Uncovering Why LLMs Fail: From Internal Mechanisms to Adversarial Reasoning Trajectories

The paper introduces CauTrace, a white‑box diagnostic framework that analyzes why large language models (LLMs) fail by examining internal states, representation propagation, and temporal coordination during reasoning. It defines seven functional states to capture support and constraint processing, tracks how these states evolve across Transformer blocks, and studies the timing of commitment and verification steps. Experiments on Qwen and LLaMA-family models show distinct failure signatures, and repair/steering tests confirm that the framework’s signals align with expected changes in the inference process.

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• llms
• safety