mlsanityData to 7 Oct 2026

DualGuard: Dual-Mode Quality Control for Logic-Preserving Data Augmentation

arXiv 2609.32431 ↗First posted 26 Sep 2026cs.CLcited by 0

On the map: LLM reasoning → Test-time compute and efficient thinking also fits: Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Large language models provide a practical way to generate augmented data for logical reasoning at scale, but a larger generation volume does not guarantee semantic, label, or logical reliability. Existing work has improved generation quality through generation constraints, candidate validation, filtering, and feedback-based revision; however, once a quality judgment is available, deciding whether a candidate should be retained, filtered, or repaired remains an important control problem. We propose DualGuard, a dual-mode quality-control framework for logic-preserving data augmentation. The first mode uses the current instance and candidate batch for selective retention, filtering, attribution, and targeted feedback. The second mode accumulates cross-instance execution records of augmentation actions on top of per-sample diagnosis and attribution, compares new executions against each action's own historical behavior, and supports retrospective anomaly inspection, targeted rollback, and bounded repair. Both modes share semantic verification and additionally use symbolic verification when a reliable logical form is available. Across seven downstream tasks in the Two-Stage Transfer setting, DualGuard achieves the highest Accuracy on five tasks and outperforms the no-augmentation BERT baseline on all seven. Controlled ablations further show complementary roles for Memory, Z3, and history-aware anomaly control.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 20 of its 28 references that are in the map. First posted on arXiv 26 Sep 2026.

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20 of 28 references.

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Same direction, newest first

  1. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing19 May
  2. 2023Self-Refine: Iterative Refinement with Self-Feedback30 Mar

Other references · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
  4. 2023ExpeL: LLM Agents Are Experiential Learnerscited by 814
  5. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  6. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 379
  7. 2020ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Languagecited by 309
  8. 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 230
  9. 2020ReClor: A Reading Comprehension Dataset Requiring Logical Reasoningcited by 211
  10. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  11. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 185
  12. 2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 128
6 more
  1. 2023SatLM: Satisfiability-Aided Language Models Using Declarative Promptingcited by 100
  2. 2024LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancementcited by 69
  3. 2021Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Textcited by 46
  4. 2024Empowering Large Language Models for Textual Data Augmentationcited by 17
  5. 2023Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoningcited by 11
  6. 2023Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulationcited by 9

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  4. 2024AIC CTU system at AVeriTeC: Re-framing automated fact-checking as a simple RAG task
  5. 2025LFC-DA: Logical Formula-Controlled Data Augmentation for Enhanced Logical Reasoning
  6. 2026Diagnosing and Repairing Factual Errors in RAG under Budget Constraints
  7. 2025seqBench: A Tunable Benchmark to Quantify Sequential Reasoning Limits of LLMs
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