arXiv AI
From Document Layout to Causal Topology: An End-to-End Architecture for Temporal Causal Reasoning
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CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching
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TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation
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TempoBench: Evaluating Temporal Causal Reasoning in Large Language Models
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Causal Agent based on Large Language Model
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Towards a Mechanistic Understanding of Propositional Logical Reasoning in Large Language Models
The paper investigates how large language models perform propositional logical reasoning by conducting a causal mechanistic analysis on the PropLogic-MI benchmark. It identifies four interlocking mechanisms—Staged Computation, Information Transmission, Fact Retrospection, and Specialized Attention Heads—that organize the reasoning process across layers. The study demonstrates that these mechanisms recur across different model families, rule categories, and reasoning hops, indicating a structured, layer‑organized internal process for propositional reasoning.