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Multi-Agent Transactive Memory | AI Research

Key Takeaways

  • Multi-Agent Transactive Memory (MATM) is a framework designed to help decentralized populations of AI agents share knowledge and learn from one another.
  • The decentralized deployment of LLM agents with diverse capabilities across diverse tasks motivates infrastructure for knowledge sharing across heterogeneous agent populations.
  • Just as search engines index human-generated artifacts to support human problem solving, retrieval systems can organize agent-generated artifacts for reuse across agent populations.
  • We extend retrieval-augmented generation - which demonstrates the value of human-authored artifacts to individual agents - to retrieval of agent-generated artifacts supporting a population of agents.
  • We focus on interactive environments (ALFWorld and WebArena), where trajectories are long and encode especially rich procedural structure.
Paper AbstractExpand

The decentralized deployment of LLM agents with diverse capabilities across diverse tasks motivates infrastructure for knowledge sharing across heterogeneous agent populations. Just as search engines index human-generated artifacts to support human problem solving, retrieval systems can organize agent-generated artifacts for reuse across agent populations. We extend retrieval-augmented generation - which demonstrates the value of human-authored artifacts to individual agents - to retrieval of agent-generated artifacts supporting a population of agents. In particular, agent trajectories encode reusable procedural knowledge, yet these artifacts are typically discarded after a single use or retained only by the producing agent, forcing newly instantiated agents to repeatedly rediscover existing solutions. We propose Multi-Agent Transactive Memory (MATM), a framework for population-level storage and retrieval of agent-generated trajectories, where producer agents contribute trajectories to a shared repository and consumer agents retrieve them to improve task execution. We focus on interactive environments (ALFWorld and WebArena), where trajectories are long and encode especially rich procedural structure. Our experiments demonstrate that retrieving trajectories from MATM improves downstream task performance and reduces interaction steps without coordination or joint training. These results position MATM as a design pattern for population-level experience sharing in open agent ecosystems.

Multi-Agent Transactive Memory (MATM) is a framework designed to help decentralized populations of AI agents share knowledge and learn from one another. Currently, when different AI agents solve tasks, they often discard their interaction histories—the step-by-step "trajectories" of their work—once a task is finished. This forces new agents to repeatedly solve the same problems from scratch. MATM creates a shared, searchable repository where agents can contribute their successful experiences and retrieve relevant procedural knowledge from others, effectively turning individual efforts into a collective, growing library of expertise.

How the Framework Works

MATM functions like a two-sided marketplace for procedural knowledge. When an agent (a "producer") completes a task, its action-observation trajectory is broken into chunks and stored in a shared index. When another agent (a "consumer") faces a similar problem, it can query this index to find relevant steps taken by previous agents. The system uses a "state-conditioned" approach, meaning the agent searches for guidance based on its current situation rather than just the initial task description. This allows the agent to receive specific, actionable advice on how to proceed from its current point in a task.

Improving Retrieval with Learning to Rank

To ensure agents get the most helpful information, the researchers implemented a "Learning to Rank" (LTRT) reranking stage. After an initial search retrieves a list of potential trajectory chunks, the reranker evaluates them using a variety of features, such as the producer agent’s past performance, the consumer agent’s specific needs, and the similarity between the current task and the stored trajectory. By training this reranker to prioritize trajectories that provide the highest "marginal utility"—meaning they actually improve the agent's success rate compared to working without help—the system ensures that the most effective solutions rise to the top.

Key Results

The researchers tested MATM in two complex, interactive environments: ALFWorld (household tasks) and WebArena (web navigation). The experiments showed that retrieving trajectories from the shared MATM repository consistently improved task performance. Furthermore, agents were able to complete tasks more efficiently, requiring fewer interaction steps to reach their goals. Importantly, these improvements were achieved without needing to coordinate the agents or perform expensive joint training, demonstrating that MATM can function as a scalable design pattern for open ecosystems where agents join and operate independently.

Considerations for Future Use

MATM is designed to grow organically as more agents interact with the repository, accumulating knowledge across diverse environments. Because the system allows for personalization—where the reranker learns which trajectories are most useful for specific types of agents—it can adapt to a heterogeneous population where different agents have varying capabilities. While the current research focuses on raw action-observation trajectories, the framework is flexible enough to support higher-level abstractions, such as shared skills or workflows, as the population’s collective memory continues to evolve.

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