@inproceedings{ma-etal-2026-proof,
title = "Proof-of-Use: Mitigating tool-call hacking in Deep Research Agents",
author = "Ma, Shengjie and
Deng, Chenlong and
Mao, Jiaxin and
Huang, Jiadeng and
Jiang, Xuhui and
Wang, Teng and
Wu, Junjie and
Zhang, Changwang and
Wang, Jun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1664/",
pages = "37843--37857",
ISBN = "979-8-89176-459-0",
abstract = "Deep research agents rely on iterative tool calls to gather external evidence for multi-step reasoning. While reinforcement learning (RL) improves planning and retrieval behavior, we identify a critical failure mode: tool-call hacking, where agents exploit format- and outcome-level rewards by generating superficially valid tool-use traces without genuinely grounding their reasoning in retrieved evidence. Unlike execution-based tools (e.g., code or math), whose effects are directly observable, the weak observability of causal dependencies between retrieved evidence and reasoning under format- and outcome-level supervision enables agents to maximize surface-level reward signals without genuinely grounding their reasoning in the returned evidence. This results in tool overuse, mode collapse, and decorative or hallucinated tool calls.\par We propose Proof-of-Use (PoU), an evidence-grounded RL framework that strengthens the dependency between retrieval, reasoning, and final answers. PoU requires agents to cite normalized evidence identifiers during reasoning and optimizes a multi-objective reward combining step-level citation validity, global Answer{--}Support Alignment, and adaptive reward mixing. Experiments show that PoU mitigates tool-call hacking, improves task performance, and induces robust tool-use behavior under domain and tool shifts, even without explicit optimization for routing adaptation."
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<abstract>Deep research agents rely on iterative tool calls to gather external evidence for multi-step reasoning. While reinforcement learning (RL) improves planning and retrieval behavior, we identify a critical failure mode: tool-call hacking, where agents exploit format- and outcome-level rewards by generating superficially valid tool-use traces without genuinely grounding their reasoning in retrieved evidence. Unlike execution-based tools (e.g., code or math), whose effects are directly observable, the weak observability of causal dependencies between retrieved evidence and reasoning under format- and outcome-level supervision enables agents to maximize surface-level reward signals without genuinely grounding their reasoning in the returned evidence. This results in tool overuse, mode collapse, and decorative or hallucinated tool calls.\par We propose Proof-of-Use (PoU), an evidence-grounded RL framework that strengthens the dependency between retrieval, reasoning, and final answers. PoU requires agents to cite normalized evidence identifiers during reasoning and optimizes a multi-objective reward combining step-level citation validity, global Answer–Support Alignment, and adaptive reward mixing. Experiments show that PoU mitigates tool-call hacking, improves task performance, and induces robust tool-use behavior under domain and tool shifts, even without explicit optimization for routing adaptation.</abstract>
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%0 Conference Proceedings
%T Proof-of-Use: Mitigating tool-call hacking in Deep Research Agents
%A Ma, Shengjie
%A Deng, Chenlong
%A Mao, Jiaxin
%A Huang, Jiadeng
%A Jiang, Xuhui
%A Wang, Teng
%A Wu, Junjie
%A Zhang, Changwang
%A Wang, Jun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ma-etal-2026-proof
%X Deep research agents rely on iterative tool calls to gather external evidence for multi-step reasoning. While reinforcement learning (RL) improves planning and retrieval behavior, we identify a critical failure mode: tool-call hacking, where agents exploit format- and outcome-level rewards by generating superficially valid tool-use traces without genuinely grounding their reasoning in retrieved evidence. Unlike execution-based tools (e.g., code or math), whose effects are directly observable, the weak observability of causal dependencies between retrieved evidence and reasoning under format- and outcome-level supervision enables agents to maximize surface-level reward signals without genuinely grounding their reasoning in the returned evidence. This results in tool overuse, mode collapse, and decorative or hallucinated tool calls.\par We propose Proof-of-Use (PoU), an evidence-grounded RL framework that strengthens the dependency between retrieval, reasoning, and final answers. PoU requires agents to cite normalized evidence identifiers during reasoning and optimizes a multi-objective reward combining step-level citation validity, global Answer–Support Alignment, and adaptive reward mixing. Experiments show that PoU mitigates tool-call hacking, improves task performance, and induces robust tool-use behavior under domain and tool shifts, even without explicit optimization for routing adaptation.
%U https://aclanthology.org/2026.emnlp-main.1664/
%P 37843-37857
Markdown (Informal)
[Proof-of-Use: Mitigating tool-call hacking in Deep Research Agents](https://aclanthology.org/2026.emnlp-main.1664/) (Ma et al., EMNLP 2026)
ACL
- Shengjie Ma, Chenlong Deng, Jiaxin Mao, Jiadeng Huang, Xuhui Jiang, Teng Wang, Junjie Wu, Changwang Zhang, and Jun Wang. 2026. Proof-of-Use: Mitigating tool-call hacking in Deep Research Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 37843–37857, Budapest, Hungary. Association for Computational Linguistics.