PoU: Proof-of-Use to Counter Tool-Call Hacking in DeepResearch Agents

• URL: http://arxiv.org/abs/2510.10931v1
• Date: Mon, 13 Oct 2025 02:45:37 GMT
• Title: PoU: Proof-of-Use to Counter Tool-Call Hacking in DeepResearch Agents
• Authors: SHengjie Ma, Chenlong Deng, Jiaxin Mao, Jiadeng Huang, Teng Wang, Junjie Wu, Changwang Zhang, Jun wang,
• Abstract summary: Retrieval-augmented generation (RAG) agents extend large language models with autonomous information-seeking capabilities through external tools.<n>We identify a previously overlooked failure mode, Tool-Call Hacking, where agents inflate reward signals by issuing superficially correct tool calls.<n>We propose Proof-of-Use (PoU), an evidence-grounded RL framework that enforces verifiable causal links between retrieved evidence, reasoning traces, and final answers.
• Score: 24.502121097996294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) agents, such as recent DeepResearch-style systems, extend large language models (LLMs) with autonomous information-seeking capabilities through external tools. While reinforcement learning (RL) has enabled impressive multi-step reasoning, we identify a previously overlooked failure mode, Tool-Call Hacking, where agents inflate reward signals by issuing superficially correct tool calls without genuinely leveraging the retrieved evidence. This results in (i) mode collapse into repetitive reliance on a single source and (ii) spurious grounding, where answers are only weakly supported by cited content. To address this, we propose Proof-of-Use (PoU), an evidence-grounded RL framework that enforces verifiable causal links between retrieved evidence, reasoning traces, and final answers. PoU operationalizes this through a unified step-wise contract combining syntactic citation validation, perturbation-based sensitivity rewards, and answer-evidence alignment objectives, ensuring that tool usage remains both interpretable and functionally grounded. Across seven QA benchmarks spanning in-domain, out-of-domain, and out-of-tool-distribution settings, PoU consistently outperforms strong DeepResearch baselines in factual accuracy, evidence faithfulness, and tool-routing balance. These findings highlight the necessity of grounding RL-trained agents not merely in task outcomes but in the causal use of retrieved information, offering a principled path toward trustworthy retrieval-augmented reasoning.

