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Evidence record 6786 · automatically gathered

BeliefShift: Benchmarking Temporal Belief Consistency and Opinion Drift in LLM Agents

LLMs are increasingly used as long-running conversational agents, yet every major benchmark evaluating their memory treats user information as static facts to be stored and retrieved. That's the wrong model. People change their minds, and over extended interactions, phenomena like opinion drift, over-alignment, and confirmation bias start to matter a lot. BeliefShift introduces a longitudinal benchmark designed specifically to evaluate belief dynamics in multi-session LLM interactions. It covers

Record details

Published: 25 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (25 March 2026), “BeliefShift: Benchmarking Temporal Belief Consistency and Opinion Drift in LLM Agents,” evidence record 6786, https://ethics.ai/record/6786 (originally published by arXiv).

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