mlsanityData to 7 Oct 2026

Residual Drift Dominates Contradiction in Multi-Turn Constraint Reasoning

arXiv 2605.23940 ↗First posted 28 Apr 2026cs.AI cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

How do multi-turn reasoning systems fail? The expected answer is logical contradiction, in which the system's maintained state becomes unsatisfiable. We show that the dominant mode is instead satisfiable drift, where the internal state stays consistent while the returned answer silently violates prior commitments. We build DRIFT-Bench (Decomposing Reasoning Into Failure Types), a solver-instrumented benchmark of 816 test problems across three constraint domains, and evaluate four methods on it across four open-weight models (8B-120B parameters). MUS-Repair, which feeds minimal unsatisfiable subsets back to the generator, is strongest in every setting (+1.8 to +15.0 pp over the best non-MUS baseline). But the central finding is what repair leaves behind. After structured feedback, models rarely contradict themselves. They forget. Residual errors are 98-100% satisfiable drift across all settings, while contradiction drops to near zero. Reliable multi-turn systems must separately validate that the returned answer respects the maintained state. Code is available at https://github.com/kaons-research/drift-bench.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 28 references that are in the map. First posted on arXiv 28 Apr 2026.

Builds on

22 of 28 references.

References · 22

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2025Qwen3 Technical Reportcited by 8,195
  5. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  7. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  8. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  9. 2023Let's Verify Step by Stepcited by 3,598
  10. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  11. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  12. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
10 more
  1. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
  2. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,085
  3. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  4. 2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 352
  5. 2023Faithful Chain-of-Thought Reasoningcited by 285
  6. 2019Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systemscited by 215
  7. 2024On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Taskscited by 137
  8. 2024HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Modelcited by 101
  9. 2023COLLIE: Systematic Construction of Constrained Text Generation Taskscited by 69
  10. 2025VerifiAgent: a Unified Verification Agent in Language Model Reasoningcited by 10

Built on by

Not cited yet.