ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning
We introduce ECMR, an evidence-aware controller over frozen role-conditioned LLMs. At each step, ECMR summarizes answer concentration, support diversity and independence, contradictions, role behavior, verification history, and remaining budget, then selects whether to propose, challenge, verify, arbitrate, diversify, correct a role, or stop.
ProofPaper ↗
Key points
- Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference.
- A fixed protocol cannot allocate computation differently to resolved and unresolved instances, and it may mistake repeated support for independent confirmation.
- We study this failure as semantic false consensus: a confident group answer supported by narrow, duplicated, or correlated rationales.
- We formulate this decision as finite-horizon budgeted control and derive sufficient conditions under which state conditioning, risk-triggered verification, and role correction improve cost-adjusted utility.
Sources (1)
- [1]ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM ReasoningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 07:30 AM
We introduce ECMR, an evidence-aware controller over frozen role-conditioned LLMs. At each step, ECMR summarizes answer concentration, support diversity and independence, contradictions, role behavior, verification history, and remaining budget, then selects whether to propose, challenge, verify, arbitrate, diversify, correct a role, or stop.
Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference.
Extractive summary: sentences quoted from the sources.
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