Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants
- URL: http://arxiv.org/abs/2604.15727v1
- Date: Fri, 17 Apr 2026 05:59:16 GMT
- Title: Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants
- Abstract summary: Large language models exhibit systematic limitations in structured logical reasoning.<n>We present a symbolic scaffold that operationalizes Peirce's tripartite inference as an explicit protocol for LLM-assisted reasoning.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- Autoformalizing Argumentative Material Inferences [26.35292656634472]
We develop an autoformalization for argumentative material inference as guard completion.<n>GUARD yields significant improvements in verified-faithful.<n>We show that the symbolic soft critique and the explicit assumption layer account for most of these gains.
arXiv Detail & Related papers (2026-09-15T11:05:26Z) - CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching [50.65932158912512]
We propose a new causal reasoning benchmark, CausalFlip, to encourage the development of new large language models.<n>CaulFlip consists of causal judgment questions built over event triples that could form different confounder, chain, and collider relations.<n>We evaluate LLMs under multiple training paradigms, including answer-only training, explicit Chain-of-Thought supervision, and a proposed internalized causal reasoning approach.
arXiv Detail & Related papers (2026-02-23T18:06:15Z) - Towards Generalizable Reasoning: Group Causal Counterfactual Policy Optimization for LLM Reasoning [50.352417879912515]
Large language models (LLMs) excel at complex tasks with advances in reasoning capabilities.<n>We propose Group Causal Counterfactual Policy Optimization to explicitly train LLMs to learn generalizable reasoning patterns.<n>We then construct token-level advantages from this reward and optimize the policy, encouraging LLMs to favor reasoning patterns that are process-valid and counterfactually robust.
arXiv Detail & Related papers (2026-02-06T08:03:11Z) - Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification [49.506412445511934]
Large Language Models (LLMs) show remarkable capabilities, yet their next-token prediction creates logical inconsistencies and reward hacking.<n>We introduce a formal logic verification-guided framework that dynamically interleaves formal symbolic verification with the natural language generation process.<n>We operationalize this framework via a novel two-stage training pipeline that synergizes formal logic verification-guided supervised fine-tuning and policy optimization.
arXiv Detail & Related papers (2026-01-30T07:01:25Z) - Beyond Correctness: Exposing LLM-generated Logical Flaws in Reasoning via Multi-step Automated Theorem Proving [11.24425572063955]
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, leading to their adoption in high-stakes domains such as healthcare, law, and scientific research.<n>They often contain subtle logical errors masked by fluent language, posing significant risks for critical applications.<n>We present MATP, an evaluation framework for systematically verifying LLM reasoning via Multi-step Automatic Theorem Proving.
arXiv Detail & Related papers (2025-12-29T14:48:15Z) - From Hypothesis to Premises: LLM-based Backward Logical Reasoning with Selective Symbolic Translation [8.104087344683604]
We propose a novel framework, Hypothesis-driven Backward Logical Reasoning (HBLR)<n>The core idea is to integrate confidence-aware symbolic translation with hypothesis-driven backward reasoning.<n>HBLR consistently outperforms strong baselines in both accuracy and efficiency.
arXiv Detail & Related papers (2025-12-03T01:52:31Z) - Think Consistently, Reason Efficiently: Energy-Based Calibration for Implicit Chain-of-Thought [33.267497114389734]
Large Language Models (LLMs) have demonstrated strong reasoning capabilities through emphChain-of-Thought (CoT) prompting.<n>CoT methods rely on discrete token-level reasoning processes prone to error propagation and limited by vocabulary.<n>We propose EBM-CoT, an Energy-Based Chain-of-Thought framework that refines latent thought representations through an energy-based model.
arXiv Detail & Related papers (2025-11-10T14:10:58Z) - Are Language Models Efficient Reasoners? A Perspective from Logic Programming [109.47572890883248]
Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of human-like reasoning: efficiency.<n>We propose a framework for assessing LM reasoning efficiency through the lens of logic programming.
arXiv Detail & Related papers (2025-10-29T15:30:31Z) - Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models [2.172419551358714]
Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret.<n>We introduce Theorem-of-Thought (ToTh), a novel framework that models reasoning as collaboration among three parallel agents.<n> Experiments on symbolic (WebOfLies) and numerical (MultiArithm) reasoning benchmarks show that ToTh consistently outperforms CoT, Self-Consistency, and CoT-Decoding.
arXiv Detail & Related papers (2025-06-08T12:28:38Z) - CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection [60.98964268961243]
We propose that guiding models to perform a systematic and comprehensive reasoning process allows models to execute much finer-grained and accurate entailment decisions.<n>We define a 3-step reasoning process, consisting of (i) claim decomposition, (ii) sub-claim attribution and entailment classification, and (iii) aggregated classification, showing that such guided reasoning indeed yields improved hallucination detection.
arXiv Detail & Related papers (2025-06-05T17:02:52Z) - The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning [56.574829311863446]
Chain-of-Thought (CoT) prompting has been widely recognized for its ability to enhance reasoning capabilities in large language models (LLMs)<n>We demonstrate that CoT and its reasoning variants consistently underperform direct answering across varying model scales and benchmark complexities.<n>Our analysis uncovers a fundamental hybrid mechanism of explicit-implicit reasoning driving CoT's performance in pattern-based ICL.
arXiv Detail & Related papers (2025-04-07T13:51:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.