
"Matvey Skripkin",
"Alexander Zubrey",
"Andrey Kuznetsov",
"Ivan Oseledets"
] | Large language models (LLMs) excel at reasoning, yet post-training remains critical for aligning their behavior with task goals. Existing reinforcement learning (RL) methods often depend on costly human annotations or external reward models. We propose Reinforcement Learning via Self-Confidence (RLSC), which uses the model's own confidence as reward signals-eliminating the need for labels, preference models, or reward engineering. Applied to Qwen2.5-Math-7B with only 16 samples per question and 10 or 20 training steps, RLSC improves accuracy by +13.4% on AIME2024, +21.2% on MATH500, +21.7% on Minerva Math, +20.8% on Olympiadbench, and +9.7% on AMC23. RLSC provides a simple, scalable post-training method for inference models, requiring only a small number of samples and unlabelled supervision. |
|
2025-06-12T00:00:00 | 2506.08900 | MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis | [
"José Morano",
"Botond Fazekas",
"Emese Sükei",
"Ronald Fecso",
"Taha Emre",
"Markus Gumpinger",
"Georg Faustmann",
"Marzieh Oghbaie",
"Ursula Schmidt-Erfurth",
"Hrvoje Bogunović"
] | https://github.com/j-morano/MIRAGE | Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing AI models often requires extensive annotation, and existing models tend to underperform on independent, unseen data. Foundation models (FMs), large AI models trained on vast unlabeled datasets, have shown promise in overcoming these challenges. Nonetheless, available FMs for ophthalmology lack extensive validation, especially for segmentation tasks, and focus on a single imaging modality. In this context, we propose MIRAGE, a novel multimodal FM for the analysis of OCT and scanning laser ophthalmoscopy (SLO) images. Additionally, we propose a new evaluation benchmark with OCT/SLO classification and segmentation tasks. The comparison with general and specialized FMs and segmentation methods shows the superiority of MIRAGE in both types of tasks, highlighting its suitability as a basis for the development of robust AI systems for retinal OCT image analysis. Both MIRAGE and the evaluation benchmark are publicly available: https://github.com/j-morano/MIRAGE. |
2025-06-12T00:00:00 | 2506.08570 | Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation | [
"Or Tal",
"Felix Kreuk",
"Yossi Adi"
] | Recent progress in text-to-music generation has enabled models to synthesize high-quality musical segments, full compositions, and even respond to fine-grained control signals, e.g. chord progressions. State-of-the-art (SOTA) systems differ significantly across many dimensions, such as training datasets, modeling paradigms, and architectural choices. This diversity complicates efforts to evaluate models fairly and pinpoint which design choices most influence performance. While factors like data and architecture are important, in this study we focus exclusively on the modeling paradigm. We conduct a systematic empirical analysis to isolate its effects, offering insights into associated trade-offs and emergent behaviors that can guide future text-to-music generation systems. Specifically, we compare the two arguably most common modeling paradigms: Auto-Regressive decoding and Conditional Flow-Matching. We conduct a controlled comparison by training all models from scratch using identical datasets, training configurations, and similar backbone architectures. Performance is evaluated across multiple axes, including generation quality, robustness to inference configurations, scalability, adherence to both textual and temporally aligned conditioning, and editing capabilities in the form of audio inpainting. This comparative study sheds light on distinct strengths and limitations of each paradigm, providing actionable insights that can inform future architectural and training decisions in the evolving landscape of text-to-music generation. Audio sampled examples are available at: https://huggingface.co/spaces/ortal1602/ARvsFM |
|
2025-06-12T00:00:00 | 2506.05309 | Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games | [
"Niv Eckhaus",
"Uri Berger",
"Gabriel Stanovsky"
] | LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are inherently asynchronous. For example, in group chats, online team meetings, or social games, there is no inherent notion of turns; therefore, the decision of when to speak forms a crucial part of the participant's decision making. In this work, we develop an adaptive asynchronous LLM-agent which, in addition to determining what to say, also decides when to say it. To evaluate our agent, we collect a unique dataset of online Mafia games, including both human participants, as well as our asynchronous agent. Overall, our agent performs on par with human players, both in game performance, as well as in its ability to blend in with the other human players. Our analysis shows that the agent's behavior in deciding when to speak closely mirrors human patterns, although differences emerge in message content. We release all our data and code to support and encourage further research for more realistic asynchronous communication between LLM agents. This work paves the way for integration of LLMs into realistic human group settings, from assistance in team discussions to educational and professional environments where complex social dynamics must be navigated. |
|
2025-06-12T00:00:00 | 2506.09991 | Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation | [
"Xinyu Yang",
"Yuwei An",
"Hongyi Liu",
"Tianqi Chen",
"Beidi Chen"
] | Autoregressive Large Language Models (AR-LLMs) frequently exhibit implicit parallelism in sequential generation. Inspired by this, we introduce Multiverse, a new generative model that enables natively parallel generation. Multiverse internalizes a MapReduce paradigm, generating automatically through three stages: (i) a Map stage for adaptive task decomposition, (ii) a Process stage for parallel subtask execution, and (iii) a Reduce stage for lossless result synthesis. Next, we build a real-world Multiverse reasoning model with co-design of data, algorithm, and system, enabling rapid and seamless transfer from frontier AR-LLMs. Starting from sequential reasoning chains, we create Multiverse 1K by converting them into structured training data using an automated LLM-assisted pipeline, avoiding costly human annotations. Algorithmically, we design Multiverse Attention to separate parallel reasoning steps while keeping compatibility with causal attention for efficient training. Systematically, we implement Multiverse Engine to enable parallel inference. It features a dedicated scheduler that dynamically switches between sequential and parallel generation, triggered directly by the model. After a 3-hour fine-tuning with 1K examples, our Multiverse-32B stands as the only open-sourced non-AR model achieving performance on par with leading AR-LLMs of the same scale, evidenced by AIME24 & 25 scores of 54% and 46%, respectively. Moreover, our budget control experiments show that Multiverse-32B exhibits superior scaling, outperforming AR-LLMs by 1.87% on average using the same context length. Such scaling further leads to practical efficiency gain, achieving up to 2x speedup across varying batch sizes. We have open-sourced the entire Multiverse ecosystem, including data, model weights, engine, supporting tools, as well as complete data curation prompts and detailed training and evaluation recipes. |
|
2025-06-12T00:00:00 | 2506.09736 | Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math Reasoning | [
"Yuting Li",
"Lai Wei",
"Kaipeng Zheng",
"Jingyuan Huang",
"Linghe Kong",
"Lichao Sun",
"Weiran Huang"
] | https://github.com/YutingLi0606/Vision-Matters | Despite the rapid progress of multimodal large language models (MLLMs), they have largely overlooked the importance of visual processing. In a simple yet revealing experiment, we interestingly find that language-only models, when provided with image captions, can achieve comparable or even better performance than MLLMs that consume raw visual inputs. This suggests that current MLLMs may generate accurate visual descriptions but fail to effectively integrate them during reasoning. Motivated by this, we propose a simple visual perturbation framework that enhances perceptual robustness without requiring algorithmic modifications or additional training data. Our approach introduces three targeted perturbations: distractor concatenation, dominance-preserving mixup, and random rotation, that can be easily integrated into existing post-training pipelines including SFT, DPO, and GRPO. Through extensive experiments across multiple datasets, we demonstrate consistent improvements in mathematical reasoning performance, with gains comparable to those achieved through algorithmic changes. Additionally, we achieve competitive performance among open-source 7B RL-tuned models by training Qwen2.5-VL-7B with visual perturbation. Through comprehensive ablation studies, we analyze the effectiveness of different perturbation strategies, revealing that each perturbation type contributes uniquely to different aspects of visual reasoning. Our findings highlight the critical role of visual perturbation in multimodal mathematical reasoning: better reasoning begins with better seeing. Our code is available at https://github.com/YutingLi0606/Vision-Matters. |
2025-06-12T00:00:00 | 2506.09229 | Cross-Frame Representation Alignment for Fine-Tuning Video Diffusion Models | [
"Sungwon Hwang",
"Hyojin Jang",
"Kinam Kim",
"Minho Park",
"Jaegul choo"
] | Fine-tuning Video Diffusion Models (VDMs) at the user level to generate videos that reflect specific attributes of training data presents notable challenges, yet remains underexplored despite its practical importance. Meanwhile, recent work such as Representation Alignment (REPA) has shown promise in improving the convergence and quality of DiT-based image diffusion models by aligning, or assimilating, its internal hidden states with external pretrained visual features, suggesting its potential for VDM fine-tuning. In this work, we first propose a straightforward adaptation of REPA for VDMs and empirically show that, while effective for convergence, it is suboptimal in preserving semantic consistency across frames. To address this limitation, we introduce Cross-frame Representation Alignment (CREPA), a novel regularization technique that aligns hidden states of a frame with external features from neighboring frames. Empirical evaluations on large-scale VDMs, including CogVideoX-5B and Hunyuan Video, demonstrate that CREPA improves both visual fidelity and cross-frame semantic coherence when fine-tuned with parameter-efficient methods such as LoRA. We further validate CREPA across diverse datasets with varying attributes, confirming its broad applicability. Project page: https://crepavideo.github.io |
|
2025-06-12T00:00:00 | 2506.08008 | Hidden in plain sight: VLMs overlook their visual representations | [
"Stephanie Fu",
"Tyler Bonnen",
"Devin Guillory",
"Trevor Darrell"
] | Language provides a natural interface to specify and evaluate performance on visual tasks. To realize this possibility, vision language models (VLMs) must successfully integrate visual and linguistic information. Our work compares VLMs to a direct readout of their visual encoders to understand their ability to integrate across these modalities. Across a series of vision-centric benchmarks (e.g., depth estimation, correspondence), we find that VLMs perform substantially worse than their visual encoders, dropping to near-chance performance. We investigate these results through a series of analyses across the entire VLM: namely 1) the degradation of vision representations, 2) brittleness to task prompt, and 3) the language model's role in solving the task. We find that the bottleneck in performing these vision-centric tasks lies in this third category; VLMs are not effectively using visual information easily accessible throughout the entire model, and they inherit the language priors present in the LLM. Our work helps diagnose the failure modes of open-source VLMs, and presents a series of evaluations useful for future investigations into visual understanding within VLMs. |
|
2025-06-12T00:00:00 | 2506.09958 | Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy | [
"Sushant Gautam",
"Michael A. Riegler",
"Pål Halvorsen"
] | https://github.com/Simula/Kvasir-VQA-x1 | Medical Visual Question Answering (MedVQA) is a promising field for developing clinical decision support systems, yet progress is often limited by the available datasets, which can lack clinical complexity and visual diversity. To address these gaps, we introduce Kvasir-VQA-x1, a new, large-scale dataset for gastrointestinal (GI) endoscopy. Our work significantly expands upon the original Kvasir-VQA by incorporating 159,549 new question-answer pairs that are designed to test deeper clinical reasoning. We developed a systematic method using large language models to generate these questions, which are stratified by complexity to better assess a model's inference capabilities. To ensure our dataset prepares models for real-world clinical scenarios, we have also introduced a variety of visual augmentations that mimic common imaging artifacts. The dataset is structured to support two main evaluation tracks: one for standard VQA performance and another to test model robustness against these visual perturbations. By providing a more challenging and clinically relevant benchmark, Kvasir-VQA-x1 aims to accelerate the development of more reliable and effective multimodal AI systems for use in clinical settings. The dataset is fully accessible and adheres to FAIR data principles, making it a valuable resource for the wider research community. Code and data: https://github.com/Simula/Kvasir-VQA-x1 and https://huggingface.co/datasets/SimulaMet/Kvasir-VQA-x1 |
2025-06-12T00:00:00 | 2506.09669 | Query-Level Uncertainty in Large Language Models | [
"Lihu Chen",
"Gaël Varoquaux"
] | It is important for Large Language Models to be aware of the boundary of their knowledge, the mechanism of identifying known and unknown queries. This type of awareness can help models perform adaptive inference, such as invoking RAG, engaging in slow and deep thinking, or adopting the abstention mechanism, which is beneficial to the development of efficient and trustworthy AI. In this work, we propose a method to detect knowledge boundaries via Query-Level Uncertainty, which aims to determine if the model is able to address a given query without generating any tokens. To this end, we introduce a novel and training-free method called Internal Confidence, which leverages self-evaluations across layers and tokens. Empirical results on both factual QA and mathematical reasoning tasks demonstrate that our internal confidence can outperform several baselines. Furthermore, we showcase that our proposed method can be used for efficient RAG and model cascading, which is able to reduce inference costs while maintaining performance. |
|
2025-06-12T00:00:00 | 2506.09501 | Give Me FP32 or Give Me Death? Challenges and Solutions for Reproducible Reasoning | [
"Jiayi Yuan",
"Hao Li",
"Xinheng Ding",
"Wenya Xie",
"Yu-Jhe Li",
"Wentian Zhao",
"Kun Wan",
"Jing Shi",
"Xia Hu",
"Zirui Liu"
] | https://github.com/nanomaoli/llm_reproducibility | Large Language Models (LLMs) are now integral across various domains and have demonstrated impressive performance. Progress, however, rests on the premise that benchmark scores are both accurate and reproducible. We demonstrate that the reproducibility of LLM performance is fragile: changing system configuration such as evaluation batch size, GPU count, and GPU version can introduce significant difference in the generated responses. This issue is especially pronounced in reasoning models, where minor rounding differences in early tokens can cascade into divergent chains of thought, ultimately affecting accuracy. For instance, under bfloat16 precision with greedy decoding, a reasoning model like DeepSeek-R1-Distill-Qwen-7B can exhibit up to 9% variation in accuracy and 9,000 tokens difference in response length due to differences in GPU count, type, and evaluation batch size. We trace the root cause of this variability to the non-associative nature of floating-point arithmetic under limited numerical precision. This work presents the first systematic investigation into how numerical precision affects reproducibility in LLM inference. Through carefully controlled experiments across various hardware, software, and precision settings, we quantify when and how model outputs diverge. Our analysis reveals that floating-point precision -- while critical for reproducibility -- is often neglected in evaluation practices. Inspired by this, we develop a lightweight inference pipeline, dubbed LayerCast, that stores weights in 16-bit precision but performs all computations in FP32, balancing memory efficiency with numerical stability. Code is available at https://github.com/nanomaoli/llm_reproducibility. |
2025-06-12T00:00:00 | 2506.09278 | UFM: A Simple Path towards Unified Dense Correspondence with Flow | [
"Yuchen Zhang",
"Nikhil Keetha",
"Chenwei Lyu",
"Bhuvan Jhamb",
"Yutian Chen",
"Yuheng Qiu",
"Jay Karhade",
"Shreyas Jha",
"Yaoyu Hu",
"Deva Ramanan",
"Sebastian Scherer",
"Wenshan Wang"
] | Dense image correspondence is central to many applications, such as visual odometry, 3D reconstruction, object association, and re-identification. Historically, dense correspondence has been tackled separately for wide-baseline scenarios and optical flow estimation, despite the common goal of matching content between two images. In this paper, we develop a Unified Flow & Matching model (UFM), which is trained on unified data for pixels that are co-visible in both source and target images. UFM uses a simple, generic transformer architecture that directly regresses the (u,v) flow. It is easier to train and more accurate for large flows compared to the typical coarse-to-fine cost volumes in prior work. UFM is 28% more accurate than state-of-the-art flow methods (Unimatch), while also having 62% less error and 6.7x faster than dense wide-baseline matchers (RoMa). UFM is the first to demonstrate that unified training can outperform specialized approaches across both domains. This result enables fast, general-purpose correspondence and opens new directions for multi-modal, long-range, and real-time correspondence tasks. |
