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JW

Junhao Wang

NeurIPS 2026×2COLM 2026智能体与工具代码推理强化学习与后训练
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1 一作 · 3 篇 · NeurIPS 2026 / COLM 2026

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2026 顶会论文

3
一作COLM 2026Poster· Poster Session 2· Franciscan A· #78

HAI-Agent: Improving Long Horizon Software Engineering with Handoff Interventions

Junhao Wang · Evander Yang · Zenan Xu · Taiqiang Wu · Feiyuan Zhang · Michael R. Lyu

improve agent collaboration in long horizon software engineeering tasks

摘要

Multi-agent systems for software engineering often suffer from a \textit{delegation tax}, where the coordination overhead of sub-agents degrades performance compared to single-agent baselines. We trace this penalty to failures at the inter-agent \textit{handoff boundary}, defined by \textit{context loss} during task delegation and \textit{weak verification} upon return. We propose \ours{}, a framework that transforms this boundary into a governed interface through three \textit{handoff interventions}: (1) \textit{Delegation Review}, a pre-dispatch gate ensuring task specifications are self-contained; (2) \textit{Retrospective Reflection}, a mandatory self-certification by the agent; and (3) \textit{Verification Fork}, an independent validation of the return by the orchestrator. Averaged over three independent runs, \ours{} resolves \textit{47.2\%} of SWE-bench-Pro tasks with Qwen3.5-397B-A17B (+4.0 over a matched SWE-Agent baseline) and, on the longer-horizon Commit0-lite, reaches a \textit{61.6\%} pass rate with Claude-4.5-Sonnet (+11.3) and \textit{59.8\%} with Qwen3.5-397B-A17B (+8.0), while consistently reducing run-to-run variance. Ablations on the full SWE-bench-Pro confirm that ungoverned delegation degrades performance below the single-agent baseline and that each intervention is necessary. Our claims are deliberately scoped to multi-agent substrates in long-horizon settings: governing the handoff removes the delegation tax where decomposition is necessary, rather than replacing single-agent test-time scaling on atomic tasks.

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