SEPAL: Separated Expert Pairs with Answer-Level Fusion for Reliable LLM Collaboration
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Computer Science > Computation and Language
arXiv:2609.39645 (cs)
[Submitted on 30 Sep 2026]
Title:SEPAL: Separated Expert Pairs with Answer-Level Fusion for Reliable LLM Collaboration
View a PDF of the paper titled SEPAL: Separated Expert Pairs with Answer-Level Fusion for Reliable LLM Collaboration, by Weijie Ren and 5 other authors
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Abstract:Multi-agent collaboration lets large language models (LLMs) improve question answering through deliberation and feedback. Yet shared discussion couples correction with exposure to the same mistakes, which can erode the diversity needed for voting. Self-consistency offers sampling diversity without feedback, while single-pair Actor-Critic collaboration refines only one candidate. We introduce SEPAL, which assigns three private Actor-Critic teams to direct reasoning, evidence grounding, and verification. Role-specific training gives the teams different reasoning objectives beyond sampling variation. Each Critic guides revisions within its own team, preventing feedback from carrying errors across candidates. Once revision ends, majority voting combines only the final answers, keeping the reasoning histories separate until the decision. Across five open-weight backbones and five question-answering benchmarks, SEPAL improves mean accuracy by 1.81 percentage points over a matched single Actor-Critic pair, with improvements across all five backbones. Code is available at this https URL.
| Comments: | 22 pages, 4 figures. Code: this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.39645 [cs.CL] |
| (or arXiv:2609.39645v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.39645
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled SEPAL: Separated Expert Pairs with Answer-Level Fusion for Reliable LLM Collaboration, by Weijie Ren and 5 other authors
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