arXiv:2602.02103v2 Announce Type: replace Abstract: Chain-of-thought (CoT) reasoning has become a central mechanism for eliciting multi-step reasoning in Large Language Models (LLMs). Yet recent evidence presents a tension: hidden states appear to already encode future reasoning before CoT fully unfolds, while explicit steps still remain crucial for tasks requiring compositional computation. To deepen the understanding between LLM's internal states and its verbalized reasoning trajectories, we investigate the latent planning strength of LLMs, through our probing method, Tele-Lens, applying to
The proliferation of LLMs and their increasing application in complex reasoning tasks necessitates a deeper understanding of their internal mechanisms for continued development and deployment.
Understanding the planning horizon of LLMs is critical for improving their reliability, predictability, and ultimately, their autonomous agentic capabilities, impacting all sectors using advanced AI.
This research provides a new methodology to probe the internal planning capacity of LLMs, potentially leading to more efficient and robust model architectures and reasoning strategies.
- · AI researchers
- · Large Language Model developers
- · Companies deploying AI agents
- · AI infrastructure providers
- · AI models lacking sophisticated planning
- · Organizations relying on simple, opaque AI solutions
Improved understanding of LLM reasoning will accelerate the development of more capable AI models and agents.
Enhanced AI agents could automate more complex tasks, fundamentally altering white-collar work and service industries.
A truly advanced AI with deep latent planning could lead to new forms of problem-solving and scientific discovery currently beyond human capacity.
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Read at arXiv cs.LG