|
2025-06-12T00:00:00 | 2506.06020 | When to Trust Context: Self-Reflective Debates for Context Reliability | [
"Zeqi Zhou",
"Fang Wu",
"Shayan Talaei",
"Haokai Zhao",
"Cheng Meixin",
"Tinson Xu",
"Amin Saberi",
"Yejin Choi"
] | https://github.com/smiles724/Self-Reflective-Debates | Large language models frequently encounter conflicts between their parametric knowledge and contextual input, often resulting in factual inconsistencies or hallucinations. We propose Self-Reflective Debate for Contextual Reliability (SR-DCR), a lightweight framework that integrates token-level self-confidence with an asymmetric multi-agent debate to adjudicate such conflicts. A critic, deprived of context, challenges a defender who argues from the given passage; a judge model evaluates the debate and determines the context's reliability. The final answer is selected by combining the verdict with model confidence. Experiments on the ClashEval benchmark demonstrate that SR-DCR consistently enhances robustness to misleading context while maintaining accuracy on trustworthy inputs, outperforming both classical debate and confidence-only baselines with minimal computational overhead. The code is available at https://github.com/smiles724/Self-Reflective-Debates. |
2025-06-12T00:00:00 | 2506.09420 | A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy | [
"Henry Peng Zou",
"Wei-Chieh Huang",
"Yaozu Wu",
"Chunyu Miao",
"Dongyuan Li",
"Aiwei Liu",
"Yue Zhou",
"Yankai Chen",
"Weizhi Zhang",
"Yangning Li",
"Liancheng Fang",
"Renhe Jiang",
"Philip S. Yu"
] | Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach is the right path forward, as these autonomous systems still have problems with reliability, transparency, and understanding the actual requirements of human. We suggest a different approach: LLM-based Human-Agent Systems (LLM-HAS), where AI works with humans rather than replacing them. By keeping human involved to provide guidance, answer questions, and maintain control, these systems can be more trustworthy and adaptable. Looking at examples from healthcare, finance, and software development, we show how human-AI teamwork can handle complex tasks better than AI working alone. We also discuss the challenges of building these collaborative systems and offer practical solutions. This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans. The most promising future for AI is not in systems that take over human roles, but in those that enhance human capabilities through meaningful partnership. |
|
2025-06-12T00:00:00 | 2506.09980 | Efficient Part-level 3D Object Generation via Dual Volume Packing | [
"Jiaxiang Tang",
"Ruijie Lu",
"Zhaoshuo Li",
"Zekun Hao",
"Xuan Li",
"Fangyin Wei",
"Shuran Song",
"Gang Zeng",
"Ming-Yu Liu",
"Tsung-Yi Lin"
] | Recent progress in 3D object generation has greatly improved both the quality and efficiency. However, most existing methods generate a single mesh with all parts fused together, which limits the ability to edit or manipulate individual parts. A key challenge is that different objects may have a varying number of parts. To address this, we propose a new end-to-end framework for part-level 3D object generation. Given a single input image, our method generates high-quality 3D objects with an arbitrary number of complete and semantically meaningful parts. We introduce a dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object. Experiments show that our model achieves better quality, diversity, and generalization than previous image-based part-level generation methods. |
|
2025-06-12T00:00:00 | 2506.05412 | Can Vision Language Models Infer Human Gaze Direction? A Controlled Study | [
"Zory Zhang",
"Pinyuan Feng",
"Bingyang Wang",
"Tianwei Zhao",
"Suyang Yu",
"Qingying Gao",
"Hokin Deng",
"Ziqiao Ma",
"Yijiang Li",
"Dezhi Luo"
] | Gaze-referential inference--the ability to infer what others are looking at--is a critical component of a theory of mind that underpins natural human-AI interaction. In a controlled study, we evaluated this skill across 111 Vision Language Models (VLMs) using photos taken with manipulated difficulty and variability, comparing performance with that of human participants (N = 65), and analyzed behaviors using mixed-effects models. We found that 94 of the 111 VLMs failed to do better than random guessing, while humans achieved near-ceiling accuracy. VLMs even respond with each choice almost equally frequently. Are they randomly guessing? Although most VLMs struggle, when we zoom in on five of the top-tier VLMs with above-chance performance, we find that their performance declined with increasing task difficulty but varied only slightly across different prompts and scene objects. These behavioral features cannot be explained by considering them as random guessers. Instead, they likely use a combination of heuristics and guessing such that their performance is subject to the task difficulty but robust to perceptual variations. This suggests that VLMs, lacking gaze inference capability, have yet to become technologies that can naturally interact with humans, but the potential remains. |
|
2025-06-12T00:00:00 | 2506.10209 | TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Games | [
"Prakamya Mishra",
"Jiang Liu",
"Jialian Wu",
"Xiaodong Yu",
"Zicheng Liu",
"Emad Barsoum"
] | Large reasoning models (LRMs) have demonstrated impressive reasoning capabilities across a broad range of tasks including Olympiad-level mathematical problems, indicating evidence of their complex reasoning abilities. While many reasoning benchmarks focus on the STEM domain, the ability of LRMs to reason correctly in broader task domains remains underexplored. In this work, we introduce TTT-Bench, a new benchmark that is designed to evaluate basic strategic, spatial, and logical reasoning abilities in LRMs through a suite of four two-player Tic-Tac-Toe-style games that humans can effortlessly solve from a young age. We propose a simple yet scalable programmatic approach for generating verifiable two-player game problems for TTT-Bench. Although these games are trivial for humans, they require reasoning about the intentions of the opponent, as well as the game board's spatial configurations, to ensure a win. We evaluate a diverse set of state-of-the-art LRMs, and discover that the models that excel at hard math problems frequently fail at these simple reasoning games. Further testing reveals that our evaluated reasoning models score on average downarrow 41\% \& downarrow 5\% lower on TTT-Bench compared to MATH 500 \& AIME 2024 respectively, with larger models achieving higher performance using shorter reasoning traces, where most of the models struggle on long-term strategic reasoning situations on simple and new TTT-Bench tasks. |
|
2025-06-12T00:00:00 | 2506.09820 | CoRT: Code-integrated Reasoning within Thinking | [
"Chengpeng Li",
"Zhengyang Tang",
"Ziniu Li",
"Mingfeng Xue",
"Keqin Bao",
"Tian Ding",
"Ruoyu Sun",
"Benyou Wang",
"Xiang Wang",
"Junyang Lin",
"Dayiheng Liu"
] | https://github.com/ChengpengLi1003/CoRT | Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable progress in natural language reasoning with long chain-of-thought (CoT), yet they remain inefficient or inaccurate when handling complex mathematical operations. Addressing these limitations through computational tools (e.g., computation libraries and symbolic solvers) is promising, but it introduces a technical challenge: Code Interpreter (CI) brings external knowledge beyond the model's internal text representations, thus the direct combination is not efficient. This paper introduces CoRT, a post-training framework for teaching LRMs to leverage CI effectively and efficiently. As a first step, we address the data scarcity issue by synthesizing code-integrated reasoning data through Hint-Engineering, which strategically inserts different hints at appropriate positions to optimize LRM-CI interaction. We manually create 30 high-quality samples, upon which we post-train models ranging from 1.5B to 32B parameters, with supervised fine-tuning, rejection fine-tuning and reinforcement learning. Our experimental results demonstrate that Hint-Engineering models achieve 4\% and 8\% absolute improvements on DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Qwen-1.5B respectively, across five challenging mathematical reasoning datasets. Furthermore, Hint-Engineering models use about 30\% fewer tokens for the 32B model and 50\% fewer tokens for the 1.5B model compared with the natural language models. The models and code are available at https://github.com/ChengpengLi1003/CoRT. |
2025-06-13T00:00:00 | 2506.10857 | VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos | [
"Jiashuo Yu",
"Yue Wu",
"Meng Chu",
"Zhifei Ren",
"Zizheng Huang",
"Pei Chu",
"Ruijie Zhang",
"Yinan He",
"Qirui Li",
"Songze Li",
"Zhenxiang Li",
"Zhongying Tu",
"Conghui He",
"Yu Qiao",
"Yali Wang",
"Yi Wang",
"Limin Wang"
] | We present VRBench, the first long narrative video benchmark crafted for evaluating large models' multi-step reasoning capabilities, addressing limitations in existing evaluations that overlook temporal reasoning and procedural validity. It comprises 1,010 long videos (with an average duration of 1.6 hours), along with 9,468 human-labeled multi-step question-answering pairs and 30,292 reasoning steps with timestamps. These videos are curated via a multi-stage filtering process including expert inter-rater reviewing to prioritize plot coherence. We develop a human-AI collaborative framework that generates coherent reasoning chains, each requiring multiple temporally grounded steps, spanning seven types (e.g., event attribution, implicit inference). VRBench designs a multi-phase evaluation pipeline that assesses models at both the outcome and process levels. Apart from the MCQs for the final results, we propose a progress-level LLM-guided scoring metric to evaluate the quality of the reasoning chain from multiple dimensions comprehensively. Through extensive evaluations of 12 LLMs and 16 VLMs on VRBench, we undertake a thorough analysis and provide valuable insights that advance the field of multi-step reasoning. |
|
2025-06-13T00:00:00 | 2506.10954 | SWE-Factory: Your Automated Factory for Issue Resolution Training Data and Evaluation Benchmarks | [
"Lianghong Guo",
"Yanlin Wang",
"Caihua Li",
"Pengyu Yang",
"Jiachi Chen",
"Wei Tao",
"Yingtian Zou",
"Duyu Tang",
"Zibin Zheng"
] | https://github.com/DeepSoftwareAnalytics/swe-factory | Constructing large-scale datasets for the GitHub issue resolution task is crucial for both training and evaluating the software engineering capabilities of Large Language Models (LLMs). However, the traditional process for creating such benchmarks is notoriously challenging and labor-intensive, particularly in the stages of setting up evaluation environments, grading test outcomes, and validating task instances. In this paper, we propose SWE-Factory, an automated pipeline designed to address these challenges. To tackle these issues, our pipeline integrates three core automated components. First, we introduce SWE-Builder, a multi-agent system that automates evaluation environment construction, which employs four specialized agents that work in a collaborative, iterative loop and leverages an environment memory pool to enhance efficiency. Second, we introduce a standardized, exit-code-based grading method that eliminates the need for manually writing custom parsers. Finally, we automate the fail2pass validation process using these reliable exit code signals. Experiments on 671 issues across four programming languages show that our pipeline can effectively construct valid task instances; for example, with GPT-4.1-mini, our SWE-Builder constructs 269 valid instances at 0.045 per instance, while with Gemini-2.5-flash, it achieves comparable performance at the lowest cost of 0.024 per instance. We also demonstrate that our exit-code-based grading achieves 100% accuracy compared to manual inspection, and our automated fail2pass validation reaches a precision of 0.92 and a recall of 1.00. We hope our automated pipeline will accelerate the collection of large-scale, high-quality GitHub issue resolution datasets for both training and evaluation. Our code and datasets are released at https://github.com/DeepSoftwareAnalytics/swe-factory. |
2025-06-13T00:00:00 | 2506.09942 | VerIF: Verification Engineering for Reinforcement Learning in Instruction Following | [
"Hao Peng",
"Yunjia Qi",
"Xiaozhi Wang",
"Bin Xu",
"Lei Hou",
"Juanzi Li"
] | https://github.com/THU-KEG/VerIF | Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). To support this approach, we construct a high-quality instruction-following dataset, VerInstruct, containing approximately 22,000 instances with associated verification signals. We apply RL training with VerIF to two models, achieving significant improvements across several representative instruction-following benchmarks. The trained models reach state-of-the-art performance among models of comparable size and generalize well to unseen constraints. We further observe that their general capabilities remain unaffected, suggesting that RL with VerIF can be integrated into existing RL recipes to enhance overall model performance. We have released our datasets, codes, and models to facilitate future research at https://github.com/THU-KEG/VerIF. |
2025-06-13T00:00:00 | 2506.09993 | Text-Aware Image Restoration with Diffusion Models | [
"Jaewon Min",
"Jin Hyeon Kim",
"Paul Hyunbin Cho",
"Jaeeun Lee",
"Jihye Park",
"Minkyu Park",
"Sangpil Kim",
"Hyunhee Park",
"Seungryong Kim"
] | Image restoration aims to recover degraded images. However, existing diffusion-based restoration methods, despite great success in natural image restoration, often struggle to faithfully reconstruct textual regions in degraded images. Those methods frequently generate plausible but incorrect text-like patterns, a phenomenon we refer to as text-image hallucination. In this paper, we introduce Text-Aware Image Restoration (TAIR), a novel restoration task that requires the simultaneous recovery of visual contents and textual fidelity. To tackle this task, we present SA-Text, a large-scale benchmark of 100K high-quality scene images densely annotated with diverse and complex text instances. Furthermore, we propose a multi-task diffusion framework, called TeReDiff, that integrates internal features from diffusion models into a text-spotting module, enabling both components to benefit from joint training. This allows for the extraction of rich text representations, which are utilized as prompts in subsequent denoising steps. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art restoration methods, achieving significant gains in text recognition accuracy. See our project page: https://cvlab-kaist.github.io/TAIR/ |
|
2025-06-13T00:00:00 | 2506.09513 | ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning | [
"Yu Sun",
"Xingyu Qian",
"Weiwen Xu",
"Hao Zhang",
"Chenghao Xiao",
"Long Li",
"Yu Rong",
"Wenbing Huang",
"Qifeng Bai",
"Tingyang Xu"
] | Though reasoning-based large language models (LLMs) have excelled in mathematics and programming, their capabilities in knowledge-intensive medical question answering remain underexplored. To address this, we introduce ReasonMed, the largest medical reasoning dataset, comprising 370k high-quality examples distilled from 1.7 million initial reasoning paths generated by various LLMs. ReasonMed is constructed through a multi-agent verification and refinement process, where we design an Error Refiner to enhance the reasoning paths by identifying and correcting error-prone steps flagged by a verifier. Leveraging ReasonMed, we systematically investigate best practices for training medical reasoning models and find that combining detailed Chain-of-Thought (CoT) reasoning with concise answer summaries yields the most effective fine-tuning strategy. Based on this strategy, we train ReasonMed-7B, which sets a new benchmark for sub-10B models, outperforming the prior best by 4.17\% and even exceeding LLaMA3.1-70B on PubMedQA by 4.60\%. |
|
2025-06-13T00:00:00 | 2506.08060 | Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques | [
"Asankhaya Sharma"
] | Large language models have transformed natural language processing, yet supervised fine-tuning (SFT) remains computationally intensive. This paper formally proves that capabilities acquired through SFT can be approximated by a base transformer model using inference-time techniques, specifically in-context learning (ICL), without altering model parameters, under idealized assumptions including unbounded computational resources and access to the fine-tuning dataset. We extend these results to practical scenarios with finite context lengths and partial dataset access. For text generation tasks with fixed output length l, datasets of size Oleft( m V{varepsilon^2} log m{delta} right) or, with bounded context, Oleft( l log V{varepsilon^2} log 1{delta} right) suffice to approximate fine-tuned behavior across m contexts within error varepsilon, where V is the vocabulary size and delta is the failure probability. For linear classification, datasets of size Oleft( d{varepsilon} right) or, with fixed context, Oleft( 1{varepsilon^2} log 1{delta} right) are sufficient, where d is the input dimension. Grounded in the Turing completeness of transformers, these results provide a theoretical foundation for resource-efficient deployment of large language models, with practical techniques like retrieval-augmented generation bridging theory to real-world applications. |
|
2025-06-13T00:00:00 | 2506.10890 | CreatiPoster: Towards Editable and Controllable Multi-Layer Graphic Design Generation | [
"Zhao Zhang",
"Yutao Cheng",
"Dexiang Hong",
"Maoke Yang",
"Gonglei Shi",
"Lei Ma",
"Hui Zhang",
"Jie Shao",
"Xinglong Wu"
