arXiv：cs.AI· Yuan Liu, Haoyue Peng, Fanliang Hu, Lei Huang, Wenli Cai·· 16 小时前
多智能体语言模型的自适应审议控制：ADMA 让冻结任务模型识别更可靠
Learning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models
AI 导读
研究提出自适应多智能体控制器 ADMA，为提议、质疑、仲裁和协调学习连续策略，同时保持任务模型冻结。在 Qwen3.5 固定下，ADMA-Full 较 Direct 提升 6.2-14.2 个百分点，较 ADMA-Fixed 提升 2.8-3.0 个百分点，token 与延迟中位数分别为 Direct 的 14.3-18.7 倍和 7.8-16.5 倍。
正文
Abstract:Multi-agent deliberation can improve recognition by criticizing and revising candidate answers, but fixed controls cannot respond to changes in disagreement or reasoning quality. We present an adaptive multi-agent controller (ADMA) that learns continuous policies for proposal, challenge, arbitration, and coordination while keeping the task model frozen. Shared trace features describe quality, agreement, and progress, while role-specific features support decentralized control during inference. One controller trained on legal and financial reasoning is transferred without policy updates to four textual and multimodal benchmarks and two successive task-model versions. With Qwen3.5 held fixed, ADMA-Full improves accuracy by 6.2-14.2 percentage points over Direct and by 2.8-3.0 points over ADMA-Fixed. Paired bootstrap ranges for the Full-Fixed comparison are positive on three tasks. Outcome decompositions show that recoveries exceed introduced errors, while process assessment records fewer false-consensus events and more supported correct outcomes. Transfer to Qwen3.6 remains task dependent, which bounds the generality of the learned policy. Median token and latency ratios are 14.3-18.7 and 7.8-16.5 relative to Direct. These results show that adaptive deliberation can improve fixed-model recognition when its task-dependent gains justify the additional computation.
| Comments: | 33 pages, including references and technical appendices. Supplementary data included in the ancillary files |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17840 [cs.AI] |
| (or arXiv:2610.17840v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17840 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenli Cai [view email]
[v1]
Tue, 13 Oct 2026 06:59:41 UTC (7,435 KB)
来源：arXiv：cs.AI · arxiv.org