Agents
Insightful paper on recursive self-improvement with a frozen LLM. (bookmark it) Memento 3 clears every level of all 25 public ARC-AGI-3 game…
Insightful paper on recursive self-improvement with a frozen LLM. (bookmark it) Memento 3 clears every level of all 25 public ARC-AGI-3 games and uses 44% of the human action count. The underlying LLM
Insightful paper on recursive self-improvement with a frozen LLM. (bookmark it) Memento 3 clears every level of all 25 public ARC-AGI-3 games and uses 44% of the human action count. The underlying LLM is never updated. How it works: The agent writes what it believes about the environment as a natural-language rulebook, then compiles that rulebook into code that predicts the next state. A new version is kept only if replaying the code reproduces every transition the agent has observed, cell for cell. In an Atari Pong case study, the learned model drives a controller that wins 21 to 0 in three games without further LLM calls. https://arxiv.org/abs/2610.11794 Chat with Paper: https://academy.dair.ai/papers/memento-3-model-based-recursive-self-improvement-through-reflective-rulebooks-2610.11794
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Source: DAIR.AI (X) | 2026-10-14