] | https://github.com/graphic-design-ai/creatiposter | Graphic design plays a crucial role in both commercial and personal contexts, yet creating high-quality, editable, and aesthetically pleasing graphic compositions remains a time-consuming and skill-intensive task, especially for beginners. Current AI tools automate parts of the workflow, but struggle to accurately incorporate user-supplied assets, maintain editability, and achieve professional visual appeal. Commercial systems, like Canva Magic Design, rely on vast template libraries, which are impractical for replicate. In this paper, we introduce CreatiPoster, a framework that generates editable, multi-layer compositions from optional natural-language instructions or assets. A protocol model, an RGBA large multimodal model, first produces a JSON specification detailing every layer (text or asset) with precise layout, hierarchy, content and style, plus a concise background prompt. A conditional background model then synthesizes a coherent background conditioned on this rendered foreground layers. We construct a benchmark with automated metrics for graphic-design generation and show that CreatiPoster surpasses leading open-source approaches and proprietary commercial systems. To catalyze further research, we release a copyright-free corpus of 100,000 multi-layer designs. CreatiPoster supports diverse applications such as canvas editing, text overlay, responsive resizing, multilingual adaptation, and animated posters, advancing the democratization of AI-assisted graphic design. Project homepage: https://github.com/graphic-design-ai/creatiposter |
2025-06-13T00:00:00 | 2506.10357 | Optimus-3: Towards Generalist Multimodal Minecraft Agents with Scalable Task Experts | [
"Zaijing Li",
"Yuquan Xie",
"Rui Shao",
"Gongwei Chen",
"Weili Guan",
"Dongmei Jiang",
"Liqiang Nie"
] | Recently, agents based on multimodal large language models (MLLMs) have achieved remarkable progress across various domains. However, building a generalist agent with capabilities such as perception, planning, action, grounding, and reflection in open-world environments like Minecraft remains challenges: insufficient domain-specific data, interference among heterogeneous tasks, and visual diversity in open-world settings. In this paper, we address these challenges through three key contributions. 1) We propose a knowledge-enhanced data generation pipeline to provide scalable and high-quality training data for agent development. 2) To mitigate interference among heterogeneous tasks, we introduce a Mixture-of-Experts (MoE) architecture with task-level routing. 3) We develop a Multimodal Reasoning-Augmented Reinforcement Learning approach to enhance the agent's reasoning ability for visual diversity in Minecraft. Built upon these innovations, we present Optimus-3, a general-purpose agent for Minecraft. Extensive experimental results demonstrate that Optimus-3 surpasses both generalist multimodal large language models and existing state-of-the-art agents across a wide range of tasks in the Minecraft environment. Project page: https://cybertronagent.github.io/Optimus-3.github.io/ |
|
2025-06-13T00:00:00 | 2506.06561 | LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles | [
"Ho Yin 'Sam' Ng",
"Ting-Yao Hsu",
"Aashish Anantha Ramakrishnan",
"Branislav Kveton",
"Nedim Lipka",
"Franck Dernoncourt",
"Dongwon Lee",
"Tong Yu",
"Sungchul Kim",
"Ryan A. Rossi",
"Ting-Hao 'Kenneth' Huang"
] | Figure captions are crucial for helping readers understand and remember a figure's key message. Many models have been developed to generate these captions, helping authors compose better quality captions more easily. Yet, authors almost always need to revise generic AI-generated captions to match their writing style and the domain's style, highlighting the need for personalization. Despite language models' personalization (LaMP) advances, these technologies often focus on text-only settings and rarely address scenarios where both inputs and profiles are multimodal. This paper introduces LaMP-Cap, a dataset for personalized figure caption generation with multimodal figure profiles. For each target figure, LaMP-Cap provides not only the needed inputs, such as figure images, but also up to three other figures from the same document--each with its image, caption, and figure-mentioning paragraphs--as a profile to characterize the context. Experiments with four LLMs show that using profile information consistently helps generate captions closer to the original author-written ones. Ablation studies reveal that images in the profile are more helpful than figure-mentioning paragraphs, highlighting the advantage of using multimodal profiles over text-only ones. |
|
2025-06-13T00:00:00 | 2506.05982 | MCA-Bench: A Multimodal Benchmark for Evaluating CAPTCHA Robustness Against VLM-based Attacks | [
"Zonglin Wu",
"Yule Xue",
"Xin Wei",
"Yiren Song"
] | As automated attack techniques rapidly advance, CAPTCHAs remain a critical defense mechanism against malicious bots. However, existing CAPTCHA schemes encompass a diverse range of modalities -- from static distorted text and obfuscated images to interactive clicks, sliding puzzles, and logic-based questions -- yet the community still lacks a unified, large-scale, multimodal benchmark to rigorously evaluate their security robustness. To address this gap, we introduce MCA-Bench, a comprehensive and reproducible benchmarking suite that integrates heterogeneous CAPTCHA types into a single evaluation protocol. Leveraging a shared vision-language model backbone, we fine-tune specialized cracking agents for each CAPTCHA category, enabling consistent, cross-modal assessments. Extensive experiments reveal that MCA-Bench effectively maps the vulnerability spectrum of modern CAPTCHA designs under varied attack settings, and crucially offers the first quantitative analysis of how challenge complexity, interaction depth, and model solvability interrelate. Based on these findings, we propose three actionable design principles and identify key open challenges, laying the groundwork for systematic CAPTCHA hardening, fair benchmarking, and broader community collaboration. Datasets and code are available online. |
|
2025-06-13T00:00:00 | 2506.08373 | Draft-based Approximate Inference for LLMs | [
"Kevin Galim",
"Ethan Ewer",
"Wonjun Kang",
"Minjae Lee",
"Hyung Il Koo",
"Kangwook Lee"
] | https://github.com/furiosa-ai/draft-based-approx-llm | Optimizing inference for long-context Large Language Models (LLMs) is increasingly important due to the quadratic compute and linear memory complexity of Transformers. Existing approximation methods, such as key-value (KV) cache dropping, sparse attention, and prompt compression, typically rely on rough predictions of token or KV pair importance. We propose a novel framework for approximate LLM inference that leverages small draft models to more accurately predict the importance of tokens and KV pairs. Specifically, we introduce two instantiations of our proposed framework: (i) SpecKV, which leverages a draft output to accurately assess the importance of each KV pair for more effective KV cache dropping, and (ii) SpecPC, which uses the draft model's attention activations to identify and discard unimportant prompt tokens. To the best of our knowledge, this is the first work to use draft models for approximate LLM inference acceleration, extending their utility beyond traditional lossless speculative decoding. We motivate our methods with theoretical and empirical analyses, and show a strong correlation between the attention patterns of draft and target models. Extensive experiments on long-context benchmarks show that our methods consistently achieve higher accuracy than existing baselines, while preserving the same improvements in memory usage, latency, and throughput. Our code is available at https://github.com/furiosa-ai/draft-based-approx-llm. |
2025-06-13T00:00:00 | 2506.10952 | Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training | [
"Mozhi Zhang",
"Howe Tissue",
"Lu Wang",
"Xipeng Qiu"
] | We introduce~Domain2Vec, a novel approach that decomposes any dataset into a linear combination of several meta-domains, a new concept designed to capture the key underlying features of datasets. Domain2Vec maintains a vocabulary of meta-domains and uses a classifier to decompose any given dataset into a domain vector that corresponds to a distribution over this vocabulary. These domain vectors enable the identification of the optimal data mixture for language model (LM) pretraining in a training-free manner under the \textbf{Distribution Alignment Assumption} (DA^{2}), which suggests that when the data distributions of the training set and the validation set are better aligned, a lower validation loss is achieved. Moreover, Domain2vec can be seamlessly integrated into previous works to model the relationship between domain vectors and LM performance, greatly enhancing the efficiency and scalability of previous methods. Extensive experiments demonstrate that Domain2Vec helps find the data mixture that enhances downstream task performance with minimal computational overhead. Specifically, Domain2Vec achieves the same validation loss on Pile-CC using only 51.5% of the computation required when training on the original mixture of The Pile dataset. Under equivalent compute budget, Domain2Vec improves downstream performance by an average of 2.83%. |
|
2025-06-13T00:00:00 | 2506.10821 | VideoDeepResearch: Long Video Understanding With Agentic Tool Using | [
"Huaying Yuan",
"Zheng Liu",
"Junjie Zhou",
"Ji-Rong Wen",
"Zhicheng Dou"
] | Long video understanding (LVU) presents a significant challenge for current multi-modal large language models (MLLMs) due to the task's inherent complexity and context window constraint. It is widely assumed that addressing LVU tasks requires foundation MLLMs with extended context windows, strong visual perception capabilities, and proficient domain expertise. In this work, we challenge this common belief by introducing VideoDeepResearch, a novel agentic framework for long video understanding. Our approach relies solely on a text-only large reasoning model (LRM) combined with a modular multi-modal toolkit, including multimodal retrievers and visual perceivers, all of which are readily available in practice. For each LVU task, the system formulates a problem-solving strategy through reasoning, while selectively accessing and utilizing essential video content via tool using. We conduct extensive experiments on popular LVU benchmarks, including MLVU, Video-MME, and LVBench. Our results demonstrate that VideoDeepResearch achieves substantial improvements over existing MLLM baselines, surpassing the previous state-of-the-art by 9.6%, 6.6%, and 3.9% on MLVU (test), LVBench, and LongVideoBench, respectively. These findings highlight the promise of agentic systems in overcoming key challenges in LVU problems. |
|
2025-06-13T00:00:00 | 2506.09344 | Ming-Omni: A Unified Multimodal Model for Perception and Generation | [
"Inclusion AI",
"Biao Gong",
"Cheng Zou",
"Chuanyang Zheng",
"Chunluan Zhou",
"Canxiang Yan",
"Chunxiang Jin",
"Chunjie Shen",
"Dandan Zheng",
"Fudong Wang",
"Furong Xu",
"GuangMing Yao",
"Jun Zhou",
"Jingdong Chen",
"Jianxin Sun",
"Jiajia Liu",
"Jianjiang Zhu",
"Jun Peng",
"Kaixiang Ji",
"Kaiyou Song",
"Kaimeng Ren",
"Libin Wang",
"Lixiang Ru",
"Lele Xie",
"Longhua Tan",
"Lyuxin Xue",
"Lan Wang",
"Mochen Bai",
"Ning Gao",
"Pei Chen",
"Qingpei Guo",
"Qinglong Zhang",
"Qiang Xu",
"Rui Liu",
"Ruijie Xiong",
"Sirui Gao",
"Tinghao Liu",
"Taisong Li",
"Weilong Chai",
"Xinyu Xiao",
"Xiaomei Wang",
"Xiaoxue Chen",
"Xiao Lu",
"Xiaoyu Li",
"Xingning Dong",
"Xuzheng Yu",
"Yi Yuan",
"Yuting Gao",
"Yunxiao Sun",
"Yipeng Chen",
"Yifei Wu",
"Yongjie Lyu",
"Ziping Ma",
"Zipeng Feng",
"Zhijiang Fang",
"Zhihao Qiu",
"Ziyuan Huang",
"Zhengyu He"
] | We propose Ming-Omni, a unified multimodal model capable of processing images, text, audio, and video, while demonstrating strong proficiency in both speech and image generation. Ming-Omni employs dedicated encoders to extract tokens from different modalities, which are then processed by Ling, an MoE architecture equipped with newly proposed modality-specific routers. This design enables a single model to efficiently process and fuse multimodal inputs within a unified framework, thereby facilitating diverse tasks without requiring separate models, task-specific fine-tuning, or structural redesign. Importantly, Ming-Omni extends beyond conventional multimodal models by supporting audio and image generation. This is achieved through the integration of an advanced audio decoder for natural-sounding speech and Ming-Lite-Uni for high-quality image generation, which also allow the model to engage in context-aware chatting, perform text-to-speech conversion, and conduct versatile image editing. Our experimental results showcase Ming-Omni offers a powerful solution for unified perception and generation across all modalities. Notably, our proposed Ming-Omni is the first open-source model we are aware of to match GPT-4o in modality support, and we release all code and model weights to encourage further research and development in the community. |
|
2025-06-13T00:00:00 | 2506.09967 | Resa: Transparent Reasoning Models via SAEs | [
"Shangshang Wang",
"Julian Asilis",
"Ömer Faruk Akgül",
"Enes Burak Bilgin",
"Ollie Liu",
"Deqing Fu",
"Willie Neiswanger"
] | How cost-effectively can we elicit strong reasoning in language models by leveraging their underlying representations? We answer this question with Resa, a family of 1.5B reasoning models trained via a novel and efficient sparse autoencoder tuning (SAE-Tuning) procedure. This method first trains an SAE to capture reasoning abilities from a source model, and then uses the trained SAE to guide a standard supervised fine-tuning process to elicit such abilities in a target model, all using verified question-answer data without any reasoning traces. Notably, when applied to certain base models before further RL post-training, SAE-Tuning retains >97% of its RL-trained counterpart's reasoning performance while reducing training costs by >2000x to roughly \1 and training time by >450x to around 20 minutes. Furthermore, when applied to lightly RL-trained models (e.g., within 1 hour on 2 GPUs), it enables reasoning performance such as 43.33% Pass@1 on AIME24 and 90% Pass@1 on AMC23 for only around 1 additional cost. Surprisingly, the reasoning abilities extracted via SAEs are potentially both generalizable and modular. Generality means abilities extracted from one dataset still elevate performance on a larger and overlapping corpus. Modularity means abilities extracted from Qwen or Qwen-Math can be attached to the R1-Distill model at test time, without any retraining, and yield comparable gains. Extensive ablations validate these findings and all artifacts are fully open-sourced. |
|
2025-06-13T00:00:00 | 2506.10953 | Build the web for agents, not agents for the web | [
"Xing Han Lù",
"Gaurav Kamath",
"Marius Mosbach",
"Siva Reddy"
] | Recent advancements in Large Language Models (LLMs) and multimodal counterparts have spurred significant interest in developing web agents -- AI systems capable of autonomously navigating and completing tasks within web environments. While holding tremendous promise for automating complex web interactions, current approaches face substantial challenges due to the fundamental mismatch between human-designed interfaces and LLM capabilities. Current methods struggle with the inherent complexity of web inputs, whether processing massive DOM trees, relying on screenshots augmented with additional information, or bypassing the user interface entirely through API interactions. This position paper advocates for a paradigm shift in web agent research: rather than forcing web agents to adapt to interfaces designed for humans, we should develop a new interaction paradigm specifically optimized for agentic capabilities. To this end, we introduce the concept of an Agentic Web Interface (AWI), an interface specifically designed for agents to navigate a website. We establish six guiding principles for AWI design, emphasizing safety, efficiency, and standardization, to account for the interests of all primary stakeholders. This reframing aims to overcome fundamental limitations of existing interfaces, paving the way for more efficient, reliable, and transparent web agent design, which will be a collaborative effort involving the broader ML community. |
|
2025-06-13T00:00:00 | 2506.06694 | Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning | [
"Yuan Yuan",
"Yukun Liu",
"Chonghua Han",
"Jie Feng",
"Yong Li"
] | Foundation models have revolutionized fields such as natural language processing and computer vision by enabling general-purpose learning across diverse tasks and datasets. However, building analogous models for human mobility remains challenging due to the privacy-sensitive nature of mobility data and the resulting data silos across institutions. To bridge this gap, we propose MoveGCL, a scalable and privacy-preserving framework for training mobility foundation models via generative continual learning. Without sharing raw data, MoveGCL enables decentralized and progressive model evolution by replaying synthetic trajectories generated from a frozen teacher model, and reinforces knowledge retention through a tailored distillation strategy that mitigates catastrophic forgetting. To address the heterogeneity of mobility patterns, MoveGCL incorporates a Mixture-of-Experts Transformer with a mobility-aware expert routing mechanism, and employs a layer-wise progressive adaptation strategy to stabilize continual updates. Experiments on six real-world urban datasets demonstrate that MoveGCL achieves performance comparable to joint training and significantly outperforms federated learning baselines, while offering strong privacy protection. MoveGCL marks a crucial step toward unlocking foundation models for mobility, offering a practical blueprint for open, scalable, and privacy-preserving model development in the era of foundation models. |
|
2025-06-13T00:00:00 | 2506.06950 | What Makes a Good Natural Language Prompt? | [
"Do Xuan Long",
"Duy Dinh",
"Ngoc-Hai Nguyen",
"Kenji Kawaguchi",
"Nancy F. Chen",
"Shafiq Joty",
"Min-Yen Kan"
