mlsanityData to 7 Oct 2026

Before Reasoning Can Fail: Pre-Evidence Procedural Failures in Agentic RAG

arXiv 2608.02011 ↗First posted 3 Aug 2026cs.AIcited by 0

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

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Abstract (arXiv)

Agentic retrieval-augmented generation (RAG) systems can fail before evidence-conditioned reasoning is tested: an agent may retrieve candidate snippets but finalize without inspecting them. We study this failure mode as a procedural property of the agent trajectory, decomposing wrong answers into pre-evidence discipline failures and post-gold-read failures using saved tool-call traces, retrieved evidence, read passages, and final answers. Across 12,000 paired trajectories on HotpotQA, 2WikiMultiHopQA, and MuSiQue, the two failure types are largely non-redundant: the both-trigger rate is in [11.2%, 13.1%] across regex and spaCy entity extractors. We then evaluate Read-Gate, a minimal runtime invariant requiring an agent to read after search and before finalization. Forced reading improves LLM-Acc by 14.9-19.9 points on trajectories that would otherwise skip reading and by 3.2-9.4 points on full minimal-reasoning cells. Additional diagnostics show that larger hidden thinking budgets do not necessarily increase evidence inspection. Together, these results indicate that evidence-gathering should be evaluated as a trajectory-level control problem, separately from answer-side reasoning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 40 references that are in the map. First posted on arXiv 3 Aug 2026.

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10 of 40 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces3 Feb
  2. 2025Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs1 Oct
  3. 2025ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning25 Mar
  4. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning12 Mar
  5. 2025R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning7 Mar

Other references · 5

  1. 2024Qwen2.5 Technical Reportcited by 4,713
  2. 2023Let's Verify Step by Stepcited by 3,598
  3. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  4. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
  5. 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175

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  1. 2026AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG
  2. 2026Skill-RAG: Failure-State-Aware Retrieval Augmentation via Hidden-State Probing and Skill Routing
  3. 2026Track, Rank, Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents
  4. 2026ROZA Graphs: Self-Improving Near-Deterministic RAG through Evidence-Centric Feedback
  5. 2026Doctor-RAG: A Failure-Aware Repair Framework for Agentic Retrieval-Augmented Generation
  6. 2026When Failures Propagate: Causal Failure Attribution in Agentic Retrieval-Augmented Generation
  7. 2025RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
  8. 2025Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges