ATLAS: Next-Generation AGI Training Framework Based on Active Inference and Swarm Intelligence

ATLAS is an AGI training framework written entirely in Rust. Its core idea is to enable AI to directly discover causal relationships from real-time authoritative data sources (such as NASA, WHO) instead of relying on human secondary texts. Through real-time data discovery, pheromone-guided curriculum learning, recursive validation mechanisms, and zero-knowledge proof chains, it builds a new paradigm of self-evolving scientific intelligence and pioneers an AGI training path that does not depend on static big data.

Traditional LLMs rely on human secondary data such as internet texts and Wikipedia, which have problems like timeliness lag, information redundancy, and entrenched bias. ATLAS proposes a revolutionary alternative: enabling AI systems to directly discover causal relationships from real-time API data sources, build a dynamic corpus updated every 10 seconds, and challenge the traditional paradigm of "training on descriptions written by humans."

ATLAS architecture includes four core components:

1. ASTRA-dev: Real-time discovery engine that extracts causal relationships from authoritative APIs via the OODA loop (Observe-Orient-Decide-Act) and writes them to the memory graph;
2. GraphPalace: Pheromone memory system based on the ant colony pheromone mechanism, which implements knowledge selection and elimination through concentration reinforcement/attenuation;
3. TRM-CausalValidator: 7 million parameter recursive validator that audits causal claims with a single verification time of <10ms;
4. ZK Schnorr Proof Chain: Attaches zero-knowledge proofs to outputs to achieve verifiable traceability and meet compliance requirements.

ATLAS follows the "zero external dependency" principle, with all functions implemented independently:

• 16 internal crates cover tensor operations, HTTP clients, etc.;
• Original CUDA kernel functions implement matrix multiplication, Flash Attention, etc.;
• No wrapper libraries like cudarc or tch are used; CUDA is called via extern "C" FFI. This design improves long-term maintainability and security, analogous to the stable operation characteristics of SQLite.

The ATLAS team used the BUTTERS system to verify the effect of pheromone memory:

• Reusing GraphPalace memory across runs achieves O(1/√T) convergence speed (T is the number of training iterations);
• Goodness of fit R²=0.982, p<0.001, with extremely strong statistical significance;
• After enabling GraphPalace, the number of ASTRA scientific discoveries increased by 34.4 times (Cohen's d=10.6).

Roadmap: Complete core functions in 7 phases over 22 weeks, covering matrix operations, model forward propagation, pheromone memory, validators, etc.; Academic Contributions: Plan 6 top conference papers covering topics such as architecture, discovery flywheel, zero-knowledge proof, etc.; Long-term Impact: Promote the democratization of scientific research, automation of knowledge updates, and improvement of AI interpretability, representing a new paradigm of AI that actively explores the world.