] | As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However, there is limited conceptual consensus on what exactly quantifies natural language prompts. We attempt to address this question by conducting a meta-analysis surveying more than 150 prompting-related papers from leading NLP and AI conferences from 2022 to 2025 and blogs. We propose a property- and human-centric framework for evaluating prompt quality, encompassing 21 properties categorized into six dimensions. We then examine how existing studies assess their impact on LLMs, revealing their imbalanced support across models and tasks, and substantial research gaps. Further, we analyze correlations among properties in high-quality natural language prompts, deriving prompting recommendations. We then empirically explore multi-property prompt enhancements in reasoning tasks, observing that single-property enhancements often have the greatest impact. Finally, we discover that instruction-tuning on property-enhanced prompts can result in better reasoning models. Our findings establish a foundation for property-centric prompt evaluation and optimization, bridging the gaps between human--AI communication and opening new prompting research directions. |
|
2025-06-13T00:00:00 | 2506.10540 | AniMaker: Automated Multi-Agent Animated Storytelling with MCTS-Driven Clip Generation | [
"Haoyuan Shi",
"Yunxin Li",
"Xinyu Chen",
"Longyue Wang",
"Baotian Hu",
"Min Zhang"
] | Despite rapid advancements in video generation models, generating coherent storytelling videos that span multiple scenes and characters remains challenging. Current methods often rigidly convert pre-generated keyframes into fixed-length clips, resulting in disjointed narratives and pacing issues. Furthermore, the inherent instability of video generation models means that even a single low-quality clip can significantly degrade the entire output animation's logical coherence and visual continuity. To overcome these obstacles, we introduce AniMaker, a multi-agent framework enabling efficient multi-candidate clip generation and storytelling-aware clip selection, thus creating globally consistent and story-coherent animation solely from text input. The framework is structured around specialized agents, including the Director Agent for storyboard generation, the Photography Agent for video clip generation, the Reviewer Agent for evaluation, and the Post-Production Agent for editing and voiceover. Central to AniMaker's approach are two key technical components: MCTS-Gen in Photography Agent, an efficient Monte Carlo Tree Search (MCTS)-inspired strategy that intelligently navigates the candidate space to generate high-potential clips while optimizing resource usage; and AniEval in Reviewer Agent, the first framework specifically designed for multi-shot animation evaluation, which assesses critical aspects such as story-level consistency, action completion, and animation-specific features by considering each clip in the context of its preceding and succeeding clips. Experiments demonstrate that AniMaker achieves superior quality as measured by popular metrics including VBench and our proposed AniEval framework, while significantly improving the efficiency of multi-candidate generation, pushing AI-generated storytelling animation closer to production standards. |
|
2025-06-13T00:00:00 | 2506.10974 | AutoMind: Adaptive Knowledgeable Agent for Automated Data Science | [
"Yixin Ou",
"Yujie Luo",
"Jingsheng Zheng",
"Lanning Wei",
"Shuofei Qiao",
"Jintian Zhang",
"Da Zheng",
"Huajun Chen",
"Ningyu Zhang"
] | Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems. LLM-driven data science agents promise to automate the entire machine learning pipeline, yet their real-world effectiveness remains limited. Existing frameworks depend on rigid, pre-defined workflows and inflexible coding strategies; consequently, they excel only on relatively simple, classical problems and fail to capture the empirical expertise that human practitioners bring to complex, innovative tasks. In this work, we introduce AutoMind, an adaptive, knowledgeable LLM-agent framework that overcomes these deficiencies through three key advances: (1) a curated expert knowledge base that grounds the agent in domain expert knowledge, (2) an agentic knowledgeable tree search algorithm that strategically explores possible solutions, and (3) a self-adaptive coding strategy that dynamically tailors code generation to task complexity. Evaluations on two automated data science benchmarks demonstrate that AutoMind delivers superior performance versus state-of-the-art baselines. Additional analyses confirm favorable effectiveness, efficiency, and qualitative solution quality, highlighting AutoMind as an efficient and robust step toward fully automated data science. |
|
2025-06-13T00:00:00 | 2506.10960 | ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark | [
"Kangwei Liu",
"Siyuan Cheng",
"Bozhong Tian",
"Xiaozhuan Liang",
"Yuyang Yin",
"Meng Han",
"Ningyu Zhang",
"Bryan Hooi",
"Xi Chen",
"Shumin Deng"
] | https://github.com/zjunlp/ChineseHarm-bench | Large language models (LLMs) have been increasingly applied to automated harmful content detection tasks, assisting moderators in identifying policy violations and improving the overall efficiency and accuracy of content review. However, existing resources for harmful content detection are predominantly focused on English, with Chinese datasets remaining scarce and often limited in scope. We present a comprehensive, professionally annotated benchmark for Chinese content harm detection, which covers six representative categories and is constructed entirely from real-world data. Our annotation process further yields a knowledge rule base that provides explicit expert knowledge to assist LLMs in Chinese harmful content detection. In addition, we propose a knowledge-augmented baseline that integrates both human-annotated knowledge rules and implicit knowledge from large language models, enabling smaller models to achieve performance comparable to state-of-the-art LLMs. Code and data are available at https://github.com/zjunlp/ChineseHarm-bench. |
2025-06-13T00:00:00 | 2506.10378 | Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning | [
"Jikai Jin",
"Vasilis Syrgkanis",
"Sham Kakade",
"Hanlin Zhang"
] | Faithful evaluation of language model capabilities is crucial for deriving actionable insights that can inform model development. However, rigorous causal evaluations in this domain face significant methodological challenges, including complex confounding effects and prohibitive computational costs associated with extensive retraining. To tackle these challenges, we propose a causal representation learning framework wherein observed benchmark performance is modeled as a linear transformation of a few latent capability factors. Crucially, these latent factors are identified as causally interrelated after appropriately controlling for the base model as a common confounder. Applying this approach to a comprehensive dataset encompassing over 1500 models evaluated across six benchmarks from the Open LLM Leaderboard, we identify a concise three-node linear causal structure that reliably explains the observed performance variations. Further interpretation of this causal structure provides substantial scientific insights beyond simple numerical rankings: specifically, we reveal a clear causal direction starting from general problem-solving capabilities, advancing through instruction-following proficiency, and culminating in mathematical reasoning ability. Our results underscore the essential role of carefully controlling base model variations during evaluation, a step critical to accurately uncovering the underlying causal relationships among latent model capabilities. |
|
2025-06-13T00:00:00 | 2506.10741 | PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework | [
"SiXiang Chen",
"Jianyu Lai",
"Jialin Gao",
"Tian Ye",
"Haoyu Chen",
"Hengyu Shi",
"Shitong Shao",
"Yunlong Lin",
"Song Fei",
"Zhaohu Xing",
"Yeying Jin",
"Junfeng Luo",
"Xiaoming Wei",
"Lei Zhu"
] | Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via best-of-n preference optimization; and (iv) joint vision-language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal-approaching the quality of SOTA commercial systems. Our code, models, and datasets can be found in the Project page: https://ephemeral182.github.io/PosterCraft |
|
2025-06-13T00:00:00 | 2506.10910 | Magistral | [
"Mistral-AI",
"Abhinav Rastogi",
"Albert Q. Jiang",
"Andy Lo",
"Gabrielle Berrada",
"Guillaume Lample",
"Jason Rute",
"Joep Barmentlo",
"Karmesh Yadav",
"Kartik Khandelwal",
"Khyathi Raghavi Chandu",
"Léonard Blier",
"Lucile Saulnier",
"Matthieu Dinot",
"Maxime Darrin",
"Neha Gupta",
"Roman Soletskyi",
"Sagar Vaze",
"Teven Le Scao",
"Yihan Wang",
"Adam Yang",
"Alexander H. Liu",
"Alexandre Sablayrolles",
"Amélie Héliou",
"Amélie Martin",
"Andy Ehrenberg",
"Anmol Agarwal",
"Antoine Roux",
"Arthur Darcet",
"Arthur Mensch",
"Baptiste Bout",
"Baptiste Rozière",
"Baudouin De Monicault",
"Chris Bamford",
"Christian Wallenwein",
"Christophe Renaudin",
"Clémence Lanfranchi",
"Darius Dabert",
"Devon Mizelle",
"Diego de las Casas",
"Elliot Chane-Sane",
"Emilien Fugier",
"Emma Bou Hanna",
"Gauthier Delerce",
"Gauthier Guinet",
"Georgii Novikov",
"Guillaume Martin",
"Himanshu Jaju",
"Jan Ludziejewski",
"Jean-Hadrien Chabran",
"Jean-Malo Delignon",
"Joachim Studnia",
"Jonas Amar",
"Josselin Somerville Roberts",
"Julien Denize",
"Karan Saxena",
"Kush Jain",
"Lingxiao Zhao",
"Louis Martin",
"Luyu Gao",
"Lélio Renard Lavaud",
"Marie Pellat",
"Mathilde Guillaumin",
"Mathis Felardos",
"Maximilian Augustin",
"Mickaël Seznec",
"Nikhil Raghuraman",
"Olivier Duchenne",
"Patricia Wang",
"Patrick von Platen",
"Patryk Saffer",
"Paul Jacob",
"Paul Wambergue",
"Paula Kurylowicz",
"Pavankumar Reddy Muddireddy",
"Philomène Chagniot",
"Pierre Stock",
"Pravesh Agrawal",
"Romain Sauvestre",
"Rémi Delacourt",
"Sanchit Gandhi",
"Sandeep Subramanian",
"Shashwat Dalal",
"Siddharth Gandhi",
"Soham Ghosh",
"Srijan Mishra",
"Sumukh Aithal",
"Szymon Antoniak",
"Thibault Schueller",
"Thibaut Lavril",
"Thomas Robert",
"Thomas Wang",
"Timothée Lacroix",
"Valeriia Nemychnikova",
"Victor Paltz",
"Virgile Richard",
"Wen-Ding Li",
"William Marshall",
"Xuanyu Zhang",
"Yunhao Tang"
] | We introduce Magistral, Mistral's first reasoning model and our own scalable reinforcement learning (RL) pipeline. Instead of relying on existing implementations and RL traces distilled from prior models, we follow a ground up approach, relying solely on our own models and infrastructure. Notably, we demonstrate a stack that enabled us to explore the limits of pure RL training of LLMs, present a simple method to force the reasoning language of the model, and show that RL on text data alone maintains most of the initial checkpoint's capabilities. We find that RL on text maintains or improves multimodal understanding, instruction following and function calling. We present Magistral Medium, trained for reasoning on top of Mistral Medium 3 with RL alone, and we open-source Magistral Small (Apache 2.0) which further includes cold-start data from Magistral Medium. |
|
2025-06-13T00:00:00 | 2506.10036 | Token Perturbation Guidance for Diffusion Models | [
"Javad Rajabi",
"Soroush Mehraban",
"Seyedmorteza Sadat",
"Babak Taati"
] | https://github.com/TaatiTeam/Token-Perturbation-Guidance | Classifier-free guidance (CFG) has become an essential component of modern diffusion models to enhance both generation quality and alignment with input conditions. However, CFG requires specific training procedures and is limited to conditional generation. To address these limitations, we propose Token Perturbation Guidance (TPG), a novel method that applies perturbation matrices directly to intermediate token representations within the diffusion network. TPG employs a norm-preserving shuffling operation to provide effective and stable guidance signals that improve generation quality without architectural changes. As a result, TPG is training-free and agnostic to input conditions, making it readily applicable to both conditional and unconditional generation. We further analyze the guidance term provided by TPG and show that its effect on sampling more closely resembles CFG compared to existing training-free guidance techniques. Extensive experiments on SDXL and Stable Diffusion 2.1 show that TPG achieves nearly a 2times improvement in FID for unconditional generation over the SDXL baseline, while closely matching CFG in prompt alignment. These results establish TPG as a general, condition-agnostic guidance method that brings CFG-like benefits to a broader class of diffusion models. The code is available at https://github.com/TaatiTeam/Token-Perturbation-Guidance |
2025-06-13T00:00:00 | 2506.07795 | LLM Unlearning Should Be Form-Independent | [
"Xiaotian Ye",
"Mengqi Zhang",
"Shu Wu"
] | Large Language Model (LLM) unlearning aims to erase or suppress undesirable knowledge within the model, offering promise for controlling harmful or private information to prevent misuse. However, recent studies highlight its limited efficacy in real-world scenarios, hindering practical adoption. In this study, we identify a pervasive issue underlying many downstream failures: the effectiveness of existing unlearning methods heavily depends on the form of training samples and frequently fails to generalize to alternate expressions of the same knowledge. We formally characterize this problem as Form-Dependent Bias and systematically investigate its specific manifestation patterns across various downstream tasks. To quantify its prevalence and support future research, we introduce ORT, a novel benchmark designed to evaluate the robustness of unlearning methods against variations in knowledge expression. Results reveal that Form-Dependent Bias is both widespread and severe among current techniques. We argue that LLM unlearning should be form-independent to address the endless forms of downstream tasks encountered in real-world security-critical scenarios. Towards this goal, we introduce Rank-one Concept Redirection (ROCR), a novel training-free method, as a promising solution path. ROCR performs unlearning by targeting the invariants in downstream tasks, specifically the activated dangerous concepts. It is capable of modifying model parameters within seconds to redirect the model's perception of a specific unlearning target concept to another harmless concept. Extensive experiments demonstrate that ROCR significantly improves unlearning effectiveness compared to traditional methods while generating highly natural outputs. |
|
2025-06-13T00:00:00 | 2506.09952 | UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting | [
"Ziyi Wang",
"Yanran Zhang",
"Jie Zhou",
"Jiwen Lu"
] | https://github.com/wangzy22/UniPre3D | The scale diversity of point cloud data presents significant challenges in developing unified representation learning techniques for 3D vision. Currently, there are few unified 3D models, and no existing pre-training method is equally effective for both object- and scene-level point clouds. In this paper, we introduce UniPre3D, the first unified pre-training method that can be seamlessly applied to point clouds of any scale and 3D models of any architecture. Our approach predicts Gaussian primitives as the pre-training task and employs differentiable Gaussian splatting to render images, enabling precise pixel-level supervision and end-to-end optimization. To further regulate the complexity of the pre-training task and direct the model's focus toward geometric structures, we integrate 2D features from pre-trained image models to incorporate well-established texture knowledge. We validate the universal effectiveness of our proposed method through extensive experiments across a variety of object- and scene-level tasks, using diverse point cloud models as backbones. Code is available at https://github.com/wangzy22/UniPre3D. |
2025-06-13T00:00:00 | 2506.08234 | Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions | [
"Yu-Ang Lee",
"Guan-Ting Yi",
"Mei-Yi Liu",
"Jui-Chao Lu",
"Guan-Bo Yang",
"Yun-Nung Chen"
] | https://github.com/MiuLab/AISysOpt-Survey | Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept at performing sophisticated tasks. However, as these systems grow in complexity, new challenges arise in optimizing not only individual components but also their interactions. While traditional optimization methods such as supervised fine-tuning (SFT) and reinforcement learning (RL) remain foundational, the rise of natural language feedback introduces promising new approaches, especially for optimizing non-differentiable systems. This paper provides a systematic review of recent progress in optimizing compound AI systems, encompassing both numerical and language-based techniques. We formalize the notion of compound AI system optimization, classify existing methods along several key dimensions, and highlight open research challenges and future directions in this rapidly evolving field. A list of surveyed papers is publicly available at https://github.com/MiuLab/AISysOpt-Survey. |
2025-06-13T00:00:00 | 2506.08862 | StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams | [
"Zike Wu",
"Qi Yan",
"Xuanyu Yi",
"Lele Wang",
"Renjie Liao"
] | https://github.com/nickwzk/StreamSplat | Real-time reconstruction of dynamic 3D scenes from uncalibrated video streams is crucial for numerous real-world applications. However, existing methods struggle to jointly address three key challenges: 1) processing uncalibrated inputs in real time, 2) accurately modeling dynamic scene evolution, and 3) maintaining long-term stability and computational efficiency. To this end, we introduce StreamSplat, the first fully feed-forward framework that transforms uncalibrated video streams of arbitrary length into dynamic 3D Gaussian Splatting (3DGS) representations in an online manner, capable of recovering scene dynamics from temporally local observations. We propose two key technical innovations: a probabilistic sampling mechanism in the static encoder for 3DGS position prediction, and a bidirectional deformation field in the dynamic decoder that enables robust and efficient dynamic modeling. Extensive experiments on static and dynamic benchmarks demonstrate that StreamSplat consistently outperforms prior works in both reconstruction quality and dynamic scene modeling, while uniquely supporting online reconstruction of arbitrarily long video streams. Code and models are available at https://github.com/nickwzk/StreamSplat. |
2025-06-13T00:00:00 | 2506.10178 | Attention, Please! Revisiting Attentive Probing for Masked Image Modeling | [
"Bill Psomas",
"Dionysis Christopoulos",
"Eirini Baltzi",
"Ioannis Kakogeorgiou",
"Tilemachos Aravanis",
"Nikos Komodakis",
"Konstantinos Karantzalos",
"Yannis Avrithis",
"Giorgos Tolias"
