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Formal Logic

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Showing 1–25 of 73 papers

TitleStatusHype
Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic CorpusCode2
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
Leveraging Rust types for modular specification and verificationCode2
Training Step-Level Reasoning Verifiers with Formal Verification ToolsCode1
Learning Deductive Reasoning from Synthetic Corpus based on Formal LogicCode1
Leveraging Large Language Models to Generate Answer Set ProgramsCode1
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic TranslationCode1
Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal LogicCode1
LTLCrit: A Temporal Logic-based LLM Critic for Safe and Efficient Embodied Agents—0
On Immutable Memory Systems for Artificial Agents: A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains—0
Are LLMs Reliable Translators of Logical Reasoning Across Lexically Diversified Contexts?Code0
Alpay Algebra III: Observer-Coupled Collapse and the Temporal Drift of Identity—0
Reasoning Under Threat: Symbolic and Neural Techniques for Cybersecurity Verification—0
Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approach—0
Building Knowledge Graphs Towards a Global Food Systems Datahub—0
Linguistic Generalizations are not Rules: Impacts on Evaluation of LMs—0
Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey—0
Automating Mathematical Proof Generation Using Large Language Model Agents and Knowledge Graphs—0
From An LLM Swarm To A PDDL-Empowered HIVE: Planning Self-Executed Instructions In A Multi-Modal Jungle—0
Formal Logic-guided Robust Federated Learning against Poisoning Attacks—0
Interchangeable Token Embeddings for Extendable Vocabulary and Alpha-Equivalence—0
Rulebreakers Challenge: Revealing a Blind Spot in Large Language Models' Reasoning with Formal Logic—0
CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought ReasoningCode0
Boosting Deductive Reasoning with Step Signals In RLHF—0
Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning—0
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