Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition
Large Language Model interfaces are increasingly verbose, exposing intermediate reasoning traces alongside final answers. Traces are framed as transparency mechanisms, yet it is unclear how people use them to solve problems. We report a preregistered between-subjects study (N = 559) in which participants solved ten LSAT-style reasoning problems under one of three conditions: an Answer-only baseline, a Full-trace revealed before the answer, and a Summary-trace presented alongside the answer. Summaries preserved task performance at the no-trace baseline while significantly elevating trust and hedonic appeal, establishing that trace exposure shifts subjective appraisal of the interaction wit...
| Authors | Daniela Fernandes, Daniel Buschek, Lev Tankelevitch et al. |
|---|---|
| Primary category | cs.HC |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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