Papers with First-Order Logic
MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection (2025.coling-main)
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| Challenge: | Existing knowledge triples are ineffective in medical question-answering because of superfluous data and inability to capture complex relationships between symptoms and treatments. |
| Approach: | They propose a first-order logical reasoning model that uses First-Order Logic to model intricate relationships between diseases and treatments. |
| Outcome: | The proposed model captures the interplay of symptoms, diseases, and treatments, enhancing context comprehension. |
From Query to Logic: Ontology-Driven Multi-Hop Reasoning in LLMs (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit limitations in complex multi-hop question answering tasks that necessitate non-linear, structured reasoning. |
| Approach: | They propose an ontology-driven reasoning and chain framework that combines LLMs’ generative capabilities with the structural benefits of knowledge graphs. |
| Outcome: | Extensive experiments across a diverse set of models and standard MQA benchmarks demonstrate that the proposed framework achieves competitive performance while producing more interpretable reasoning chains. |
Faithful Logical Reasoning via Symbolic Chain-of-Thought (2024.acl-long)
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| Challenge: | SymbCoT is a framework that integrates symbolic expressions and logic rules with CoT prompting. |
| Approach: | They propose a Symbolic Chain-of-Thought framework that integrates symbolic expressions and logic rules with CoT prompting. |
| Outcome: | The proposed framework improves on 5 standard datasets with symbolic expressions and rules . it shows that it is more faithful, flexible, and explainable than the current method . |
Assessing the Sensitivity and Alignment of FOL Closeness Metrics (2025.findings-emnlp)
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| Challenge: | a recent paradigm of logical reasoning with tool-augmented large language models has been successful, but the correctness of FOL statements often go unverified due to the lack of a reliable evaluation metric. |
| Approach: | They compare sensitivity of existing metrics and their alignment with LLM to evaluate FOLs. |
| Outcome: | The proposed metrics improve the sensitivity and robustness of the proposed models. |
ReEfBench: Quantifying the Reasoning Efficiency of LLMs (2026.acl-long)
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| Challenge: | Existing methods for Chain-of-Thought evaluations do not distinguish between genuine reasoning and mere verbosity. |
| Approach: | They propose a framework for the non-intrusive, comprehensive process-centric evaluation of reasoning grounded in First-Order Logic. |
| Outcome: | The proposed framework identifies four distinct behavioral prototypes and diagnoses the failure modes. |
Inverting the Shield: Systematically Generating Safety Tests from Policy Specifications (2026.acl-long)
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| Challenge: | Existing safety evaluation paradigms rely on constructed benchmarks or dynamic red-teaming to probe potential vulnerabilities. |
| Approach: | They propose a framework that integrates specification-based software testing with AI safety. |
| Outcome: | The proposed framework achieves higher coverage and attack success counts compared to baselines. |