Adaptive, Intent-Indexed Working Memory for Latent Multi-Agent Reasoning

TL;DR: Imagine if agents in LatentMAS could organize their shared memory by “intent”—like sorting ideas into folders based on task goals—so they find and use relevant knowledge faster.

Research Question: Does structuring latent working memory around dynamically indexed intents improve collaborative efficiency and context retention in multi-agent reasoning?

Hypothesis: Intent-indexed memory will enable more targeted and efficient information retrieval, reducing context loss and boosting team reasoning, especially on multi-step or multi-task problems.

Experiment Plan: - Setup: Integrate an intent-indexed memory layer (as in MemIndex) into LatentMAS, allowing memory negotiation and dynamic partitioning by agent intent.

• Data: Use multi-task benchmarks and scenarios requiring sub-team specialization.
• Measurements: Evaluate latency, context-switching accuracy, and collaborative performance versus standard LatentMAS.
• Expected Outcomes: Faster, more accurate multi-agent reasoning, especially as task complexity and agent count scale.

References:

• Zou, J., Yang, X., Qiu, R., Li, G., Tieu, K., Lu, P., Shen, K., Tong, H., Choi, Y., He, J., Zou, J., Wang, M., & Yang, L. (2025). Latent Collaboration in Multi-Agent Systems.
• Saleh, A., Tarkoma, S., Lindgren, A., Donta, P. K., Dustdar, S., Pirttikangas, S., & Lovén, L. (2025). MemIndex: Agentic Event-based Distributed Memory Management for Multi-agent Systems. ACM Transactions on Autonomous and Adaptive Systems.