] | https://github.com/billpsomas/efficient-probing | As fine-tuning (FT) becomes increasingly impractical at scale, probing is emerging as the preferred evaluation protocol for self-supervised learning (SSL). Yet, the standard linear probing (LP) fails to adequately reflect the potential of models trained with Masked Image Modeling (MIM), due to the distributed nature of patch tokens. This motivates the need for attentive probing, an alternative that uses attention to selectively aggregate patch-level features. Despite its growing adoption, attentive probing remains under-explored, with existing methods suffering from excessive parameterization and poor computational efficiency. In this work, we revisit attentive probing through the lens of the accuracy-efficiency trade-off. We conduct a systematic study of existing methods, analyzing their mechanisms and benchmarking their performance. We introduce efficient probing (EP), a multi-query cross-attention mechanism that eliminates redundant projections, reduces the number of trainable parameters, and achieves up to a 10times speed-up over conventional multi-head attention. Despite its simplicity, EP outperforms LP and prior attentive probing approaches across seven benchmarks, generalizes well beyond MIM to diverse pre-training paradigms, produces interpretable attention maps, and achieves strong gains in low-shot and layer-wise settings. Code available at https://github.com/billpsomas/efficient-probing. |
2025-06-13T00:00:00 | 2506.10568 | DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers | [
"Lizhen Wang",
"Zhurong Xia",
"Tianshu Hu",
"Pengrui Wang",
"Pengfei Wang",
"Zerong Zheng",
"Ming Zhou"
] | In e-commerce and digital marketing, generating high-fidelity human-product demonstration videos is important for effective product presentation. However, most existing frameworks either fail to preserve the identities of both humans and products or lack an understanding of human-product spatial relationships, leading to unrealistic representations and unnatural interactions. To address these challenges, we propose a Diffusion Transformer (DiT)-based framework. Our method simultaneously preserves human identities and product-specific details, such as logos and textures, by injecting paired human-product reference information and utilizing an additional masked cross-attention mechanism. We employ a 3D body mesh template and product bounding boxes to provide precise motion guidance, enabling intuitive alignment of hand gestures with product placements. Additionally, structured text encoding is used to incorporate category-level semantics, enhancing 3D consistency during small rotational changes across frames. Trained on a hybrid dataset with extensive data augmentation strategies, our approach outperforms state-of-the-art techniques in maintaining the identity integrity of both humans and products and generating realistic demonstration motions. Project page: https://submit2025-dream.github.io/DreamActor-H1/. |
|
2025-06-13T00:00:00 | 2506.10978 | Fine-Grained Perturbation Guidance via Attention Head Selection | [
"Donghoon Ahn",
"Jiwon Kang",
"Sanghyun Lee",
"Minjae Kim",
"Jaewon Min",
"Wooseok Jang",
"Saungwu Lee",
"Sayak Paul",
"Susung Hong",
"Seungryong Kim"
] | Recent guidance methods in diffusion models steer reverse sampling by perturbing the model to construct an implicit weak model and guide generation away from it. Among these approaches, attention perturbation has demonstrated strong empirical performance in unconditional scenarios where classifier-free guidance is not applicable. However, existing attention perturbation methods lack principled approaches for determining where perturbations should be applied, particularly in Diffusion Transformer (DiT) architectures where quality-relevant computations are distributed across layers. In this paper, we investigate the granularity of attention perturbations, ranging from the layer level down to individual attention heads, and discover that specific heads govern distinct visual concepts such as structure, style, and texture quality. Building on this insight, we propose "HeadHunter", a systematic framework for iteratively selecting attention heads that align with user-centric objectives, enabling fine-grained control over generation quality and visual attributes. In addition, we introduce SoftPAG, which linearly interpolates each selected head's attention map toward an identity matrix, providing a continuous knob to tune perturbation strength and suppress artifacts. Our approach not only mitigates the oversmoothing issues of existing layer-level perturbation but also enables targeted manipulation of specific visual styles through compositional head selection. We validate our method on modern large-scale DiT-based text-to-image models including Stable Diffusion 3 and FLUX.1, demonstrating superior performance in both general quality enhancement and style-specific guidance. Our work provides the first head-level analysis of attention perturbation in diffusion models, uncovering interpretable specialization within attention layers and enabling practical design of effective perturbation strategies. |
|
2025-06-13T00:00:00 | 2506.10274 | Discrete Audio Tokens: More Than a Survey! | [
"Pooneh Mousavi",
"Gallil Maimon",
"Adel Moumen",
"Darius Petermann",
"Jiatong Shi",
"Haibin Wu",
"Haici Yang",
"Anastasia Kuznetsova",
"Artem Ploujnikov",
"Ricard Marxer",
"Bhuvana Ramabhadran",
"Benjamin Elizalde",
"Loren Lugosch",
"Jinyu Li",
"Cem Subakan",
"Phil Woodland",
"Minje Kim",
"Hung-yi Lee",
"Shinji Watanabe",
"Yossi Adi",
"Mirco Ravanelli"
] | Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics while enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks.They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/. |
|
2025-06-13T00:00:00 | 2506.10728 | Beyond True or False: Retrieval-Augmented Hierarchical Analysis of Nuanced Claims | [
"Priyanka Kargupta",
"Runchu Tian",
"Jiawei Han"
] | Claims made by individuals or entities are oftentimes nuanced and cannot be clearly labeled as entirely "true" or "false" -- as is frequently the case with scientific and political claims. However, a claim (e.g., "vaccine A is better than vaccine B") can be dissected into its integral aspects and sub-aspects (e.g., efficacy, safety, distribution), which are individually easier to validate. This enables a more comprehensive, structured response that provides a well-rounded perspective on a given problem while also allowing the reader to prioritize specific angles of interest within the claim (e.g., safety towards children). Thus, we propose ClaimSpect, a retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a claim and enriching them with corpus-specific perspectives. This structure hierarchically partitions an input corpus to retrieve relevant segments, which assist in discovering new sub-aspects. Moreover, these segments enable the discovery of varying perspectives towards an aspect of the claim (e.g., support, neutral, or oppose) and their respective prevalence (e.g., "how many biomedical papers believe vaccine A is more transportable than B?"). We apply ClaimSpect to a wide variety of real-world scientific and political claims featured in our constructed dataset, showcasing its robustness and accuracy in deconstructing a nuanced claim and representing perspectives within a corpus. Through real-world case studies and human evaluation, we validate its effectiveness over multiple baselines. |
|
2025-06-13T00:00:00 | 2506.10737 | TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora | [
"Priyanka Kargupta",
"Nan Zhang",
"Yunyi Zhang",
"Rui Zhang",
"Prasenjit Mitra",
"Jiawei Han"
] | The rapid evolution of scientific fields introduces challenges in organizing and retrieving scientific literature. While expert-curated taxonomies have traditionally addressed this need, the process is time-consuming and expensive. Furthermore, recent automatic taxonomy construction methods either (1) over-rely on a specific corpus, sacrificing generalizability, or (2) depend heavily on the general knowledge of large language models (LLMs) contained within their pre-training datasets, often overlooking the dynamic nature of evolving scientific domains. Additionally, these approaches fail to account for the multi-faceted nature of scientific literature, where a single research paper may contribute to multiple dimensions (e.g., methodology, new tasks, evaluation metrics, benchmarks). To address these gaps, we propose TaxoAdapt, a framework that dynamically adapts an LLM-generated taxonomy to a given corpus across multiple dimensions. TaxoAdapt performs iterative hierarchical classification, expanding both the taxonomy width and depth based on corpus' topical distribution. We demonstrate its state-of-the-art performance across a diverse set of computer science conferences over the years to showcase its ability to structure and capture the evolution of scientific fields. As a multidimensional method, TaxoAdapt generates taxonomies that are 26.51% more granularity-preserving and 50.41% more coherent than the most competitive baselines judged by LLMs. |
|
2025-06-13T00:00:00 | 2506.10674 | TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving | [
"Vincenzo Colle",
"Mohamed Sana",
"Nicola Piovesan",
"Antonio De Domenico",
"Fadhel Ayed",
"Merouane Debbah"
] | The increasing adoption of artificial intelligence in telecommunications has raised interest in the capability of Large Language Models (LLMs) to address domain-specific, mathematically intensive tasks. Although recent advancements have improved the performance of LLMs in general mathematical reasoning, their effectiveness within specialized domains, such as signal processing, network optimization, and performance analysis, remains largely unexplored. To address this gap, we introduce TeleMath, the first benchmark dataset specifically designed to evaluate LLM performance in solving mathematical problems with numerical solutions in the telecommunications domain. Comprising 500 question-answer (QnA) pairs, TeleMath covers a wide spectrum of topics in the telecommunications field. This paper outlines the proposed QnAs generation pipeline, starting from a selected seed of problems crafted by Subject Matter Experts. The evaluation of a wide range of open-source LLMs reveals that best performance on TeleMath is achieved by recent models explicitly designed for mathematical or logical reasoning. In contrast, general-purpose models, even those with a large number of parameters, often struggle with these challenges. We have released the dataset and the evaluation code to ease result reproducibility and support future research. |
|
2025-06-13T00:00:00 | 2506.10920 | Decomposing MLP Activations into Interpretable Features via Semi-Nonnegative Matrix Factorization | [
"Or Shafran",
"Atticus Geiger",
"Mor Geva"
] | A central goal for mechanistic interpretability has been to identify the right units of analysis in large language models (LLMs) that causally explain their outputs. While early work focused on individual neurons, evidence that neurons often encode multiple concepts has motivated a shift toward analyzing directions in activation space. A key question is how to find directions that capture interpretable features in an unsupervised manner. Current methods rely on dictionary learning with sparse autoencoders (SAEs), commonly trained over residual stream activations to learn directions from scratch. However, SAEs often struggle in causal evaluations and lack intrinsic interpretability, as their learning is not explicitly tied to the computations of the model. Here, we tackle these limitations by directly decomposing MLP activations with semi-nonnegative matrix factorization (SNMF), such that the learned features are (a) sparse linear combinations of co-activated neurons, and (b) mapped to their activating inputs, making them directly interpretable. Experiments on Llama 3.1, Gemma 2 and GPT-2 show that SNMF derived features outperform SAEs and a strong supervised baseline (difference-in-means) on causal steering, while aligning with human-interpretable concepts. Further analysis reveals that specific neuron combinations are reused across semantically-related features, exposing a hierarchical structure in the MLP's activation space. Together, these results position SNMF as a simple and effective tool for identifying interpretable features and dissecting concept representations in LLMs. |
|
2025-06-13T00:00:00 | 2506.10600 | EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence | [
"Wang Xinjie",
"Liu Liu",
"Cao Yu",
"Wu Ruiqi",
"Qin Wenkang",
"Wang Dehui",
"Sui Wei",
"Su Zhizhong"
] | Constructing a physically realistic and accurately scaled simulated 3D world is crucial for the training and evaluation of embodied intelligence tasks. The diversity, realism, low cost accessibility and affordability of 3D data assets are critical for achieving generalization and scalability in embodied AI. However, most current embodied intelligence tasks still rely heavily on traditional 3D computer graphics assets manually created and annotated, which suffer from high production costs and limited realism. These limitations significantly hinder the scalability of data driven approaches. We present EmbodiedGen, a foundational platform for interactive 3D world generation. It enables the scalable generation of high-quality, controllable and photorealistic 3D assets with accurate physical properties and real-world scale in the Unified Robotics Description Format (URDF) at low cost. These assets can be directly imported into various physics simulation engines for fine-grained physical control, supporting downstream tasks in training and evaluation. EmbodiedGen is an easy-to-use, full-featured toolkit composed of six key modules: Image-to-3D, Text-to-3D, Texture Generation, Articulated Object Generation, Scene Generation and Layout Generation. EmbodiedGen generates diverse and interactive 3D worlds composed of generative 3D assets, leveraging generative AI to address the challenges of generalization and evaluation to the needs of embodied intelligence related research. Code is available at https://horizonrobotics.github.io/robot_lab/embodied_gen/index.html. |
|
2025-06-13T00:00:00 | 2506.06952 | LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer | [
"Ying Shen",
"Zhiyang Xu",
"Jiuhai Chen",
"Shizhe Diao",
"Jiaxin Zhang",
"Yuguang Yao",
"Joy Rimchala",
"Ismini Lourentzou",
"Lifu Huang"
] | Recent advances in multimodal foundation models unifying image understanding and generation have opened exciting avenues for tackling a wide range of vision-language tasks within a single framework. Despite progress, existing unified models typically require extensive pretraining and struggle to achieve the same level of performance compared to models dedicated to each task. Additionally, many of these models suffer from slow image generation speeds, limiting their practical deployment in real-time or resource-constrained settings. In this work, we propose Layerwise Timestep-Expert Flow-based Transformer (LaTtE-Flow), a novel and efficient architecture that unifies image understanding and generation within a single multimodal model. LaTtE-Flow builds upon powerful pretrained Vision-Language Models (VLMs) to inherit strong multimodal understanding capabilities, and extends them with a novel Layerwise Timestep Experts flow-based architecture for efficient image generation. LaTtE-Flow distributes the flow-matching process across specialized groups of Transformer layers, each responsible for a distinct subset of timesteps. This design significantly improves sampling efficiency by activating only a small subset of layers at each sampling timestep. To further enhance performance, we propose a Timestep-Conditioned Residual Attention mechanism for efficient information reuse across layers. Experiments demonstrate that LaTtE-Flow achieves strong performance on multimodal understanding tasks, while achieving competitive image generation quality with around 6x faster inference speed compared to recent unified multimodal models. |
|
2025-06-13T00:00:00 | 2506.10911 | NoLoCo: No-all-reduce Low Communication Training Method for Large Models | [
"Jari Kolehmainen",
"Nikolay Blagoev",
"John Donaghy",
"Oğuzhan Ersoy",
"Christopher Nies"
] | Training large language models is generally done via optimization methods on clusters containing tens of thousands of accelerators, communicating over a high-bandwidth interconnect. Scaling up these clusters is expensive and can become impractical, imposing limits on the size of models that can be trained. Several recent studies have proposed training methods that are less communication intensive, avoiding the need for a highly connected compute cluster. These state-of-the-art low communication training methods still employ a synchronization step for model parameters, which, when performed over all model replicas, can become costly on a low-bandwidth network. In this work, we propose a novel optimization method, NoLoCo, that does not explicitly synchronize all model parameters during training and, as a result, does not require any collective communication. NoLoCo implicitly synchronizes model weights via a novel variant of the Nesterov momentum optimizer by partially averaging model weights with a randomly selected other one. We provide both a theoretical convergence analysis for our proposed optimizer as well as empirical results from language model training. We benchmark NoLoCo on a wide range of accelerator counts and model sizes, between 125M to 6.8B parameters. Our method requires significantly less communication overhead than fully sharded data parallel training or even widely used low communication training method, DiLoCo. The synchronization step itself is estimated to be one magnitude faster than the all-reduce used in DiLoCo for few hundred accelerators training over the internet. We also do not have any global blocking communication that reduces accelerator idling time. Compared to DiLoCo, we also observe up to 4% faster convergence rate with wide range of model sizes and accelerator counts. |
|
2025-06-13T00:00:00 | 2506.09250 | Comment on The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity | [
"C. Opus",
"A. Lawsen"
