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Graph of States: Solving Abductive Tasks with Large Language Models

Yu Luo, Rongchen Gao, Lu Teng, Xidao Wen, Jiamin Jiang, Qingliang Zhang, Yongqian Sun, Shenglin Zhang, Jiasong Feng, Tong Liu, Wenjie Zhang, Dan Pei

Abstract

Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive reasoning remains significantly underexplored. Existing frameworks, predominantly designed for static deductive tasks, fail to generalize to abductive reasoning due to unstructured state representation and lack of explicit state control. Consequently, they are inevitably prone to Evidence Fabrication, Context Drift, Failed Backtracking, and Early Stopping. To bridge this gap, we introduce Graph of States (GoS), a general-purpose neuro-symbolic framework tailored for abductive tasks. GoS grounds multi-agent collaboration in a structured belief states, utilizing a causal graph to explicitly encode logical dependencies and a state machine to govern the valid transitions of the reasoning process. By dynamically aligning the reasoning focus with these symbolic constraints, our approach transforms aimless, unconstrained exploration into a convergent, directed search. Extensive evaluations on two real-world datasets demonstrate that GoS significantly outperforms all baselines, providing a robust solution for complex abductive tasks. Code repo and all prompts: https://anonymous.4open.science/r/Graph-of-States-5B4E.

Graph of States: Solving Abductive Tasks with Large Language Models

Abstract

Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive reasoning remains significantly underexplored. Existing frameworks, predominantly designed for static deductive tasks, fail to generalize to abductive reasoning due to unstructured state representation and lack of explicit state control. Consequently, they are inevitably prone to Evidence Fabrication, Context Drift, Failed Backtracking, and Early Stopping. To bridge this gap, we introduce Graph of States (GoS), a general-purpose neuro-symbolic framework tailored for abductive tasks. GoS grounds multi-agent collaboration in a structured belief states, utilizing a causal graph to explicitly encode logical dependencies and a state machine to govern the valid transitions of the reasoning process. By dynamically aligning the reasoning focus with these symbolic constraints, our approach transforms aimless, unconstrained exploration into a convergent, directed search. Extensive evaluations on two real-world datasets demonstrate that GoS significantly outperforms all baselines, providing a robust solution for complex abductive tasks. Code repo and all prompts: https://anonymous.4open.science/r/Graph-of-States-5B4E.
Paper Structure (19 sections, 1 equation, 10 figures, 5 tables, 2 algorithms)

This paper contains 19 sections, 1 equation, 10 figures, 5 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Dual-layer Framework
  5. Bi-Directional Neuro-Symbolic Interaction
  6. State Conversions: Backtracking and Drill-Down
  7. Experiments
  8. Medical Diagnosis
  9. Failure Diagnosis in Distributed Systems
  10. Conclusion
  11. Algorithm
  12. Limitations
  13. Descriptions of datasets
  14. Dataset for Medical Diagnosis
  15. Dataset for Failure Diagnosis in Distributed Systems
  16. ...and 4 more sections

Figures (10)

  • Figure 1: Illustration of reasoning frameworks applied to Deductive tasks (Left) versus Abductive tasks (Right). While deductive frameworks succeed in static logic (e.g., Game of 24), applying them to abductive tasks exposes four deficiencies: (1) Evidence Fabrication, (2) Context Drift, (3) Failed Backtracking, and (4) Early Stopping.
  • Figure 2: Overview of GoS.Left: Schematic of the dual-layer architecture. Right: The iterative inference workflow.
  • Figure 3: Bi-Directional Neuro-Symbolic Interaction.
  • Figure 4: State Conversions: Backtracking & Drill-Down.
  • Figure 5: Sensitivity Analysis. Solid line stands for GoS, dashed line stands for best baseline (Multi/FoT). (1) upper-left: maximum number of neuro-symbolic interaction iterations; (2) upper-right: maximum number of retrieval actions of expert agent; (3) lower-left: minimum support evidence for drill-down transition; (4) lower-right: confidence gap for drill-down transition.
  • ...and 5 more figures