From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents

Zijie Zhuang; Changxin Lao; Pengbo Xu; Hanwen Xu; Ruochen Yang; Yingzhi He; Peng Zhang; Jiangxia Cao; Yusheng Huang; Guohong Mu; Jian Liang; Ruiming Tang; Shuang Yang; Zhaojie Liu; Wenwu Ou; Kun Gai

arXiv · 2608.05235

From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents

Zijie Zhuang·Changxin Lao·Pengbo Xu·Hanwen Xu·Ruochen Yang·Yingzhi He·Peng Zhang·Jiangxia Cao·Yusheng Huang·Guohong Mu·Jian Liang·Ruiming Tang·Shuang Yang·Zhaojie Liu·Wenwu Ou·Kun Gai

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Abstract

Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions. Yet a completed trajectory is not automatically evidence: generated artifacts may be unsupported or incomplete, executed rounds may be invalid or confounded, and later modifications may obscure earlier findings. We study \textbf{trajectory-to-evidence conversion}, asking what a completed research process has actually established. We introduce an evidence-grounded framework that couples bounded verification of consequential artifacts with post-execution claim qualification. A context-isolated generate--verify--repair process checks artifacts for evidence violations and missing downstream requirements before release. After execution, validity and attribution checks consolidate evidence across rounds, qualify intervention-level claims as actionable repairs, diagnostic guards, or withheld findings, and preserve admitted claims as auditable records with explicit provenance and applicability boundaries. A hybrid LLM-assisted controller subsequently applies, defers, or rejects records based on available target evidence. Record audits characterize which claims survive qualification, while downstream diagnostics identify affirmative applicability judgment as a bottleneck for the tested controller. Across paper-to-target adaptations, later rounds often improve on the first, while final rounds frequently underperform an earlier best, exposing non-monotonic trajectory evolution. Candidates produced through the complete workflow also yielded positive online lifts relative to deployed baselines.

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Zijie Zhuang, Changxin Lao, Pengbo Xu, Hanwen Xu, Ruochen Yang, Yingzhi He, Peng Zhang, Jiangxia Cao, Yusheng Huang, Guohong Mu, Jian Liang, Ruiming Tang, Shuang Yang, Zhaojie Liu, Wenwu Ou, Kun Gai. 2026-08-05. From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents. https://arxiv.org/abs/2608.05235

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