] | Shojaee et al. (2025) report that Large Reasoning Models (LRMs) exhibit "accuracy collapse" on planning puzzles beyond certain complexity thresholds. We demonstrate that their findings primarily reflect experimental design limitations rather than fundamental reasoning failures. Our analysis reveals three critical issues: (1) Tower of Hanoi experiments systematically exceed model output token limits at reported failure points, with models explicitly acknowledging these constraints in their outputs; (2) The authors' automated evaluation framework fails to distinguish between reasoning failures and practical constraints, leading to misclassification of model capabilities; (3) Most concerningly, their River Crossing benchmarks include mathematically impossible instances for N > 5 due to insufficient boat capacity, yet models are scored as failures for not solving these unsolvable problems. When we control for these experimental artifacts, by requesting generating functions instead of exhaustive move lists, preliminary experiments across multiple models indicate high accuracy on Tower of Hanoi instances previously reported as complete failures. These findings highlight the importance of careful experimental design when evaluating AI reasoning capabilities. |
|
2025-06-16T00:00:00 | 2506.11924 | Aligned Novel View Image and Geometry Synthesis via Cross-modal Attention Instillation | [
"Min-Seop Kwak",
"Junho Kim",
"Sangdoo Yun",
"Dongyoon Han",
"Taekyoung Kim",
"Seungryong Kim",
"Jin-Hwa Kim"
] | We introduce a diffusion-based framework that performs aligned novel view image and geometry generation via a warping-and-inpainting methodology. Unlike prior methods that require dense posed images or pose-embedded generative models limited to in-domain views, our method leverages off-the-shelf geometry predictors to predict partial geometries viewed from reference images, and formulates novel-view synthesis as an inpainting task for both image and geometry. To ensure accurate alignment between generated images and geometry, we propose cross-modal attention distillation, where attention maps from the image diffusion branch are injected into a parallel geometry diffusion branch during both training and inference. This multi-task approach achieves synergistic effects, facilitating geometrically robust image synthesis as well as well-defined geometry prediction. We further introduce proximity-based mesh conditioning to integrate depth and normal cues, interpolating between point cloud and filtering erroneously predicted geometry from influencing the generation process. Empirically, our method achieves high-fidelity extrapolative view synthesis on both image and geometry across a range of unseen scenes, delivers competitive reconstruction quality under interpolation settings, and produces geometrically aligned colored point clouds for comprehensive 3D completion. Project page is available at https://cvlab-kaist.github.io/MoAI. |
|
2025-06-16T00:00:00 | 2506.11928 | LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming? | [
"Zihan Zheng",
"Zerui Cheng",
"Zeyu Shen",
"Shang Zhou",
"Kaiyuan Liu",
"Hansen He",
"Dongruixuan Li",
"Stanley Wei",
"Hangyi Hao",
"Jianzhu Yao",
"Peiyao Sheng",
"Zixuan Wang",
"Wenhao Chai",
"Aleksandra Korolova",
"Peter Henderson",
"Sanjeev Arora",
"Pramod Viswanath",
"Jingbo Shang",
"Saining Xie"
] | Recent reports claim that large language models (LLMs) now outperform elite humans in competitive programming. Drawing on knowledge from a group of medalists in international algorithmic contests, we revisit this claim, examining how LLMs differ from human experts and where limitations still remain. We introduce LiveCodeBench Pro, a benchmark composed of problems from Codeforces, ICPC, and IOI that are continuously updated to reduce the likelihood of data contamination. A team of Olympiad medalists annotates every problem for algorithmic categories and conducts a line-by-line analysis of failed model-generated submissions. Using this new data and benchmark, we find that frontier models still have significant limitations: without external tools, the best model achieves only 53% pass@1 on medium-difficulty problems and 0% on hard problems, domains where expert humans still excel. We also find that LLMs succeed at implementation-heavy problems but struggle with nuanced algorithmic reasoning and complex case analysis, often generating confidently incorrect justifications. High performance appears largely driven by implementation precision and tool augmentation, not superior reasoning. LiveCodeBench Pro thus highlights the significant gap to human grandmaster levels, while offering fine-grained diagnostics to steer future improvements in code-centric LLM reasoning. |
|
2025-06-16T00:00:00 | 2506.11702 | Configurable Preference Tuning with Rubric-Guided Synthetic Data | [
"Víctor Gallego"
] | https://github.com/vicgalle/configurable-preference-tuning | Models of human feedback for AI alignment, such as those underpinning Direct Preference Optimization (DPO), often bake in a singular, static set of preferences, limiting adaptability. This paper challenges the assumption of monolithic preferences by introducing Configurable Preference Tuning (CPT), a novel framework for endowing language models with the ability to dynamically adjust their behavior based on explicit, human-interpretable directives. CPT leverages synthetically generated preference data, conditioned on system prompts derived from structured, fine-grained rubrics that define desired attributes like writing style. By fine-tuning with these rubric-guided preferences, the LLM learns to modulate its outputs at inference time in response to the system prompt, without retraining. This approach not only offers fine-grained control but also provides a mechanism for modeling more nuanced and context-dependent human feedback. Several experimental artifacts, such as training code, generated datasets and fine-tuned models are released at https://github.com/vicgalle/configurable-preference-tuning |
2025-06-16T00:00:00 | 2506.09427 | A High-Quality Dataset and Reliable Evaluation for Interleaved Image-Text Generation | [
"Yukang Feng",
"Jianwen Sun",
"Chuanhao Li",
"Zizhen Li",
"Jiaxin Ai",
"Fanrui Zhang",
"Yifan Chang",
"Sizhuo Zhou",
"Shenglin Zhang",
"Yu Dai",
"Kaipeng Zhang"
] | Recent advancements in Large Multimodal Models (LMMs) have significantly improved multimodal understanding and generation. However, these models still struggle to generate tightly interleaved image-text outputs, primarily due to the limited scale, quality and instructional richness of current training datasets. To address this, we introduce InterSyn, a large-scale multimodal dataset constructed using our Self-Evaluation with Iterative Refinement (SEIR) method. InterSyn features multi-turn, instruction-driven dialogues with tightly interleaved imagetext responses, providing rich object diversity and rigorous automated quality refinement, making it well-suited for training next-generation instruction-following LMMs. Furthermore, to address the lack of reliable evaluation tools capable of assessing interleaved multimodal outputs, we introduce SynJudge, an automatic evaluation model designed to quantitatively assess multimodal outputs along four dimensions: text content, image content, image quality, and image-text synergy. Experimental studies show that the SEIR method leads to substantially higher dataset quality compared to an otherwise identical process without refinement. Moreover, LMMs trained on InterSyn achieve uniform performance gains across all evaluation metrics, confirming InterSyn's utility for advancing multimodal systems. |
|
2025-06-16T00:00:00 | 2506.10892 | The Diffusion Duality | [
"Subham Sekhar Sahoo",
"Justin Deschenaux",
"Aaron Gokaslan",
"Guanghan Wang",
"Justin Chiu",
"Volodymyr Kuleshov"
] | Uniform-state discrete diffusion models hold the promise of fast text generation due to their inherent ability to self-correct. However, they are typically outperformed by autoregressive models and masked diffusion models. In this work, we narrow this performance gap by leveraging a key insight: Uniform-state diffusion processes naturally emerge from an underlying Gaussian diffusion. Our method, Duo, transfers powerful techniques from Gaussian diffusion to improve both training and sampling. First, we introduce a curriculum learning strategy guided by the Gaussian process, doubling training speed by reducing variance. Models trained with curriculum learning surpass autoregressive models in zero-shot perplexity on 3 of 7 benchmarks. Second, we present Discrete Consistency Distillation, which adapts consistency distillation from the continuous to the discrete setting. This algorithm unlocks few-step generation in diffusion language models by accelerating sampling by two orders of magnitude. We provide the code and model checkpoints on the project page: http://s-sahoo.github.io/duo |
|
2025-06-16T00:00:00 | 2506.09366 | SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending | [
"Yuxuan Kuang",
"Haoran Geng",
"Amine Elhafsi",
"Tan-Dzung Do",
"Pieter Abbeel",
"Jitendra Malik",
"Marco Pavone",
"Yue Wang"
] | Humanoid robots hold significant potential in accomplishing daily tasks across diverse environments thanks to their flexibility and human-like morphology. Recent works have made significant progress in humanoid whole-body control and loco-manipulation leveraging optimal control or reinforcement learning. However, these methods require tedious task-specific tuning for each task to achieve satisfactory behaviors, limiting their versatility and scalability to diverse tasks in daily scenarios. To that end, we introduce SkillBlender, a novel hierarchical reinforcement learning framework for versatile humanoid loco-manipulation. SkillBlender first pretrains goal-conditioned task-agnostic primitive skills, and then dynamically blends these skills to accomplish complex loco-manipulation tasks with minimal task-specific reward engineering. We also introduce SkillBench, a parallel, cross-embodiment, and diverse simulated benchmark containing three embodiments, four primitive skills, and eight challenging loco-manipulation tasks, accompanied by a set of scientific evaluation metrics balancing accuracy and feasibility. Extensive simulated experiments show that our method significantly outperforms all baselines, while naturally regularizing behaviors to avoid reward hacking, resulting in more accurate and feasible movements for diverse loco-manipulation tasks in our daily scenarios. Our code and benchmark will be open-sourced to the community to facilitate future research. Project page: https://usc-gvl.github.io/SkillBlender-web/. |
|
2025-06-16T00:00:00 | 2506.08477 | Detecting Harmful Memes with Decoupled Understanding and Guided CoT Reasoning | [
"Fengjun Pan",
"Anh Tuan Luu",
"Xiaobao Wu"
] | Detecting harmful memes is essential for maintaining the integrity of online environments. However, current approaches often struggle with resource efficiency, flexibility, or explainability, limiting their practical deployment in content moderation systems. To address these challenges, we introduce U-CoT+, a novel framework for harmful meme detection. Instead of relying solely on prompting or fine-tuning multimodal models, we first develop a high-fidelity meme-to-text pipeline that converts visual memes into detail-preserving textual descriptions. This design decouples meme interpretation from meme classification, thus avoiding immediate reasoning over complex raw visual content and enabling resource-efficient harmful meme detection with general large language models (LLMs). Building on these textual descriptions, we further incorporate targeted, interpretable human-crafted guidelines to guide models' reasoning under zero-shot CoT prompting. As such, this framework allows for easy adaptation to different harmfulness detection criteria across platforms, regions, and over time, offering high flexibility and explainability. Extensive experiments on seven benchmark datasets validate the effectiveness of our framework, highlighting its potential for explainable and low-resource harmful meme detection using small-scale LLMs. Codes and data are available at: https://anonymous.4open.science/r/HMC-AF2B/README.md. |
|
2025-06-16T00:00:00 | 2506.09600 | Effective Red-Teaming of Policy-Adherent Agents | [
"Itay Nakash",
"George Kour",
"Koren Lazar",
"Matan Vetzler",
"Guy Uziel",
"Ateret Anaby-Tavor"
] | Task-oriented LLM-based agents are increasingly used in domains with strict policies, such as refund eligibility or cancellation rules. The challenge lies in ensuring that the agent consistently adheres to these rules and policies, appropriately refusing any request that would violate them, while still maintaining a helpful and natural interaction. This calls for the development of tailored design and evaluation methodologies to ensure agent resilience against malicious user behavior. We propose a novel threat model that focuses on adversarial users aiming to exploit policy-adherent agents for personal benefit. To address this, we present CRAFT, a multi-agent red-teaming system that leverages policy-aware persuasive strategies to undermine a policy-adherent agent in a customer-service scenario, outperforming conventional jailbreak methods such as DAN prompts, emotional manipulation, and coercive. Building upon the existing tau-bench benchmark, we introduce tau-break, a complementary benchmark designed to rigorously assess the agent's robustness against manipulative user behavior. Finally, we evaluate several straightforward yet effective defense strategies. While these measures provide some protection, they fall short, highlighting the need for stronger, research-driven safeguards to protect policy-adherent agents from adversarial attacks |
|
2025-06-16T00:00:00 | 2506.08592 | Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings | [
"Liyan Xu",
"Zhenlin Su",
"Mo Yu",
"Jiangnan Li",
"Fandong Meng",
"Jie Zhou"
] | https://github.com/lxucs/CapRetrieval | This work focuses on an observed limitation of text encoders: embeddings may not be able to recognize fine-grained entities or events within the semantics, resulting in failed dense retrieval on even simple cases. To examine such behaviors, we first introduce a new evaluation dataset in Chinese, named CapRetrieval, whose passages are image captions, and queries are phrases inquiring entities or events in various forms. Zero-shot evaluation suggests that encoders may fail on these fine-grained matching, regardless of training sources or model sizes. Aiming for enhancement, we proceed to finetune encoders with our proposed data generation strategies, which obtains the best performance on CapRetrieval. Within this process, we further identify an issue of granularity dilemma, a challenge for embeddings to express fine-grained salience while aligning with overall semantics. Our dataset, code and models in this work are publicly released at https://github.com/lxucs/CapRetrieval. |
2025-06-16T00:00:00 | 2506.11997 | pLSTM: parallelizable Linear Source Transition Mark networks | [
"Korbinian Pöppel",
"Richard Freinschlag",
"Thomas Schmied",
"Wei Lin",
"Sepp Hochreiter"
] | https://github.com/ml-jku/plstm_experiments | Modern recurrent architectures, such as xLSTM and Mamba, have recently challenged the Transformer in language modeling. However, their structure constrains their applicability to sequences only or requires processing multi-dimensional data structures, such as images or molecular graphs, in a pre-defined sequential order. In contrast, Multi-Dimensional RNNs (MDRNNs) are well suited for data with a higher level structure, like 2D grids, trees, and directed acyclic graphs (DAGs). In this work, we extend the notion of multi-dimensionality to linear RNNs. We introduce parallelizable Linear Source Transition Mark networks (pLSTMs) using Source, Transition, and Mark gates that act on the line graph of a general DAG. This enables parallelization in analogy to parallel associative scans and the chunkwise-recurrent form of sequential linear RNNs, but for DAGs. For regular grids (1D and 2D), like images, this scheme can be efficiently implemented using einsum operations, concatenations, and padding in logarithmic time. pLSTMs tackle the vanishing/exploding activation/gradient problem for long distances in DAGs via two distinct modes: a directed propagation mode (P-mode) and a diffusive distribution mode (D-mode). To showcase the long-range capabilities of pLSTM, we introduce arrow-pointing extrapolation as a synthetic computer vision task that contains long-distance directional information. We demonstrate that pLSTMs generalize well to larger image sizes, whereas Transformers struggle to extrapolate. On established molecular graph and computer vision benchmarks, pLSTMs also show strong performance. Code and Datasets are available at: https://github.com/ml-jku/plstm_experiments. |
2025-06-16T00:00:00 | 2506.07464 | DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO | [
"Jinyoung Park",
"Jeehye Na",
"Jinyoung Kim",
"Hyunwoo J. Kim"
] | Recent works have demonstrated the effectiveness of reinforcement learning (RL)-based post-training in enhancing the reasoning capabilities of large language models (LLMs). In particular, Group Relative Policy Optimization (GRPO) has shown impressive success by employing a PPO-style reinforcement algorithm with group-based normalized rewards. However, the application of GRPO to Video Large Language Models (Video LLMs) has been less studied. In this paper, we explore GRPO for video LLMs and identify two primary issues that impede its effective learning: (1) reliance on safeguards, and (2) the vanishing advantage problem. To mitigate these challenges, we propose DeepVideo-R1, a video large language model trained with our proposed Reg-GRPO (Regressive GRPO) and difficulty-aware data augmentation strategy. Reg-GRPO reformulates the GRPO objective as a regression task, directly predicting the advantage in GRPO. This design eliminates the need for safeguards like clipping and min functions, thereby facilitating more direct policy guidance by aligning the model with the advantage values. We also design the difficulty-aware data augmentation strategy that dynamically augments training samples at solvable difficulty levels, fostering diverse and informative reward signals. Our comprehensive experiments show that DeepVideo-R1 significantly improves video reasoning performance across multiple video reasoning benchmarks. |
|
