AI RESEARCH
Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention
arXiv CS.LG
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ArXi:2606.01243v1 Announce Type: cross Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over explicit Chain-of-Thought (CoT). However, the opacity of these continuous thought vectors hinders their reliability and controllability. This paper bridges the gap between mechanistic interpretability and actionable control.