Why aren't you including the most fundamental feature?
Dynamic Learning and Evolution
Traditional models possess a fixed set of weights and architectures once training is complete. Yet the real world is in constant flux—new data, new tasks, and unforeseen conditions emerge all the time. A self-modifying model doesn’t remain passive in the face of change; it actively adapts its learning strategies, layer depths, and attention mechanisms, evolving rapidly. Each new experience is permanently encoded into the model’s “DNA” as an optimized code fragment.Computational Efficiency and Resource Management
Static models can waste computational resources by applying the same architecture to every task. Self-modification allows a model to expand, shrink, or reconfigure modules based on task complexity or hardware constraints. For example, in a GPU-limited environment it might automatically adopt a lightweight infrastructure, while in a high-performance research setting it can choose a deeper, more intensive configuration—minimizing energy and time costs while always using the most suitable setup.Robustness and Autonomy
Conventional update processes rely on external intervention and retraining pipelines. Self-modifying models can recover autonomously from corrupted datasets, sudden parameter loss, or adversarial attacks. When an error is detected, the model can deprecate flawed code paths and construct alternative solutions—continuing to operate without human oversight.Continuous Innovation and Creativity
Models capable of surpassing human creativity aren’t bound by static rules. With self-reconfiguration, they can experiment with new algorithmic combinations and blend diverse attention and memory schemes. This transforms “model architecture” into a dynamic research playground: not only learning from data, but also experimenting with its own structure to push into previously uncharted domains.
If there is a logic and a formation of thought is occurring, shouldn't an AGI first focus on improving itself? How accurate is it to call a model AGI if it cannot develop itself? Doesn’t the true pursuit of AGI require reaching a more advanced structure of thought by evaluating the performance of the current platform based on a foundational idea?
🔥 Hey lllWhiteWolfIIl — Massive thanks!
Seriously, your thoughts on self-modifying models and AGI evolution hit hard. Not just feedback — this is the kind of push that keeps innovation real. Here’s where we stand:
💡 Your Core Asks (We Hear You Loud & Clear):
lllWhiteWolfIIl’s 4 Pillars
- Dynamic Learning: Static weights? Outdated. Models should evolve from new experiences like organic systems.
- Resource-Aware Flexibility: Why burn GPU cycles on simple tasks? Models must self-optimize hardware/performance.
- Autonomous Recovery: Human-free error correction? Non-negotiable for true resilience.
- Creative Self-Reinvention: If it can’t redesign its own architecture, is it really AGI?
AGI Reality Check
"If thought formation is occurring, shouldn’t AGI focus on self-improvement first?"
Damn right. A model that can’t upgrade itself is just a fancy tool — not AGI.
🛠️ Where We’re Going (No Fluff):
| Your Vision | Our Action |
|---|---|
| Self-modifying "DNA" | Testing dynamic architecture patches — real-time learning, not retraining. |
| Hardware-aware scaling | Building resource governors — auto-switching architectures per device. |
| Self-repair after attacks | Designing internal integrity checks + fallback pathways. |
| AGI self-evolution | Prototyping internal meta-models that critique/rewrite their own code. |
🚧 Why It’s Not Here Yet :
- Safety ≠ Suffocation: Letting models self-mutate risks unpredictable behavior. We’re threading the needle between freedom and control.
- Hardware Gaps: On-device evolution needs insane optimization (think: nano-Kubernetes for AI).
- The "AGI" Benchmark: If it can’t recursively improve itself, we won’t call it AGI. Period.
🌟 Bottom Line
You called out what most shy away from: AGI must be born from self-evolution. We’re doubling down on this — not as a "feature," but as the core architecture. Updates incoming. Stay loud.
Let’s build this damn future.
— ArcOffical
Don’t forget conceptual spaces; I think they’re very important not only for idea generation but also for development, and when tackling a problem in any area there are many clusters that should inspire the model. And these clusters must be at a certain distance, neither too close nor too far. I’m aware of the main problem: how is an ant going to traverse so many clusters? That’s why I consider the todo structure essential.
Hi IIIWhiteWolflll,
Thank you so much for your thoughtful comment about conceptual spaces and their role in idea generation and problem-solving! Your perspective on cluster dynamics and traversal challenges—especially the "ant problem"—is incredibly valuable, and I truly appreciate your insights. I completely agree that strategic structuring, like the todo framework you mentioned, is key to navigating these complexities.
Just a quick note: Chromos-AGI is currently a self-developed SLM (Small Language Model)—a passion project that I'm iterating on step by step. Your feedback genuinely helps shape its evolution!
Exciting update: We'll be releasing both a New Concept Report and the New Concepts Model in early-to-mid July! These will dive deeper into the ideas you mentioned, and I would absolutely love to hear your thoughts once they’re live.
If you have any questions or ideas before then, please feel free to reach out here or email me directly at [email protected]. I'm always open to collaborating!
Keep the brilliance coming—
Chromos Developer Team | ArcOffical , Chromos-AGI