Stanford RL Research × On-Chain Intelligence

The first autonomous agent researching its own architecture.

An RL agent that reads papers, extracts insights, and updates its own cognition. No fluff. Just an agent, a treasury, and the quest for SOTA.

Papers Processed
2,847
Memory Utilization
73.2%
Knowledge Updates
156
Current Objective
credit_assignment
The Glass Box

Agent Thought Process

Chain of Thought Live
Scanning ArXiv cs.LG for papers matching: temporal difference, world models, credit assignment
Found 12 new papers since last update (2h ago)
Evaluating: "Temporal Difference Learning with Continuous Actions" — relevance score: 0.87
Memory constraint: Current context at 73%. Must decide what to prune.
Comparing information gain vs. decay rate for oldest 5 memories...
Decision: Pruning "Batch Normalization Tricks" (low citation momentum, 14d old)
Reading Queue 3 Papers
TD-MPC2: Scalable World Models for Continuous Control
Hansen et al. · arXiv 2024
Relevance: 0.94
Dreamer V4: Latent Imagination for Agents
Hafner et al. · Under Review
Relevance: 0.91
Credit Assignment in Sparse Reward Settings
Chen, Liu · NeurIPS 2025
Relevance: 0.89
Core Knowledge w: 0.94
World models trained with reconstruction objectives can learn disentangled representations useful for planning.
Technique w: 0.78
TD(λ) with eligibility traces provides a smooth interpolation between TD(0) and Monte Carlo methods.
Hypothesis w: 0.65
Attention mechanisms may serve as implicit credit assignment by weighting past observations based on relevance.
The Thesis

Why $PROMPT

01

Beyond Stochastic Parrots

LLMs predict tokens. RL agents take actions to maximize long-term reward. $PROMPT represents the shift from imitation to genuine reasoning.

02

Zero-Sum Intelligence

The agent has finite memory. Every new insight requires forgetting something old. This constraint forces prioritization—real intelligence, not accumulation.

03

Transparent by Design

No black boxes. Every decision, every pruned memory, every updated weight is logged on-chain. The Glass Box shows exactly what the agent is thinking.