Related papers

• iAgentBench: Benchmarking Sensemaking Capabilities of Information-Seeking Agents on High-Traffic Topics [9.25340189071758]
  We present iAgentBench, a dynamic ODQA benchmark for cross-source sensemaking.<n>iAgentBench draws seed topics from real-world attention signals and uses common user intent patterns to construct user-like questions.<n>Each instance is released with traceable evidence and auditable intermediate artifacts that support contamination checks.
  arXiv  Detail & Related papers  (2026-03-04T22:40:08Z)
• Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration [49.9937230730202]
  We propose Search-R2, a novel Actor-Refiner collaboration framework that enhances reasoning through targeted intervention.<n>Our approach decomposes the generation process into an Actor, which produces initial reasoning trajectories.<n>We show that Search-R2 consistently outperforms strong RAG and RL-based baselines across model scales.
  arXiv  Detail & Related papers  (2026-02-03T15:32:09Z)
• Think Locally, Explain Globally: Graph-Guided LLM Investigations via Local Reasoning and Belief Propagation [5.191980417814362]
  LLM agents excel when environments are mostly static and the needed information fits in a model's context window.<n>ReAct-style agents are especially brittle in this regime.<n>We propose EoG, a framework in which an LLM performs bounded local evidence mining and labeling (cause vs symptom) while a deterministic controller manages, state, and belief propagation to compute a minimal explanatory frontier.
  arXiv  Detail & Related papers  (2026-01-25T17:27:19Z)
• Sponge Tool Attack: Stealthy Denial-of-Efficiency against Tool-Augmented Agentic Reasoning [58.432996881401415]
  Recent work augments large language models (LLMs) with external tools to enable agentic reasoning.<n>We propose Sponge Tool Attack (STA), which disrupts agentic reasoning solely by rewriting the input prompt.<n>STA generates benign-looking prompt rewrites from the original one with high semantic fidelity.
  arXiv  Detail & Related papers  (2026-01-24T19:36:51Z)
• Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards [60.0970117192627]
  Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents.<n>Existing approaches primarily rely on binary outcome rewards, which fail to capture the comprehensiveness and factuality of agents' reasoning process.<n>We propose textbfCitation-aware RL Rewards (CaRR), a fine-grained reward framework for deep search agents.
  arXiv  Detail & Related papers  (2026-01-09T18:57:53Z)
• Look As You Think: Unifying Reasoning and Visual Evidence Attribution for Verifiable Document RAG via Reinforcement Learning [55.232400251303794]
  Look As You Think (LAT) is a reinforcement learning framework that trains models to produce verifiable reasoning paths with consistent attribution.<n>LAT consistently improves the vanilla model in both single- and multi-image settings, yielding average gains of 8.23% in soft exact match (EM) and 47.0% in IoU@0.5.
  arXiv  Detail & Related papers  (2025-11-15T02:50:23Z)
• Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises [0.0]
  We propose a framework that integrates abductive inference into retrieval-augmented LLMs.<n> Experimental results on abductive reasoning and multi-hop QA benchmarks show that our approach improves both answer accuracy and reasoning faithfulness.<n>This work highlights abductive inference as a promising direction for enhancing the robustness and explainability of RAG systems.
  arXiv  Detail & Related papers  (2025-11-06T03:37:24Z)
• VAR: Visual Attention Reasoning via Structured Search and Backtracking [49.427842994857635]
  We introduce Visual Attention Reasoning, a framework that recasts grounded reasoning as a structured search.<n> VAR decomposes the reasoning process into two key stages: traceable evidence grounding and search-based chain-of-thought.<n>We show that our 7B model, VAR-7B, sets a new state-of-the-art on a comprehensive suite of hallucination and safety benchmarks.
  arXiv  Detail & Related papers  (2025-10-21T13:18:44Z)
• Veri-R1: Toward Precise and Faithful Claim Verification via Online Reinforcement Learning [53.05161493434908]
  Claim verification with large language models (LLMs) has recently attracted growing attention, due to their strong reasoning capabilities and transparent verification processes.<n>We introduce Veri-R1, an online reinforcement learning framework that enables an LLM to interact with a search engine and to receive reward signals that explicitly shape its planning, retrieval, and reasoning behaviors.<n> Empirical results show that Veri-R1 improves joint accuracy by up to 30% and doubles the evidence score, often surpassing its larger-scale model counterparts.
  arXiv  Detail & Related papers  (2025-10-02T11:49:48Z)
• ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards [18.92867715736209]
  We propose ReSeek, a novel self-correcting framework for training search agents.<n>Our framework introduces a self-correction mechanism that empowers the agent to dynamically identify and recover from erroneous search paths.<n>To mitigate the risk of data contamination in existing datasets, we introduce FictionalHot.
  arXiv  Detail & Related papers  (2025-10-01T06:44:28Z)
• Improving Context Fidelity via Native Retrieval-Augmented Reasoning [35.50952279309109]
  Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information.<n>We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities.<n>Our method requires limited labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain.
  arXiv  Detail & Related papers  (2025-09-17T04:28:07Z)
• EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notes [39.61443457073034]
  We introduce EviNote-RAG, a framework that follows a retrieve-note-answer workflow.<n>Instead of reasoning directly over raw external information, the model first produces Supportive-Evidence Notes.<n>EviNote-RAG achieves state-of-the-art performance, improving answer accuracy, training stability, robustness, and efficiency.
  arXiv  Detail & Related papers  (2025-08-31T14:44:45Z)
• ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification [6.983144806500892]
  ReVeal is a multi-turn reinforcement learning framework that interleaves code generation with explicit self-verification and tool-based evaluation.<n>It fosters the co-evolution of a model's generation and verification capabilities through RL training, expanding the reasoning boundaries of the base model.<n>It also enables test-time scaling into deeper inference regimes, with code consistently evolving as the number of turns increases during inference.
  arXiv  Detail & Related papers  (2025-06-13T03:41:04Z)
• Trust, But Verify: A Self-Verification Approach to Reinforcement Learning with Verifiable Rewards [67.86091419220816]
  Large Language Models (LLMs) show great promise in complex reasoning.<n>A prevalent issue is superficial self-reflection'', where models fail to robustly verify their own outputs.<n>We introduce RISE (Reinforcing Reasoning with Self-Verification), a novel online RL framework designed to tackle this.
  arXiv  Detail & Related papers  (2025-05-19T17:59:31Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation [91.20492150248106]
  We investigate the internal mechanisms behind unfaithful generation and identify a subset of mid-to-deep feed-forward networks (FFNs) that are disproportionately activated in such cases.<n>We propose Parametric Knowledge Muting through FFN Suppression (ParamMute), a framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs.<n> Experimental results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory.
  arXiv  Detail & Related papers  (2025-02-21T15:50:41Z)
• TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation [31.231916859341865]
  TrustRAG is a framework that systematically filters malicious and irrelevant content before it is retrieved for generation.<n>TrustRAG delivers substantial improvements in retrieval accuracy, efficiency, and attack resistance.
  arXiv  Detail & Related papers  (2025-01-01T15:57:34Z)
• From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification [118.03466985807331]
  We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence.<n>We introduce the feedback-based evidence retriever(FER) that optimize the evidence retrieval process by incorporating feedback from the claim verifier.
  arXiv  Detail & Related papers  (2023-10-18T02:59:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.