2025-06-16T00:00:00 | 2506.11130 | A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data | [
"Cheng Kang Chou",
"Chan-Jan Hsu",
"Ho-Lam Chung",
"Liang-Hsuan Tseng",
"Hsi-Chun Cheng",
"Yu-Kuan Fu",
"Kuan Po Huang",
"Hung-Yi Lee"
] | We propose a self-refining framework that enhances ASR performance with only unlabeled datasets. The process starts with an existing ASR model generating pseudo-labels on unannotated speech, which are then used to train a high-fidelity text-to-speech (TTS) system. Then, synthesized speech text pairs are bootstrapped into the original ASR system, completing the closed-loop self-improvement cycle. We demonstrated the effectiveness of the framework on Taiwanese Mandarin speech. Leveraging 6,000 hours of unlabeled speech, a moderate amount of text data, and synthetic content from the AI models, we adapt Whisper-large-v2 into a specialized model, Twister. Twister reduces error rates by up to 20% on Mandarin and 50% on Mandarin-English code-switching benchmarks compared to Whisper. Results highlight the framework as a compelling alternative to pseudo-labeling self-distillation approaches and provides a practical pathway for improving ASR performance in low-resource or domain-specific settings. |
|
2025-06-16T00:00:00 | 2506.08915 | Inherently Faithful Attention Maps for Vision Transformers | [
"Ananthu Aniraj",
"Cassio F. Dantas",
"Dino Ienco",
"Diego Marcos"
] | We introduce an attention-based method that uses learned binary attention masks to ensure that only attended image regions influence the prediction. Context can strongly affect object perception, sometimes leading to biased representations, particularly when objects appear in out-of-distribution backgrounds. At the same time, many image-level object-centric tasks require identifying relevant regions, often requiring context. To address this conundrum, we propose a two-stage framework: stage 1 processes the full image to discover object parts and identify task-relevant regions, while stage 2 leverages input attention masking to restrict its receptive field to these regions, enabling a focused analysis while filtering out potentially spurious information. Both stages are trained jointly, allowing stage 2 to refine stage 1. Extensive experiments across diverse benchmarks demonstrate that our approach significantly improves robustness against spurious correlations and out-of-distribution backgrounds. |
|
2025-06-16T00:00:00 | 2506.10128 | ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs | [
"Xiyao Wang",
"Zhengyuan Yang",
"Chao Feng",
"Yongyuan Liang",
"Yuhang Zhou",
"Xiaoyu Liu",
"Ziyi Zang",
"Ming Li",
"Chung-Ching Lin",
"Kevin Lin",
"Linjie Li",
"Furong Huang",
"Lijuan Wang"
] | Reinforcement learning (RL) has shown great effectiveness for fine-tuning large language models (LLMs) using tasks that are challenging yet easily verifiable, such as math reasoning or code generation. However, extending this success to visual perception in vision-language models (VLMs) has been impeded by the scarcity of vision-centric tasks that are simultaneously challenging and unambiguously verifiable. To this end, we introduce ViCrit (Visual Caption Hallucination Critic), an RL proxy task that trains VLMs to localize a subtle, synthetic visual hallucination injected into paragraphs of human-written image captions. Starting from a 200-word captions, we inject a single, subtle visual description error-altering a few words on objects, attributes, counts, or spatial relations-and task the model to pinpoint the corrupted span given the image and the modified caption. This formulation preserves the full perceptual difficulty while providing a binary, exact-match reward that is easy to compute and unambiguous. Models trained with the ViCrit Task exhibit substantial gains across a variety of VL benchmarks. Crucially, the improvements transfer beyond natural-image training data to abstract image reasoning and visual math, showing promises of learning to perceive rather than barely memorizing seen objects. To facilitate evaluation, we further introduce ViCrit-Bench, a category-balanced diagnostic benchmark that systematically probes perception errors across diverse image domains and error types. Together, our results demonstrate that fine-grained hallucination criticism is an effective and generalizable objective for enhancing visual perception in VLMs. |
|
2025-06-16T00:00:00 | 2506.11886 | Beyond Homogeneous Attention: Memory-Efficient LLMs via Fourier-Approximated KV Cache | [
"Xiaoran Liu",
"Siyang He",
"Qiqi Wang",
"Ruixiao Li",
"Yuerong Song",
"Zhigeng Liu",
"Linlin Li",
"Qun Liu",
"Zengfeng Huang",
"Qipeng Guo",
"Ziwei He",
"Xipeng Qiu"
] | Large Language Models struggle with memory demands from the growing Key-Value (KV) cache as context lengths increase. Existing compression methods homogenize head dimensions or rely on attention-guided token pruning, often sacrificing accuracy or introducing computational overhead. We propose FourierAttention, a training-free framework that exploits the heterogeneous roles of transformer head dimensions: lower dimensions prioritize local context, while upper ones capture long-range dependencies. By projecting the long-context-insensitive dimensions onto orthogonal Fourier bases, FourierAttention approximates their temporal evolution with fixed-length spectral coefficients. Evaluations on LLaMA models show that FourierAttention achieves the best long-context accuracy on LongBench and Needle-In-A-Haystack (NIAH). Besides, a custom Triton kernel, FlashFourierAttention, is designed to optimize memory via streamlined read-write operations, enabling efficient deployment without performance compromise. |
|
2025-06-16T00:00:00 | 2506.08989 | SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning | [
"Xiao Liang",
"Zhong-Zhi Li",
"Yeyun Gong",
"Yang Wang",
"Hengyuan Zhang",
"Yelong Shen",
"Ying Nian Wu",
"Weizhu Chen"
] | Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for training large language models (LLMs) on complex reasoning tasks, such as mathematical problem solving. A prerequisite for the scalability of RLVR is a high-quality problem set with precise and verifiable answers. However, the scarcity of well-crafted human-labeled math problems and limited-verification answers in existing distillation-oriented synthetic datasets limit their effectiveness in RL. Additionally, most problem synthesis strategies indiscriminately expand the problem set without considering the model's capabilities, leading to low efficiency in generating useful questions. To mitigate this issue, we introduce a Self-aware Weakness-driven problem Synthesis framework (SwS) that systematically identifies model deficiencies and leverages them for problem augmentation. Specifically, we define weaknesses as questions that the model consistently fails to learn through its iterative sampling during RL training. We then extract the core concepts from these failure cases and synthesize new problems to strengthen the model's weak areas in subsequent augmented training, enabling it to focus on and gradually overcome its weaknesses. Without relying on external knowledge distillation, our framework enables robust generalization byempowering the model to self-identify and address its weaknesses in RL, yielding average performance gains of 10.0% and 7.7% on 7B and 32B models across eight mainstream reasoning benchmarks. |
|
2025-06-16T00:00:00 | 2506.11136 | JAFAR: Jack up Any Feature at Any Resolution | [
"Paul Couairon",
"Loick Chambon",
"Louis Serrano",
"Jean-Emmanuel Haugeard",
"Matthieu Cord",
"Nicolas Thome"
] | Foundation Vision Encoders have become essential for a wide range of dense vision tasks. However, their low-resolution spatial feature outputs necessitate feature upsampling to produce the high-resolution modalities required for downstream tasks. In this work, we introduce JAFAR, a lightweight and flexible feature upsampler that enhances the spatial resolution of visual features from any Foundation Vision Encoder to an arbitrary target resolution. JAFAR employs an attention-based module designed to promote semantic alignment between high-resolution queries, derived from low-level image features, and semantically enriched low-resolution keys, using Spatial Feature Transform (SFT) modulation. Notably, despite the absence of high-resolution supervision, we demonstrate that learning at low upsampling ratios and resolutions generalizes remarkably well to significantly higher output scales. Extensive experiments show that JAFAR effectively recovers fine-grained spatial details and consistently outperforms existing feature upsampling methods across a diverse set of downstream tasks. Project page at https://jafar-upsampler.github.io |
|
2025-06-16T00:00:00 | 2506.11274 | Learning a Continue-Thinking Token for Enhanced Test-Time Scaling | [
"Liran Ringel",
"Elad Tolochinsky",
"Yaniv Romano"
] | Test-time scaling has emerged as an effective approach for improving language model performance by utilizing additional compute at inference time. Recent studies have shown that overriding end-of-thinking tokens (e.g., replacing "</think>" with "Wait") can extend reasoning steps and improve accuracy. In this work, we explore whether a dedicated continue-thinking token can be learned to trigger extended reasoning. We augment a distilled version of DeepSeek-R1 with a single learned "<|continue-thinking|>" token, training only its embedding via reinforcement learning while keeping the model weights frozen. Our experiments show that this learned token achieves improved accuracy on standard math benchmarks compared to both the baseline model and a test-time scaling approach that uses a fixed token (e.g., "Wait") for budget forcing. In particular, we observe that in cases where the fixed-token approach enhances the base model's accuracy, our method achieves a markedly greater improvement. For example, on the GSM8K benchmark, the fixed-token approach yields a 1.3% absolute improvement in accuracy, whereas our learned-token method achieves a 4.2% improvement over the base model that does not use budget forcing. |
|
2025-06-16T00:00:00 | 2506.10082 | LoRA-Edit: Controllable First-Frame-Guided Video Editing via Mask-Aware LoRA Fine-Tuning | [
"Chenjian Gao",
"Lihe Ding",
"Xin Cai",
"Zhanpeng Huang",
"Zibin Wang",
"Tianfan Xue"
] | Video editing using diffusion models has achieved remarkable results in generating high-quality edits for videos. However, current methods often rely on large-scale pretraining, limiting flexibility for specific edits. First-frame-guided editing provides control over the first frame, but lacks flexibility over subsequent frames. To address this, we propose a mask-based LoRA (Low-Rank Adaptation) tuning method that adapts pretrained Image-to-Video (I2V) models for flexible video editing. Our approach preserves background regions while enabling controllable edits propagation. This solution offers efficient and adaptable video editing without altering the model architecture. To better steer this process, we incorporate additional references, such as alternate viewpoints or representative scene states, which serve as visual anchors for how content should unfold. We address the control challenge using a mask-driven LoRA tuning strategy that adapts a pre-trained image-to-video model to the editing context. The model must learn from two distinct sources: the input video provides spatial structure and motion cues, while reference images offer appearance guidance. A spatial mask enables region-specific learning by dynamically modulating what the model attends to, ensuring that each area draws from the appropriate source. Experimental results show our method achieves superior video editing performance compared to state-of-the-art methods. |
|
2025-06-16T00:00:00 | 2506.11474 | Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards | [
"Jaehoon Yun",
"Jiwoong Sohn",
"Jungwoo Park",
"Hyunjae Kim",
"Xiangru Tang",
"Yanjun Shao",
"Yonghoe Koo",
"Minhyeok Ko",
"Qingyu Chen",
"Mark Gerstein",
"Michael Moor",
"Jaewoo Kang"
] | Large language models have shown promise in clinical decision making, but current approaches struggle to localize and correct errors at specific steps of the reasoning process. This limitation is critical in medicine, where identifying and addressing reasoning errors is essential for accurate diagnosis and effective patient care. We introduce Med-PRM, a process reward modeling framework that leverages retrieval-augmented generation to verify each reasoning step against established medical knowledge bases. By verifying intermediate reasoning steps with evidence retrieved from clinical guidelines and literature, our model can precisely assess the reasoning quality in a fine-grained manner. Evaluations on five medical QA benchmarks and two open-ended diagnostic tasks demonstrate that Med-PRM achieves state-of-the-art performance, with improving the performance of base models by up to 13.50% using Med-PRM. Moreover, we demonstrate the generality of Med-PRM by integrating it in a plug-and-play fashion with strong policy models such as Meerkat, achieving over 80\% accuracy on MedQA for the first time using small-scale models of 8 billion parameters. Our code and data are available at: https://med-prm.github.io/ |
|
2025-06-16T00:00:00 | 2506.11116 | Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models | [
"Jijie Li",
"Li Du",
"Hanyu Zhao",
"Bo-wen Zhang",
"Liangdong Wang",
"Boyan Gao",
"Guang Liu",
"Yonghua Lin"
] | Large Language Models (LLMs) demonstrate strong performance in real-world applications, yet existing open-source instruction datasets often concentrate on narrow domains, such as mathematics or coding, limiting generalization and widening the gap with proprietary models. To bridge this gap, we introduce Infinity-Instruct, a high-quality instruction dataset designed to enhance both foundational and chat capabilities of LLMs through a two-phase pipeline. In Phase 1, we curate 7.4M high-quality foundational instructions (InfInstruct-F-7.4M) from over 100M samples using hybrid data selection techniques. In Phase 2, we synthesize 1.5M high-quality chat instructions (InfInstruct-G-1.5M) through a two-stage process involving instruction selection, evolution, and diagnostic filtering. We empirically evaluate Infinity-Instruct by fine-tuning several open-source models, including Mistral, LLaMA, Qwen, and Yi, and observe substantial performance gains across both foundational and instruction following benchmarks, consistently surpassing official instruction-tuned counterparts. Notably, InfInstruct-LLaMA3.1-70B outperforms GPT-4-0314 by 8.6\% on instruction following tasks while achieving comparable foundational performance. These results underscore the synergy between foundational and chat training and offer new insights into holistic LLM development. Our datasethttps://huggingface.co/datasets/BAAI/Infinity-Instruct and codeshttps://gitee.com/li-touch/infinity-instruct have been publicly released. |
|
2025-06-16T00:00:00 | 2506.03857 | Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation | [
"Mingxuan Xia",
"Haobo Wang",
"Yixuan Li",
"Zewei Yu",
"Jindong Wang",
"Junbo Zhao",
"Runze Wu"
] | https://github.com/MingxuanXia/CanDist | Recently, Large Language Models (LLMs) have demonstrated significant potential for data annotation, markedly reducing the labor costs associated with downstream applications. However, existing methods mostly adopt an aggressive strategy by prompting LLM to determine a single gold label for each unlabeled sample. Due to the inherent uncertainty within LLMs, they often produce incorrect labels for difficult samples, severely compromising the data quality for downstream applications. Motivated by ambiguity aversion in human behaviors, we propose a novel candidate annotation paradigm wherein large language models are encouraged to output all possible labels when incurring uncertainty. To ensure unique labels are provided for downstream tasks, we develop a teacher-student framework CanDist that distills candidate annotations with a Small Language Model (SLM). We further provide a rigorous justification demonstrating that distilling candidate annotations from the teacher LLM offers superior theoretical guarantees compared to directly using single annotations. Extensive experiments across six text classification tasks validate the effectiveness of our proposed method. The source code is available at https://github.com/MingxuanXia/CanDist. |
2025-06-16T00:00:00 | 2506.11930 | Feedback Friction: LLMs Struggle to Fully Incorporate External Feedback | [
"Dongwei Jiang",
"Alvin Zhang",
"Andrew Wang",
"Nicholas Andrews",
"Daniel Khashabi"
] | Recent studies have shown LLMs possess some ability to improve their responses when given external feedback. However, it remains unclear how effectively and thoroughly these models can incorporate extrinsic feedback. In an ideal scenario, if LLMs receive near-perfect and complete feedback, we would expect them to fully integrate the feedback and change their incorrect answers to correct ones. In this paper, we systematically investigate LLMs' ability to incorporate feedback by designing a controlled experimental environment. For each problem, a solver model attempts a solution, then a feedback generator with access to near-complete ground-truth answers produces targeted feedback, after which the solver tries again. We evaluate this pipeline across a diverse range of tasks, including math reasoning, knowledge reasoning, scientific reasoning, and general multi-domain evaluations with state-of-the-art language models including Claude 3.7 (with and without extended thinking). Surprisingly, even under these near-ideal conditions, solver models consistently show resistance to feedback, a limitation that we term FEEDBACK FRICTION. To mitigate this limitation, we experiment with sampling-based strategies like progressive temperature increases and explicit rejection of previously attempted incorrect answers, which yield improvements but still fail to help models achieve target performance. We also perform a rigorous exploration of potential causes of FEEDBACK FRICTION, ruling out factors such as model overconfidence and data familiarity. We hope that highlighting this issue in LLMs and ruling out several apparent causes will help future research in self-improvement. |
|
2025-06-16T00:00:00 | 2506.10387 | Mirage-1: Augmenting and Updating GUI Agent with Hierarchical Multimodal Skills | [
