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Graph of States: Solving Abductive Tasks with Large Language Models

arXiv:2603.21250v1 Announce Type: new 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 appro

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Yu Luo, Rongchen Gao, Lu Teng, Xidao Wen, Jiamin Jiang, Qingliang Zhang, Yongqian Sun, Shenglin Zhang, Jiasong Feng, Tong Liu, Wenjie Zhang, Dan Pei · · 1 min read · 6 views
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arXiv:2603.21250v1 Announce Type: new 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.

Executive Summary

This article introduces Graph of States (GoS), a neuro-symbolic framework designed to overcome the limitations of existing frameworks in solving abductive tasks using Large Language Models (LLMs). GoS addresses the challenges of unstructured state representation and lack of explicit state control by utilizing a causal graph and state machine to encode logical dependencies and govern valid transitions. The framework enables directed search and convergent reasoning, outperforming all baselines in evaluations on two real-world datasets. The article showcases the potential of GoS in solving complex abductive tasks and highlights its robustness in addressing Evidence Fabrication, Context Drift, Failed Backtracking, and Early Stopping. The significance of GoS lies in its ability to generalize to abductive reasoning, a critical yet underexplored aspect of logical reasoning.

Key Points

  • ▸ GoS addresses the limitations of existing frameworks in solving abductive tasks using LLMs
  • ▸ The framework utilizes a causal graph and state machine to encode logical dependencies and govern valid transitions
  • ▸ GoS outperforms all baselines in evaluations on two real-world datasets

Merits

Strength

GoS's structured state representation and explicit state control enable directed search and convergent reasoning, addressing Evidence Fabrication, Context Drift, Failed Backtracking, and Early Stopping.

Robustness

GoS's robustness in solving complex abductive tasks makes it a reliable solution for LLMs.

Generalizability

GoS's ability to generalize to abductive reasoning, a critical aspect of logical reasoning, makes it a significant contribution to the field.

Demerits

Limitation

The framework's complexity and require significant computational resources to implement.

Scalability

The performance of GoS may degrade as the size of the causal graph and state machine increases.

Expert Commentary

The introduction of Graph of States (GoS) is a significant contribution to the field of AI, addressing a long-standing challenge in solving abductive tasks using Large Language Models. GoS's ability to generalize to abductive reasoning is a critical aspect of logical reasoning, and its robustness in solving complex tasks makes it a reliable solution. However, the framework's complexity and scalability issues are areas for further research. The article's implications for AI systems and policy development are substantial, and further work is needed to explore the full potential of GoS.

Recommendations

  • ✓ Further research is needed to explore the scalability and performance of GoS on larger datasets and more complex tasks.
  • ✓ The development of GoS has significant practical implications for AI systems, and its adoption should be encouraged in industries where complex abductive tasks are prevalent.

Sources

Original: arXiv - cs.AI

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