Diagnosing and Repairing Factual Errors in RAG under Budget Constraints

• URL: http://arxiv.org/abs/2606.29377v1
• Date: Sun, 28 Jun 2026 13:01:35 GMT
• Title: Diagnosing and Repairing Factual Errors in RAG under Budget Constraints
• Abstract summary: We propose D2R-RAG (Diagnose-to-Repair RAG), a model-agnostic and resource-aware framework that combines lightweight failure diagnosis with adaptive repair.<n>D2R-RAG derives interpretable failure signatures from observable signals in the query, retrieved evidence, and generated response, and then selects from a small set of corrective actions under explicit latency and VRAM constraints.
• Score: 3.9980186127766935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real-world deployments remain fragile. Failures often stem from missing or weakly relevant evidence, as well as from generation that does not faithfully reflect the retrieved context. Many existing approaches rely on fine-tuning, privileged access to internal model signals, or resource-insensitive escalation strategies, which limits their practicality in black-box and budget-constrained settings. We propose D2R-RAG (Diagnose-to-Repair RAG), a model-agnostic and resource-aware framework that combines lightweight failure diagnosis with adaptive repair. D2R-RAG derives interpretable failure signatures from observable signals in the query, retrieved evidence, and generated response, and then selects from a small set of corrective actions under explicit latency and VRAM constraints. Experiments on FEVER and HotpotQA show that D2R-RAG improves reliability over recent baselines and achieves better accuracy--efficiency trade-offs across multiple compute budgets. The code is available at https://github.com/CyberScienceLab/D2R-RAG/.

Related papers

• FailForge: Distilling Procedural Competence from Persistent Failures into Code Agents [49.519184792774304]
  FailForge is an agentic framework that converts failed rollouts into training signal.<n>FailForge recovers over 26% of previously failed instances at marginal additional cost.
  arXiv  Detail & Related papers  (2026-08-09T08:22:57Z)
• REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling [56.888597798431704]
  Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set of known categories.<n>Existing frameworks rely heavily on exemplar replay to mitigate catastrophic forgetting, but in some real applications, storing raw data conflicts with data access restrictions and leads to data exposure risks.<n>We propose REAL-OW, a novel rehearsal-free framework that decouples incremental knowledge through a collaborative adapter architecture.
  arXiv  Detail & Related papers  (2026-07-03T06:38:54Z)
• Revisiting Vul-RAG: Reproducibility and Replicability of RAG-based Vulnerability Detection with Open-Weight Models [0.2730969268472861]
  We present a study of Vul-RAG, a framework for source code vulnerability detection that generalizes with high-level vulnerability knowledge.<n>Results confirm that the findings of Vul-RAG are reproducible under local deployment, but with minor deviations.<n>We discuss practical implications and trade-offs between detection effectiveness, model capabilities, and model scale.
  arXiv  Detail & Related papers  (2026-06-03T11:20:37Z)
• Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking [18.698122955613584]
  InSemRAG is a framework for a retrieval-augmented generation (RAG) system.<n>IAR implements a dynamic hybrid retrieval method that adaptively weights the retrieval channels based on the query intent.<n>SPC performs detection and reparation to the damaged evidence chunks to preserve the semantic integrity.<n>Our method achieves significant gains on multi-hop and evidence-sensitive tasks, with a 2.65-point improvement in F1 on HotPotQA and a 1.5-point increase in accuracy on FEVER.
  arXiv  Detail & Related papers  (2026-05-31T13:42:36Z)
• FD-RAG: Federated Dual-System Retrieval-Augmented Generation [6.293987395462135]
  FD-RAG learns semantic-aware adaptive hypergraphs over local corpora and distills them into compact QA memories.<n>It answers well-covered queries via direct memory matching and invokes LLM-based reasoning only when necessary.<n>Experiments on QA benchmarks show that FD-RAG improves accuracy by up to 7.8% while reducing latency by 8.4times$ compared with strong local and federated baselines.
  arXiv  Detail & Related papers  (2026-05-22T05:21:39Z)
• Reasoning-Aware AIGC Detection via Alignment and Reinforcement [55.09684020007737]
  REVEAL is a framework that generates interpretable reasoning chains before classification.<n>It achieves state-of-the-art performance across multiple benchmarks.
  arXiv  Detail & Related papers  (2026-04-21T07:29:55Z)
• Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing [12.09179106162719]
  We propose Skill-RAG, a failure-aware RAG framework that couples a hidden-state prober with a prompt-based skill router.<n> Experiments show that Skill-RAG substantially improves accuracy on hard cases persisting after multi-turn retrieval.
  arXiv  Detail & Related papers  (2026-04-17T07:25:43Z)
• Doctor-RAG: Failure-Aware Repair for Agentic Retrieval-Augmented Generation [23.961760006898157]
  Agentic Retrieval-Augmented Generation (Agentic RAG) has become a widely adopted paradigm for question answering and complex knowledge reasoning.<n>Existing approaches typically address such failures by either stopping at diagnostic analysis or rerunning the entire retrieval-reasoning pipeline.<n>We propose Doctor-RAG, a unified diagnose-and-repair framework that corrects failures in Agentic RAG through explicit error localization and prefix reuse.
  arXiv  Detail & Related papers  (2026-04-01T13:13:27Z)
• Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation [64.09110141948693]
  Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise.<n>We propose BAR-RAG, which reframes the reranker as a boundary-aware evidence selector that targets the generator's Goldilocks Zone.<n>Bar-RAG consistently improves end-to-end performance under noisy retrieval.
  arXiv  Detail & Related papers  (2026-02-03T16:08:23Z)
• 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)
• UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation [93.38604803625294]
  We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) We use Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. UncertaintyRAG outperforms baselines by 2.03% on LLaMA-2-7B, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-03T17:39:38Z)

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.