"Yuquan Xie",
"Zaijing Li",
"Rui Shao",
"Gongwei Chen",
"Kaiwen Zhou",
"Yinchuan Li",
"Dongmei Jiang",
"Liqiang Nie"
] | Recent efforts to leverage the Multi-modal Large Language Model (MLLM) as GUI agents have yielded promising outcomes. However, these agents still struggle with long-horizon tasks in online environments, primarily due to insufficient knowledge and the inherent gap between offline and online domains. In this paper, inspired by how humans generalize knowledge in open-ended environments, we propose a Hierarchical Multimodal Skills (HMS) module to tackle the issue of insufficient knowledge. It progressively abstracts trajectories into execution skills, core skills, and ultimately meta-skills, providing a hierarchical knowledge structure for long-horizon task planning. To bridge the domain gap, we propose the Skill-Augmented Monte Carlo Tree Search (SA-MCTS) algorithm, which efficiently leverages skills acquired in offline environments to reduce the action search space during online tree exploration. Building on HMS, we propose Mirage-1, a multimodal, cross-platform, plug-and-play GUI agent. To validate the performance of Mirage-1 in real-world long-horizon scenarios, we constructed a new benchmark, AndroidLH. Experimental results show that Mirage-1 outperforms previous agents by 32\%, 19\%, 15\%, and 79\% on AndroidWorld, MobileMiniWob++, Mind2Web-Live, and AndroidLH, respectively. Project page: https://cybertronagent.github.io/Mirage-1.github.io/ |
|
2025-06-16T00:00:00 | 2506.10056 | Reward Models Enable Scalable Code Verification by Trading Accuracy for Throughput | [
"Gabriel Orlanski",
"Nicholas Roberts",
"Aws Albarghouthi",
"Frederic Sala"
] | The standard paradigm for solving coding tasks via large language models (LLMs) is to generate-then-rank programs, where the latter step uses a verifier in the ranking process. The growing consensus is that a comprehensive verifier (e.g., a full test suite) should be prioritized over an outcome reward model (ORM) whenever possible, with little consideration given to the trade-offs involved. We aim to challenge this assumption by systematically exploring the tradeoff between speed and accuracy. We find that ORMs play a crucial role in scaling verification through trading accuracy for speed, even when a comprehensive verifier is available. Their value becomes especially apparent when used in a generate-prune-then-rank approach, where a faster but less accurate verifier removes incorrect solutions prior to ranking -- leading to a system that is 11.65x faster while only being 8.33% less accurate than the full test suite. We analyze the generate-prune-then-rank approach and show that it works by filtering out incorrect but highly ranked solutions. These findings enable the design of scalable and accurate program ranking systems. |
|
2025-06-16T00:00:00 | 2506.09038 | AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions | [
"Polina Kirichenko",
"Mark Ibrahim",
"Kamalika Chaudhuri",
"Samuel J. Bell"
] | For Large Language Models (LLMs) to be reliably deployed in both everyday and high-stakes domains, knowing when not to answer is equally critical as answering correctly. Real-world user queries, which can be underspecified, ill-posed, or fundamentally unanswerable, require LLMs to reason about uncertainty and selectively abstain -- i.e., refuse to answer definitively. However, abstention remains understudied, without a systematic evaluation framework for modern LLMs. In this work, we introduce AbstentionBench, a large-scale benchmark for holistically evaluating abstention across 20 diverse datasets, including questions with unknown answers, underspecification, false premises, subjective interpretations, and outdated information. Evaluating 20 frontier LLMs reveals abstention is an unsolved problem, and one where scaling models is of little use. While recent reasoning LLMs have shown impressive results in complex problem solving, surprisingly, we find that reasoning fine-tuning degrades abstention (by 24% on average), even for math and science domains on which reasoning models are explicitly trained. We find that while a carefully crafted system prompt can boost abstention in practice, it does not resolve models' fundamental inability to reason about uncertainty. We release AbstentionBench to foster research into advancing LLM reliability. |
|
2025-06-16T00:00:00 | 2506.11305 | Don't Pay Attention | [
"Mohammad Hammoud",
"Devang Acharya"
] | The Transformer has become the de facto standard for large language models and a wide range of downstream tasks across various domains. Despite its numerous advantages like inherent training parallelism, the Transformer still faces key challenges due to its inability to effectively process sequences beyond a fixed context window and the quadratic complexity of its attention mechanism. These challenges have renewed interest in RNN-like architectures, which offer linear scaling with sequence length and improved handling of long-range dependencies, albeit with limited parallelism due to their inherently recurrent nature. In this paper, we propose Avey, a new neural foundational architecture that breaks away from both attention and recurrence. Avey comprises a ranker and an autoregressive neural processor, which collaboratively identify and contextualize only the most relevant tokens for any given token, regardless of their positions in the sequence. Specifically, Avey decouples sequence length from context width, thus enabling effective processing of arbitrarily long sequences. Experimental results show that Avey compares favorably to the Transformer across a variety of standard short-range NLP benchmarks, while notably excelling at capturing long-range dependencies. |
|
2025-06-17T00:00:00 | 2506.13585 | MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention | [
"MiniMax",
"Aili Chen",
"Aonian Li",
"Bangwei Gong",
"Binyang Jiang",
"Bo Fei",
"Bo Yang",
"Boji Shan",
"Changqing Yu",
"Chao Wang",
"Cheng Zhu",
"Chengjun Xiao",
"Chengyu Du",
"Chi Zhang",
"Chu Qiao",
"Chunhao Zhang",
"Chunhui Du",
"Congchao Guo",
"Da Chen",
"Deming Ding",
"Dianjun Sun",
"Dong Li",
"Enwei Jiao",
"Haigang Zhou",
"Haimo Zhang",
"Han Ding",
"Haohai Sun",
"Haoyu Feng",
"Huaiguang Cai",
"Haichao Zhu",
"Jian Sun",
"Jiaqi Zhuang",
"Jiaren Cai",
"Jiayuan Song",
"Jin Zhu",
"Jingyang Li",
"Jinhao Tian",
"Jinli Liu",
"Junhao Xu",
"Junjie Yan",
"Junteng Liu",
"Junxian He",
"Kaiyi Feng",
"Ke Yang",
"Kecheng Xiao",
"Le Han",
"Leyang Wang",
"Lianfei Yu",
"Liheng Feng",
"Lin Li",
"Lin Zheng",
"Linge Du",
"Lingyu Yang",
"Lunbin Zeng",
"Minghui Yu",
"Mingliang Tao",
"Mingyuan Chi",
"Mozhi Zhang",
"Mujie Lin",
"Nan Hu",
"Nongyu Di",
"Peng Gao",
"Pengfei Li",
"Pengyu Zhao",
"Qibing Ren",
"Qidi Xu",
"Qile Li",
"Qin Wang",
"Rong Tian",
"Ruitao Leng",
"Shaoxiang Chen",
"Shaoyu Chen",
"Shengmin Shi",
"Shitong Weng",
"Shuchang Guan",
"Shuqi Yu",
"Sichen Li",
"Songquan Zhu",
"Tengfei Li",
"Tianchi Cai",
"Tianrun Liang",
"Weiyu Cheng",
"Weize Kong",
"Wenkai Li",
"Xiancai Chen",
"Xiangjun Song",
"Xiao Luo",
"Xiao Su",
"Xiaobo Li",
"Xiaodong Han",
"Xinzhu Hou",
"Xuan Lu",
"Xun Zou",
"Xuyang Shen",
"Yan Gong",
"Yan Ma",
"Yang Wang",
"Yiqi Shi",
"Yiran Zhong",
"Yonghong Duan",
"Yongxiang Fu",
"Yongyi Hu",
"Yu Gao",
"Yuanxiang Fan",
"Yufeng Yang",
"Yuhao Li",
"Yulin Hu",
"Yunan Huang",
"Yunji Li",
"Yunzhi Xu",
"Yuxin Mao",
"Yuxuan Shi",
"Yuze Wenren",
"Zehan Li",
"Zelin Li",
"Zhanxu Tian",
"Zhengmao Zhu",
"Zhenhua Fan",
"Zhenzhen Wu",
"Zhichao Xu",
"Zhihang Yu",
"Zhiheng Lyu",
"Zhuo Jiang",
"Zibo Gao",
"Zijia Wu",
"Zijian Song",
"Zijun Sun"
] | https://github.com/MiniMax-AI/MiniMax-M1 | We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. The model is developed based on our previous MiniMax-Text-01 model, which contains a total of 456 billion parameters with 45.9 billion parameters activated per token. The M1 model natively supports a context length of 1 million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism in MiniMax-M1 enables efficient scaling of test-time compute. These properties make M1 particularly suitable for complex tasks that require processing long inputs and thinking extensively. MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems including sandbox-based, real-world software engineering environments. In addition to M1's inherent efficiency advantage for RL training, we propose CISPO, a novel RL algorithm to further enhance RL efficiency. CISPO clips importance sampling weights rather than token updates, outperforming other competitive RL variants. Combining hybrid-attention and CISPO enables MiniMax-M1's full RL training on 512 H800 GPUs to complete in only three weeks, with a rental cost of just $534,700. We release two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively, where the 40K model represents an intermediate phase of the 80K training. Experiments on standard benchmarks show that our models are comparable or superior to strong open-weight models such as the original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex software engineering, tool utilization, and long-context tasks. We publicly release MiniMax-M1 at https://github.com/MiniMax-AI/MiniMax-M1. |
2025-06-17T00:00:00 | 2506.11763 | DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents | [
"Mingxuan Du",
"Benfeng Xu",
"Chiwei Zhu",
"Xiaorui Wang",
"Zhendong Mao"
] | https://github.com/Ayanami0730/deep_research_bench | Deep Research Agents are a prominent category of LLM-based agents. By autonomously orchestrating multistep web exploration, targeted retrieval, and higher-order synthesis, they transform vast amounts of online information into analyst-grade, citation-rich reports--compressing hours of manual desk research into minutes. However, a comprehensive benchmark for systematically evaluating the capabilities of these agents remains absent. To bridge this gap, we present DeepResearch Bench, a benchmark consisting of 100 PhD-level research tasks, each meticulously crafted by domain experts across 22 distinct fields. Evaluating DRAs is inherently complex and labor-intensive. We therefore propose two novel methodologies that achieve strong alignment with human judgment. The first is a reference-based method with adaptive criteria to assess the quality of generated research reports. The other framework is introduced to evaluate DRA's information retrieval and collection capabilities by assessing its effective citation count and overall citation accuracy. We have open-sourced DeepResearch Bench and key components of these frameworks at https://github.com/Ayanami0730/deep_research_bench to accelerate the development of practical LLM-based agents. |
2025-06-17T00:00:00 | 2506.03968 | From Real to Synthetic: Synthesizing Millions of Diversified and Complicated User Instructions with Attributed Grounding | [
"Chiwei Zhu",
"Benfeng Xu",
"Xiaorui Wang",
"Zhendong Mao"
] | https://github.com/Ignoramus0817/SynthQuestions | The pursuit of diverse, complex, and large-scale instruction data is crucial for automatically aligning large language models (LLMs). While there are methods capable of generating synthetic instructions at scale, they either suffer from limited grounding sources, leading to a narrow distribution, or rely on trivial extensions that fail to produce meaningful trajectories in terms of complexity. In contrast, instructions that benefit efficient alignment are typically crafted with cognitive insights and grounded in real-world use cases. In this paper, we synthesize such instructions using attributed grounding, which involves 1) a top-down attribution process that grounds a selective set of real instructions to situated users, and 2) a bottom-up synthesis process that leverages web documents to first generate a situation, then a meaningful instruction. This framework allows us to harvest diverse and complex instructions at scale, utilizing the vast range of web documents. Specifically, we construct a dataset of 1 million instructions, called SynthQuestions, and demonstrate that models trained on it achieve leading performance on several common benchmarks, with improvements that continually scale with more web corpora. Data, models and codes will be available at https://github.com/Ignoramus0817/SynthQuestions. |
2025-06-17T00:00:00 | 2506.13759 | Discrete Diffusion in Large Language and Multimodal Models: A Survey | [
"Runpeng Yu",
"Qi Li",
"Xinchao Wang"
] | https://github.com/LiQiiiii/DLLM-Survey | In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs). Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy. This paradigm naturally enables parallel generation, fine-grained output controllability, and dynamic, response-aware perception. These capabilities are previously difficult to achieve with AR models. Recently, a growing number of industrial-scale proprietary d(M)LLMs, as well as a large number of open-source academic d(M)LLMs, have demonstrated performance comparable to their autoregressive counterparts, while achieving up to 10x acceleration in inference speed. The advancement of discrete diffusion LLMs and MLLMs has been largely driven by progress in two domains. The first is the development of autoregressive LLMs and MLLMs, which has accumulated vast amounts of data, benchmarks, and foundational infrastructure for training and inference. The second contributing domain is the evolution of the mathematical models underlying discrete diffusion. Together, these advancements have catalyzed a surge in dLLMs and dMLLMs research in early 2025. In this work, we present a comprehensive overview of the research in the dLLM and dMLLM domains. We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, and categorize representative models. We further analyze key techniques for training and inference, and summarize emerging applications across language, vision-language, and biological domains. We conclude by discussing future directions for research and deployment. Paper collection: https://github.com/LiQiiiii/DLLM-Survey |
2025-06-17T00:00:00 | 2506.08343 | Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency | [
"Chenlong Wang",
"Yuanning Feng",
"Dongping Chen",
"Zhaoyang Chu",
"Ranjay Krishna",
"Tianyi Zhou"
] | Recent advances in large reasoning models have enabled complex, step-by-step reasoning but often introduce significant overthinking, resulting in verbose and redundant outputs that hinder efficiency. In this study, we examine whether explicit self-reflection, signaled by tokens such as "Wait" and "Hmm", is necessary for advanced reasoning. We propose NoWait, a simple yet effective approach that disables explicit self-reflection by suppressing these tokens during inference. Extensive experiments on ten benchmarks across textual, visual, and video reasoning tasks show that NoWait reduces chain-of-thought trajectory length by up to 27%-51% in five R1-style model series, without compromising model utility. NoWait thus offers a plug-and-play solution for efficient and utility-preserving multimodal reasoning. |
|
2025-06-17T00:00:00 | 2506.07961 | BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models | [
"Peiyan Li",
"Yixiang Chen",
"Hongtao Wu",
"Xiao Ma",
"Xiangnan Wu",
"Yan Huang",
"Liang Wang",
"Tao Kong",
"Tieniu Tan"
] | Recently, leveraging pre-trained vision-language models (VLMs) for building vision-language-action (VLA) models has emerged as a promising approach to effective robot manipulation learning. However, only few methods incorporate 3D signals into VLMs for action prediction, and they do not fully leverage the spatial structure inherent in 3D data, leading to low sample efficiency. In this paper, we introduce BridgeVLA, a novel 3D VLA model that (1) projects 3D inputs to multiple 2D images, ensuring input alignment with the VLM backbone, and (2) utilizes 2D heatmaps for action prediction, unifying the input and output spaces within a consistent 2D image space. In addition, we propose a scalable pre-training method that equips the VLM backbone with the capability to predict 2D heatmaps before downstream policy learning. Extensive experiments show the proposed method is able to learn 3D manipulation efficiently and effectively. BridgeVLA outperforms state-of-the-art baseline methods across three simulation benchmarks. In RLBench, it improves the average success rate from 81.4% to 88.2%. In COLOSSEUM, it demonstrates significantly better performance in challenging generalization settings, boosting the average success rate from 56.7% to 64.0%. In GemBench, it surpasses all the comparing baseline methods in terms of average success rate. In real-robot experiments, BridgeVLA outperforms a state-of-the-art baseline method by 32% on average. It generalizes robustly in multiple out-of-distribution settings, including visual disturbances and unseen instructions. Remarkably, it is able to achieve a success rate of 96.8% on 10+ tasks with only 3 trajectories per task, highlighting its extraordinary sample efficiency. Project Website:https://bridgevla.github.io/ |
|
2025-06-17T00:00:00 | 2506.13750 | Test3R: Learning to Reconstruct 3D at Test Time | [
"Yuheng Yuan",
"Qiuhong Shen",
"Shizun Wang",
"Xingyi Yang",
"Xinchao Wang"
] | https://github.com/nopQAQ/Test3R | Dense matching methods like DUSt3R regress pairwise pointmaps for 3D reconstruction. However, the reliance on pairwise prediction and the limited generalization capability inherently restrict the global geometric consistency. In this work, we introduce Test3R, a surprisingly simple test-time learning technique that significantly boosts geometric accuracy. Using image triplets (I_1,I_2,I_3), Test3R generates reconstructions from pairs (I_1,I_2) and (I_1,I_3). The core idea is to optimize the network at test time via a self-supervised objective: maximizing the geometric consistency between these two reconstructions relative to the common image I_1. This ensures the model produces cross-pair consistent outputs, regardless of the inputs. Extensive experiments demonstrate that our technique significantly outperforms previous state-of-the-art methods on the 3D reconstruction and multi-view depth estimation tasks. Moreover, it is universally applicable and nearly cost-free, making it easily applied to other models and implemented with minimal test-time training overhead and parameter footprint. Code is available at https://github.com/nopQAQ/Test3R. |
Subsets and Splits