Empirical Characterization of Inference-Time Elicited Probability Transformations in Large Language Models
arXiv:2603.19262v2 Announce Type: replace-cross Abstract: Large language models increasingly rely on inference-time procedures such as chain-of-thought reasoning, self-refinement, retrieval augmentation, and verifier-guided revision, yet the structure of elicited probability transformations under these procedures remains poorly understood. We study externally elicited probability assignments over candidate answers and observe recurring approximate log-ratio relationships: \[ \log \tilde q_t(i) = \alpha_t \left( \log q_t(i) + \log b_t(i) \right) + c_t, \] where $q_t$ and $\tilde q_t$ are pre- a
The increasing reliance on complex inference-time procedures in large language models necessitates a deeper understanding of how these models transform probabilities to ensure reliability and advanced capabilities.
Understanding the empirical probability transformations in LLMs is crucial for developing more robust, transparent, and controllable AI systems, impacting their real-world deployment across various domains.
This research provides a foundational empirical characterization of how LLMs process and transform probabilities during complex reasoning steps, which was previously poorly understood.
- · AI Researchers
- · AI Developers
- · AI Safety Organizations
- · Developers of opaque LLM systems
It provides a mathematical framework for analyzing and predicting the behavior of LLMs during complex inference tasks.
This improved understanding could lead to more efficient and reliable AI agents and systems by optimizing their reasoning processes.
It might enable the development of new model architectures or fine-tuning methods that are explicitly designed to handle probabilistic transformations more effectively.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI