CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2604.05467v1
• Date: Tue, 07 Apr 2026 06:05:08 GMT
• Title: CUE-R: Beyond the Final Answer in Retrieval-Augmented Generation
• Abstract summary: We introduce CUE-R, a framework for measuring per-evidence-item operational utility in single-shot RAG.<n>We show that CUE-R perturbs individual evidence items via REMOVE, REPLACE, and DUPLICATE operators, then measures changes along three utility axes.<n>Our results suggest that answer-only evaluation misses important evidence effects and that intervention-based utility analysis is a practical complement for RAG evaluation.
• Score: 0.996038809132083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As language models shift from single-shot answer generation toward multi-step reasoning that retrieves and consumes evidence mid-inference, evaluating the role of individual retrieved items becomes more important. Existing RAG evaluation typically targets final-answer quality, citation faithfulness, or answer-level attribution, but none of these directly targets the intervention-based, per-evidence-item utility view we study here. We introduce CUE-R, a lightweight intervention-based framework for measuring per-evidence-item operational utility in single-shot RAG using shallow observable retrieval-use traces. CUE-R perturbs individual evidence items via REMOVE, REPLACE, and DUPLICATE operators, then measures changes along three utility axes (correctness, proxy-based grounding faithfulness, and confidence error) plus a trace-divergence signal. We also outline an operational evidence-role taxonomy for interpreting intervention outcomes. Experiments on HotpotQA and 2WikiMultihopQA with Qwen-3 8B and GPT-5.2 reveal a consistent pattern: REMOVE and REPLACE substantially harm correctness and grounding while producing large trace shifts, whereas DUPLICATE is often answer-redundant yet not fully behaviorally neutral. A zero-retrieval control confirms that these effects arise from degradation of meaningful retrieval. A two-support ablation further shows that multi-hop evidence items can interact non-additively: removing both supports harms performance far more than either single removal. Our results suggest that answer-only evaluation misses important evidence effects and that intervention-based utility analysis is a practical complement for RAG evaluation.

Related papers

• CAFE: Self-Improving Search Agents Need Co-Evolving Feedback [64.5257526832476]
  Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongoing trajectory before those errors compound.<n>Treating corrective feedback as a learned in-trajectory intervention couples the two roles: the agent must decide when to request and use feedback, while the critic must infer useful corrections from outcome-confounded rollouts whose failure patterns shift as the agent improves.
  arXiv  Detail & Related papers  (2026-08-25T16:39:00Z)
• LOTAPO: Leave-One-Turn Attribution for Self-Generated Process Rewards in Multi-Turn Search Reasoning [12.013691798765594]
  LOTAPO is a self-generated process-supervision method based on backward leave-one-turn attribution.<n>For each search turn, LOTAPO replaces the turn and its retrieval observation with a fixed [DELETE] placeholder.<n>This Answer-Likelihood Gain estimates the turn's contribution while preserving all downstream interactions.
  arXiv  Detail & Related papers  (2026-07-15T06:55:28Z)
• Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs [68.58207076756237]
  We introduce ProCauEval, a perturbation-based evaluation protocol that shifts from outcome assessment to mechanism diagnosis.<n>We find that models faithfully perceive video content yet systematically underexploit it during causal reasoning.<n>We propose Anti-Distillation Policy Optimization (ADPO), a reinforcement learning framework built on negative teacher alignment.
  arXiv  Detail & Related papers  (2026-05-10T08:48:58Z)
• Bounding Hallucinations: Information-Theoretic Guarantees for RAG Systems via Merlin-Arthur Protocols [40.19713302778418]
  We introduce a training framework that treats the entire RAG pipeline as an interactive proof system.<n>We show that M/A-trained LLMs show improved groundedness, completeness, soundness, and reject behavior.<n>Our results demonstrate that autonomous interactive-proof-style supervision provides a principled and practical path toward reliable RAG systems.
  arXiv  Detail & Related papers  (2025-12-12T14:50:38Z)
• 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)
• Thinking Forward and Backward: Multi-Objective Reinforcement Learning for Retrieval-Augmented Reasoning [137.33138614095435]
  Retrieval-augmented generation (RAG) has proven to be effective in mitigating hallucinations in large language models.<n>Recent efforts have incorporated search-based interactions into RAG, enabling iterative reasoning with real-time retrieval.<n>We propose Bi-RAR, a novel retrieval-augmented reasoning framework that evaluates each intermediate step jointly in both forward and backward directions.
  arXiv  Detail & Related papers  (2025-11-12T08:29:39Z)
• Quantifying Document Impact in RAG-LLMs [9.10734114158633]
  We introduce the Influence Score (IS), a novel metric based on Partial Information Decomposition that measures the impact of each retrieved document on the generated response.<n>First, a poison attack simulation across three datasets demonstrates that IS correctly identifies the malicious document as the most influential in $86%$ of cases.<n>Second, an ablation study shows that a response generated using only the top-ranked documents is consistently judged more similar to the original response than one generated from the remaining documents.
  arXiv  Detail & Related papers  (2025-10-27T00:47:13Z)
• PoU: Proof-of-Use to Counter Tool-Call Hacking in DeepResearch Agents [24.502121097996294]
  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.
  arXiv  Detail & Related papers  (2025-10-13T02:45:37Z)
• 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)
• 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)
• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications.<n>Our research identifies two critical latent factors affecting RAG's confidence in its predictions.<n>We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)

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.