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PathAnchor: Path-Structured Evidence for Scientific Agents

arXiv 2609.38766 ↗First posted 30 Sep 2026cs.AIcited by 0

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Abstract (arXiv)

Scientific agents can retrieve relevant passages yet still lose functional order, mix evidence across sources, or state conclusions that exceed the retrieved record. We introduce PathAnchor, a bounded scientific reasoning system built on path-structured evidence workspaces. Instead of treating passages or extracted concepts as independent units, the system retrieves source-linked Material-Sensor-Signal-System trajectories that preserve role, direction, and the evidence supporting each transition. A controller uses three read-only tools to search paper-specific trajectories, trace paths across candidate sources, and open exact evidence before producing a claim-cited answer and an explicit evidence boundary. On 120 single- and cross-paper flexible-sensor questions, PathAnchor scores 82.6% and leads six evaluated systems. Under a matched controller, corpus, and six-call budget, replacing unordered concept graphs with path-structured records raises source recall from 61.3% to 82.9%, increases answers whose claims all cite opened evidence from 69.2% to 90.0%, and reduces tool calls. These results show that evidence organization affects retrieval and citation completeness under fixed agent resources.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 30 Sep 2026.

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References · 17

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  3. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  4. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  5. 2020Fact or Fiction: Verifying Scientific Claimscited by 554
  6. 2021A Dataset of Information-Seeking Questions and Answers Anchored in Research Paperscited by 486
  7. 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 322
  8. 2022Teaching language models to support answers with verified quotescited by 292
  9. 2023PaperQA: Retrieval-Augmented Generative Agent for Scientific Researchcited by 149
  10. 2024Language agents achieve superhuman synthesis of scientific knowledgecited by 137
  11. 2020SciREX: A Challenge Dataset for Document-Level Information Extractioncited by 113
  12. 2022SciFact-Open: Towards open-domain scientific claim verificationcited by 94
5 more
  1. 2024LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QAcited by 81
  2. 2025AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suitecited by 53
  3. 2023Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learningcited by 50
  4. 2025SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literaturecited by 4
  5. 2025SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Modelscited by 2

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  1. 2026Sci-MMR: Benchmarking Multi-Step Evidence-Grounded Scientific Reasoning in Multimodal Agents
  2. 2026From Papers to Mechanisms: An Evidence-Grounded Knowledge Substrate for Scientific Language Models
  3. 2026EviGraph: Evidence-Guided Autonomous Research Agents
  4. 2026SearchAtlas: Analyzing Agentic Search Strategies via Evidential Query Graphs
  5. 2026PathRouter: Aligning Rewards with Retrieval Quality in Agentic Graph Retrieval-Augmented Generation
  6. 2026Agents-K1: Towards Agent-native Knowledge Orchestration
  7. 2026LongHarness: Advancing Agentic Reasoning over 100M-Token Corpora via Stateful Evidence Management
  8. 2025Reasoning Paths as Signals: Augmenting Multi-hop Fact Verification through Structural Reasoning Progression