Learning Adaptive and Interpretable Multi-Agent Collaboration Policies

Develop learning algorithms that produce adaptive, interpretable collaboration policies for large language model-based multi-agent systems and demonstrate robustness under partial observability and adversarial conditions.

Background

Debate- and role-based multi-agent systems can outperform single agents, yet most collaboration structures are still manually designed. Recent multi-agent reinforcement learning approaches begin to treat collaboration as a trainable skill, but group-level credit assignment and scalable training remain poorly understood.

As agent populations scale, topology adaptation, communication overhead, and safety become critical. Robust and interpretable collaboration policies that adapt to uncertainty and adversarial settings are a key missing capability.

References

A key open problem is how to learn adaptive, interpretable collaboration policies that remain robust under partial observability and adversarial conditions.

— Agentic Reasoning for Large Language Models  (2601.12538 - Wei et al., 18 Jan 2026) in Section 7.4

Its smaller evidence base also leaves open how well allocation signals transfer across tasks, models, topologies, and attack strategies.

— SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems  (2609.00595 - Yang et al., 1 Sep 2026) in Section 5, subsection “Adaptive defense allocation”