{"paper":{"title":"Helix: A Dual-Helix Co-Evolutionary Multi-Agent System for Prompt Optimization and Question Reformulation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"Helix jointly optimizes question reformulation and prompt instructions via a three-stage co-evolutionary multi-agent framework.","cross_cats":[],"primary_cat":"cs.MA","authors_text":"Kewen Zhu, Liping Yi, Qinghua Hu, Xiang Li, Zhiming Zhao","submitted_at":"2026-03-20T08:16:09Z","abstract_excerpt":"Automated prompt optimization (APO) aims to improve large language model performance by refining prompt instructions. However, existing methods are largely constrained by fixed prompt templates, limited search spaces, or single-sided optimization that treats user questions as immutable inputs. In practice, question formulation and prompt design are inherently interdependent: clearer question structures facilitate focused reasoning and task understanding, while effective prompts reveal better ways to organize and restate queries. Ignoring this coupling fundamentally limits the effectiveness and"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We propose a unified multi-agent system (Helix) that jointly optimizes question reformulation and prompt instructions through a structured three-stage co-evolutionary framework... achieving up to 3.95% performance improvements across tasks with favorable optimization efficiency.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That question formulation and prompt design are inherently interdependent in a manner that allows dual-track co-evolution between specialized agents to produce complementary improvements unavailable to single-sided optimization methods.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Helix introduces a three-stage dual-helix co-evolutionary multi-agent framework that jointly optimizes question reformulation and prompt instructions, reporting up to 3.95% gains on 12 benchmarks versus 6 baselines.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Helix jointly optimizes question reformulation and prompt instructions via a three-stage co-evolutionary multi-agent framework.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3785e8b305b4cb6878a8eea3e1d83681943589de06ea196e7fb0ab5a5216b2c1"},"source":{"id":"2603.19732","kind":"arxiv","version":2},"verdict":{"id":"aa2e58ce-8dd7-488e-9044-34df551825c9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T18:23:40.956352Z","strongest_claim":"We propose a unified multi-agent system (Helix) that jointly optimizes question reformulation and prompt instructions through a structured three-stage co-evolutionary 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