Loading
Life sciences · Preprint
arXiv · September 8, 2026
Raises a question worth testing. It does not answer one.
This preprint introduces answer-distribution trajectories, a novel representational framework for tracking how language model predictions evolve during chain-of-thought reasoning. The work demonstrates that this finer-grained approach can distinguish reasoning dynamics missed by endpoint accuracy and entropy profiles alone, but remains a methodological contribution without validation of predictive utility or external outcome relevance.
Exploratory comparative analysis. Open-weight language models; reasoning benchmarks (specific datasets not named in abstract).. Intervention: Answer-distribution trajectory representation applied to chain-of-thought reasoning traces.. Compared with: Endpoint accuracy and entropy profiles..
Traces with identical endpoints and similar entropy profiles exhibit substantially different reasoning dynamics across the sixteen models studied. Substantial variation in dynamical profiles observed both within individual models and across different reasoning tasks. Training and inference choices systematically reshape answer-distribution profiles.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
This is a methodological paper introducing a novel analytical framework for LLM reasoning dynamics without empirical validation against clinical or standardized outcomes, suitable for hypothesis generation rather than evidence for practice.
As stated by the source record.
Quoted from the source exactly as published.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Chain-of-thought reasoning provides a structured computation between a model's input and final answer. Yet it is often evaluated through endpoint accuracy, which ignores the path taken to reach that answer. An emerging line of work addresses this limitation using entropy profiles, which track how uncertainty evolves over the reasoning process but do not reveal which competing hypotheses account for that uncertainty. We introduce answer-distribution trajectories, a stochastic-dynamics-inspired representation that tracks the model's full predictive distribution over answers as reasoning unfolds. As a strictly finer representation than endpoint and entropy summaries, answer-distribution trajectories enable us to characterize a trace through a dynamical reasoning profile spanning exploration, revision, motion, and commitment, and to distinguish different dynamical mechanisms of reasoning success and failure. Across sixteen open-weight language models and four reasoning benchmarks, we show that traces with the same endpoint and similar entropy profiles can exhibit substantially different reasoning dynamics. We further find substantial variation in these dynamics both within and across models and tasks, with different objectives favoring different dynamical profiles. Additionally, we show that training and inference choices systematically reshape these profiles. Our results suggest that answer-distribution trajectories provide a rich framework for analysing and evaluating the dynamics of LLM reasoning.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.