Learning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models
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arXiv:2608. 10665v1 Announce Type: new Abstract: Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers.
arXiv:2610.17869v1 Announce Type: new Abstract: Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference. A fixed protocol cannot allocate computati...
ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning proposes a controller that dynamically manages deliberation steps in multi-agent LLM systems. The controller evaluates answer concentration, support diversity, contradictions, role behavior, verification history, and remaining budget to decide actions such as propose, challenge, verify, arbitrate, diversify, correct, or stop. In a held‑out evaluation on MedQA, CaseHOLD, and FinQA, ECMR‑Full improves pooled accuracy from 62.1% to 84.9%, lowers false‑consensus from 40.7% to 17.0%, and raises evidence‑support from 58.0% to 88.7%.
arXiv:2608.29168v1 Announce Type: new Abstract: The LLM-as-a-Judge paradigm has emerged as a scalable alternative to human evaluation. However, single-model judges are limited by their inherent model...
CAST is a critique‑aware training framework that transforms sparse task outcomes into action‑level supervision for both critique learning and policy optimization. By analyzing agent trajectories, CAST synthesizes structured rationales that explain action validity under partial observability, enabling the creation of richer training data. Fine‑tuned Qwen3‑family models trained with CAST show significant reliability gains, outperforming GPT‑OSS‑120B by over 10% on Retail tasks and improving Telehealth performance by 9% in an out‑of‑domain setting.
arXiv:2610.09858v1 Announce Type: cross Abstract: Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment. Prior work has s...