mlsanityData to 7 Oct 2026

Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck

arXiv 2601.12499 ↗First posted 18 Jan 2026cs.AI cs.LGcited by 3

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

Abstract (arXiv)

Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which causes them to overlook information at certain positions. Whether these failures stem from an inability to locate evidence (recognition failure) or integrate it (synthesis failure) is unclear. We introduce Multi-Focus Attention Instruction (MFAI), a semantic probe to disentangle these mechanisms by explicitly steering attention towards selected positions. Across 5 LLMs on two multi-hop QA tasks (MuSiQue and NeoQA), we identify the "Weakest Link Effect": in our 18-document, 3-bucket setting, multi-hop reasoning performance collapses to the level of the least visible evidence, governed by absolute position rather than the linear distance between facts. While matched MFAI resolves recognition bottlenecks, improving accuracy by up to 11.49% in low-visibility positions, misleading MFAI yields divergent effects modulated by task topology: entity-centric tasks with vertical reasoning chains are vulnerable, whereas event-centric tasks with horizontal evidence structures are more resilient. Finally, we demonstrate that thinking models utilizing System-2 reasoning effectively locate and integrate the required information, matching gold-only baselines even in noisy, long-context settings. Supplementary experiments on 2WikiMultiHopQA, extended 3-4 hop counts, and a 32B model confirm these findings generalize across datasets, reasoning depths, and model scales.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 35 of its 53 references that are in the map. First posted on arXiv 18 Jan 2026.

Builds on

35 of 53 references.

Closest ancestors · 7

Same direction, newest first

  1. 2024Lost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QA13 Dec
  2. 2024StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization11 Oct
  3. 2024Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context9 Oct
  4. 2024How Much Can RAG Help the Reasoning of LLM?3 Oct
  5. 2024Making Long-Context Language Models Better Multi-Hop Reasoners6 Aug
  6. 2024MoreHopQA: More Than Multi-hop Reasoning19 Jun
  7. 2024Do Large Language Models Latently Perform Multi-Hop Reasoning?26 Feb

Other references · 28

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  5. 2023Efficient Streaming Language Models with Attention Sinkscited by 2,115
  6. 2024RULER: What's the Real Context Size of Your Long-Context Language Models?cited by 1,270
  7. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  8. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
  9. 2022In-context Learning and Induction Headscited by 930
  10. 2023Extending Context Window of Large Language Models via Positional Interpolationcited by 721
  11. 2024Quiet-STaR: Language Models Can Teach Themselves to Think Before Speakingcited by 292
  12. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 275
16 more
  1. 2025Reasoning Models Can Be Effective Without Thinkingcited by 182
  2. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  3. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  4. 2021Do Long-Range Language Models Actually Use Long-Range Context?cited by 92
  5. 2019Compositional Questions Do Not Necessitate Multi-hop Reasoningcited by 87
  6. 2023System 2 Attention (is something you might need too)cited by 85
  7. 2020Shortformer: Better Language Modeling using Shorter Inputscited by 76
  8. 2023Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyondcited by 65
  9. 2024Eliminating Position Bias of Language Models: A Mechanistic Approachcited by 61
  10. 2025Retrieval-Augmented Generation with Conflicting Evidencecited by 60
  11. 2023Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?cited by 26
  12. 2025More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAGcited by 16
  13. 2025NeoQA: Evidence-based Question Answering with Generated News Eventscited by 8
  14. 2024Attention Speaks Volumes: Localizing and Mitigating Bias in Language Modelscited by 6
  15. 2025Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metricscited by 4
  16. 2025Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliabilitycited by 1
  2. 2026Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrievalcited by 0
  3. 2026Before Answering: Evidence Sufficiency under Size-Matched Memory Constructioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Models
  2. 2026Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models
  3. 2025Cognitive Load Limits in Large Language Models: Benchmarking Multi-Hop Reasoning
  4. 2024Towards Interpreting Language Models: A Case Study in Multi-Hop Reasoning
  5. 2023Memory Injections: Correcting Multi-Hop Reasoning Failures during Inference in Transformer-Based Language Models
  6. 2024Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
  7. 2025Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models
  8. 2026CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps