Related papers: Predicting thinking time in Reasoning models

Predicting thinking time in Reasoning models

URL: http://arxiv.org/abs/2506.23274v1
Date: Sun, 29 Jun 2025 15:01:01 GMT
Title: Predicting thinking time in Reasoning models
Authors: Hans Peter Lynsgøe Raaschou-jensen, Constanza Fierro, Anders Søgaard,
Abstract summary: Reasoning models produce long, hidden chains of thought.<n>Users have little insight into how much time the model will spend reasoning before returning an answer.
Score: 42.58699486487709
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Reasoning models that produce long, hidden chains of thought have emerged as powerful tools for complex, reasoning-intensive tasks\citep{deepseekai2025deepseekr1incentivizingreasoningcapability, openai2024openaio1card}. However, this paradigm introduces a new user experience challenge: users have little insight into how much time the model will spend reasoning before returning an answer. This unpredictability, can lead to user frustration and is likely to compound as LLMs can produce increasingly long tasks asynchronously \citep{kwa2025measuringaiabilitycomplete}. In this paper, we introduce and evaluate methods for both online and offline prediction of model "thinking time," aiming to develop a practical "progress bar for reasoning." We discuss the implications for user interaction and future research directions.

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