AGENTIC-SYSTEMSApril 20, 20261 min read5 views

Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants

Researchers have introduced a symbolic reasoning scaffold that utilizes Peirce's tripartite inference and algebraic invariants to prevent logical inconsistencies and error propagation in Large Language Models.

Computer Science > Artificial Intelligence

Title:Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants

View PDF HTML (experimental)Abstract:Large language models exhibit systematic limitations in structured logical reasoning: they conflate hypothesis generation with verification, cannot distinguish conjecture from validated knowledge, and allow weak reasoning steps to propagate unchecked through inference chains. We present a symbolic reasoning scaffold that operationalizes Peirce's tripartite inference -- abduction, deduction, and induction -- as an explicit protocol for LLM-assisted reasoning. The framework enforces logical consistency through five algebraic invariants (the Gamma Quintet), the strongest of which -- the Weakest Link bound -- ensures that no conclusion in a reasoning chain can exceed the reliability of its least-supported premise. This principle, independently grounded as weakest link resolution in possibilistic logic and empirically validated for chain-of-thought reasoning, prevents logical inconsistencies from accumulating across multi-step inference. We verify all invariants through a property-based testing suite of 100 properties and 16 fuzz tests over 10^5+ generated cases, providing a verified reference implementation of the invariants suitable as a foundation for future reasoning benchmarks.

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Source: arXiv cs.AI Recent

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