arXiv:2506.23274v4 Announce Type: replace Abstract: Recent reasoning language models, particularly those that employ long latent chains of thought, achieve strong performance on complex agentic tasks. However, as these models operate over increasingly long time horizons, their internal progress becomes opaque to users, making expectation management and real-time oversight difficult. In this work, we investigate whether real-time progress prediction is feasible for such models. We first test whether hidden states encode progress information by discretizing reasoning trajectories and training a
The increasing complexity of reasoning by large language models, particularly with long 'chains of thought,' necessitates new methods for real-time monitoring and control as they are deployed.
The ability to predict the progress of AI agents in real-time is crucial for building trustworthy, controllable, and efficient autonomous systems, addressing a key limitation for their widespread adoption.
This research outlines a method to make the internal state and progress of complex AI reasoning more transparent, enabling better user oversight and expectation management for agentic tasks.
- · AI developers
- · AI-powered businesses
- · Robotics
- · Autonomous systems
- · Opaque AI systems
- · Manual oversight processes
Improved debugging and reliability of advanced AI agents operating over extended periods.
Accelerated deployment of AI agents in critical applications due to enhanced transparency and control.
New regulatory frameworks and audit requirements that mandate real-time explainability for autonomous AI systems.
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