MemWM: Memory-Augmented Text-Based World Model
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TL;DR - MemWM is a memory-augmented text-based world model that conditions next-state prediction on a curated "world memory" bank, reducing the factual drift that plagues LLM world models used for agent planning. It matters because accurate state imagination is the bottleneck for reliable model-based LLM agents.
- World memory stores transition rules, state caches, and hard-to-predict facts, which are retrieved to condition next-state imagination instead of relying on fluent-but-lossy generation.
- Introduces Structured State Fidelity (SSF), a metric scoring predicted states against benchmark-specific facts and fields; memory-augmented training beats SFT by up to 206.3% on SSF.
- In full planning, the policy model stays frozen and receives "policy-side world skill" — retrieved task-level skills plus step-wise corrective guidance for action selection.
- On ALFWorld, WebShop, and ScienceWorld, downstream success improves up to 65.4% relative to an SFT-trained world-model agent, with sensitivity analyses showing gains hold across memory and action-budget settings.
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MemWM: Memory-Augmented Text-Based World Model
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TL;DR - MemWM is a memory-augmented text-based world model that conditions next-state prediction on a curated "world memory" bank, reducing the factual drift that plagues LLM world models used for agent planning. It matters because accurate state imagination is the bottleneck for reliable model-based LLM agents.
- World memory stores transition rules, state caches, and hard-to-predict facts, which are retrieved to condition next-state imagination instead of relying on fluent-but-lossy generation.
- Introduces Structured State Fidelity (SSF), a metric scoring predicted states against benchmark-specific facts and fields; memory-augmented training beats SFT by up to 206.3% on SSF.
- In full planning, the policy model stays frozen and receives "policy-side world skill" — retrieved task-level skills plus step-wise corrective guidance for action selection.
- On ALFWorld, WebShop, and ScienceWorld, downstream success improves up to 65.4% relative to an SFT-trained world-model agent, with sensitivity analyses showing gains hold across memory and action-budget settings.