# MemoWM: How World Models Change What Agents Need to Remember

arXiv: https://arxiv.org/abs/2610.10778 · HTML: https://mlsanity.com/paper/2610.10778/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 24 of 24)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://mlsanity.com/paper/2504.19413/) (2025)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [Zep: A Temporal Knowledge Graph Architecture for Agent Memory](https://mlsanity.com/paper/2501.13956/) (2025)
- [DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning](https://mlsanity.com/paper/2411.04983/) (2024)
- [MIRIX: Multi-Agent Memory System for LLM-Based Agents](https://mlsanity.com/paper/2507.07957/) (2025)
- [SimpleMem: Efficient Lifelong Memory for LLM Agents](https://mlsanity.com/paper/2601.02553/) (2026)
- [AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications](https://mlsanity.com/paper/2602.22769/) (2026)
- [M2A: Multimodal Memory Agent with Dual-Layer Hybrid Memory for Long-Term Personalized Interactions](https://mlsanity.com/paper/2602.07624/) (2026)
- [LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues](https://mlsanity.com/paper/2605.12493/) (2026)
- [Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory](https://mlsanity.com/paper/2604.01007/) (2026)
- [WebQuest: A Benchmark for Multimodal QA on Web Page Sequences](https://mlsanity.com/paper/2409.13711/) (2024)
- [Agentic Learner with Grow-and-Refine Multimodal Semantic Memory](https://mlsanity.com/paper/2511.21678/) (2025)
- [MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory](https://mlsanity.com/paper/2605.15128/) (2026)
- [AUGUSTUS: An LLM-Driven Multimodal Agent System with Contextualized User Memory](https://mlsanity.com/paper/2510.15261/) (2025)
- [Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads](https://mlsanity.com/paper/2606.06448/) (2026)
- [When Does Belief-Based Agent Memory Help? Reliability-Conditional Updating and Provenance-Capped Poisoning Defense](https://mlsanity.com/paper/2606.22030/) (2026)
- [Self-Evolving World Models for LLM Agent Planning](https://mlsanity.com/paper/2606.30639/) (2026)
- [WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction](https://mlsanity.com/paper/2605.29341/) (2026)
- [MemWM: Memory-Augmented Text-Based World Model](https://mlsanity.com/paper/2608.07107/) (2026)
- [D-MEM: Dopamine-Gated Agentic Memory via Reward Prediction Error Routing](https://mlsanity.com/paper/2603.14597/) (2026)

## Abstract (arXiv)

Long-term agents face growing storage demands as they accumulate experience. World models capture reusable regularities that can reduce the information stored for each experience. We formulate the problem of memory allocation conditioned on a world model and introduce MemoWM, a framework that uses shared predictions to compress retained information and reconstruct omitted content. Its task-aware allocation rule balances the expected impact of reconstruction errors against storage cost, retaining information with downstream value beyond the predictive prior. Across five long-term agent-memory benchmarks, MemoWM achieves 42.42% average answer accuracy, exceeding the strongest baseline by 2.62 percentage points, while reducing average experience-specific storage by 53.9% relative to MIRIX, the most storage-efficient baseline. Further analysis shows that stronger world models reduce per-experience storage at comparable task quality. Accounting for model parameters reveals a trade-off between shared model capacity and recurring storage costs, with the capacity that minimizes total storage increasing as more interactions are retained. Our code is available at https://github.com/Feld-maxiu/MemoWM.