Memento 3

Headlines · 2

1. 5d
   Huawei Memento 3 clears all 25 ARC-AGI-3 games, 56% fewer actions than humans

   Huawei's Noah's Ark Lab, working with UCL and other teams, introduced Memento 3, an agent system that completed all 25 public ARC-AGI-3 games and 183 levels, scoring a full 100 on RHAE (Relative Human Action Efficiency) with 7,518 actions — about 56% fewer than the 17,135 actions humans needed for the same set. The system used Claude Opus 5 as its base model without updating weights or fine-tuning on the games; its "Code as Model" approach has the agent maintain an evolving Markdown rulebook plus an executable Python world model, revising both when its rules prove wrong and tracking changes with Git version control (the report says one rulebook went through 11 versions). On the Atari game Pong, once the agent had learned the bounce dynamics it stopped querying the LLM and won three matches 21:0; in a controlled test, running two world models cut operations on the game wa30 from 899 to 597.

   量子位（原生 RSS） · 🔥 0
2. Oct 7
   Memento 3: frozen LLM agents self-improve via reflective rulebooks

   The paper introduces Memento 3, which lets frozen LLM agents continually learn explicit world models through external memory: the agent keeps a natural-language rulebook of revisable hypotheses about environment dynamics, then compiles it into executable code for prediction and planning. A loop of observation, reflection, rule revision, compilation and verification uses prediction errors to refine both the rulebook and its code; updated code is accepted only when the LLM judges it faithful to the rulebook and cell-exact replay reproduces the observed transitions. On ARC-AGI-3, the single-model agent clears every level of all 25 public games with a mean Relative Human Action Efficiency of 100.0 while using 44% of the human action count; in an Atari Pong case study, a learned feedback controller won 21:0 in each of three episodes with different openings and no further LLM calls.

   Hugging Face · Papers · 🔥 0

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