Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention
arXiv:2606.01243v1 Announce Type: new Abstract: 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. We first present a systematic analysis using structural, causal, and geometric probes, revealing that latent vectors encode compressed, faithful representations of reasoning ste
The paper provides a timely advancement in AI interpretability just as Large Language Models are becoming ubiquitous, addressing a critical bottleneck in their reliable deployment.
Understanding and controlling latent reasoning in LLMs is crucial for ensuring their safety, reliability, and ultimately, their broader adoption in sensitive applications.
This interpretability-guided approach moves beyond surface-level understanding of LLM outputs to direct intervention in their internal thought processes, enhancing control and debugging capabilities.
- · AI Safety Researchers
- · LLM Developers
- · AI-reliant Industries
- · Black-Box AI Solutions
- · Companies reliant on opaque LLMs
Improved reliability and explainability of Large Language Models.
Accelerated development and adoption of AI systems in highly regulated or safety-critical domains.
Enhanced trust in autonomous AI agents, potentially leading to more complex deployments and increased societal integration.
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Read at arXiv cs.CL