# Multi-Agent Transactive Memory

arXiv: https://arxiv.org/abs/2606.19911 · HTML: https://mlsanity.com/paper/2606.19911/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 31 of its 58 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 30 of 31)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Continual Learning of Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2404.16789/) (2024)
- [Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control](https://mlsanity.com/paper/2306.07863/) (2023)
- [Inducing Programmatic Skills for Agentic Tasks](https://mlsanity.com/paper/2504.06821/) (2025)
- [Multi-Agent Collaboration via Evolving Orchestration](https://mlsanity.com/paper/2505.19591/) (2025)
- [Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://mlsanity.com/paper/2104.08762/) (2021)
- [CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization](https://mlsanity.com/paper/2310.10134/) (2023)
- [Sample Complexity of Multi-task Reinforcement Learning](https://mlsanity.com/paper/1309.6821/) (2013)
- [Learning to Search Better Than Your Teacher](https://mlsanity.com/paper/1502.02206/) (2015)
- [Distilling LLM Agent into Small Models with Retrieval and Code Tools](https://mlsanity.com/paper/2505.17612/) (2025)
- [SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction](https://mlsanity.com/paper/2410.09008/) (2024)
- [NaturalThoughts: Selecting and Distilling Reasoning Traces for General Reasoning Tasks](https://mlsanity.com/paper/2507.01921/) (2025)
- [Discovery of Options via Meta-Learned Subgoals](https://mlsanity.com/paper/2102.06741/) (2021)
- [MaestroMotif: Skill Design from Artificial Intelligence Feedback](https://mlsanity.com/paper/2412.08542/) (2024)
- [SkillOrchestra: Learning to Route Agents via Skill Transfer](https://mlsanity.com/paper/2602.19672/) (2026)
- [Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook](https://mlsanity.com/paper/2602.14299/) (2026)
- [Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts](https://mlsanity.com/paper/2509.21743/) (2025)
- [Retrieval-Enhanced Machine Learning: Synthesis and Opportunities](https://mlsanity.com/paper/2407.12982/) (2024)
- [A Compression-Inspired Framework for Macro Discovery](https://mlsanity.com/paper/1711.09048/) (2017)
- [RAG over Thinking Traces Can Improve Reasoning Tasks](https://mlsanity.com/paper/2605.03344/) (2026)
- [Offline Preference-Based Trajectory Evaluation](https://mlsanity.com/paper/2606.17541/) (2026)

## Built on by (most cited first, 4 of 4)

- [Beyond Corrected Memory: Execution Consistency in Multi-Agent Systems](https://mlsanity.com/paper/2610.08101/) (2026)
- [Not All Experience Belongs in the Weights: Component Routing for Self-Improving GUI Agents](https://mlsanity.com/paper/2610.01787/) (2026)
- [MemCo: Memory-Centric Collaboration for Generalizing LLM Agents to Unseen Environments](https://mlsanity.com/paper/2610.07376/) (2026)
- [Scaling Multi-Agent Intelligence with Substrate-Centric Coordination](https://mlsanity.com/paper/2610.16120/) (2026)

## Abstract (arXiv)

Decentralized deployment of LLM agents with diverse capabilities motivates infrastructure for knowledge sharing across heterogeneous agent populations. Just as search engines index human-generated artifacts for human problem solving, retrieval systems can organize agent-generated artifacts for reuse across agents, extending retrieval-augmented generation from human-authored documents to agent-generated ones. Agent trajectories in particular encode reusable procedural knowledge, yet are typically discarded after a single use or retained only by the producing agent, forcing newly instantiated agents to rediscover existing solutions. We propose Multi-Agent Transactive Memory (MATM), a framework for population-level storage and retrieval of agent trajectories, where producer agents contribute trajectories to a shared repository and consumer agents retrieve them to improve task execution. In interactive environments with long, procedurally rich trajectories (ALFWorld and WebArena), retrieving from MATM improves downstream task performance and reduces interaction steps without coordination or joint training, positioning MATM as a design pattern for population-level experience sharing in open agent ecosystems.