Retrospective Affective Belief Revision: When New Evidence Changes How LLM Agents Interpret Past Emotions

An agent can recall an interaction accurately and still misunderstand how a person felt. Later disclosures can change the meaning of an earlier silence, hesitation, or emotional response, creating a need to revisit past judgments. To address this issue, we introduce Retrospective Affective Belief Revision (RABR), a framework that connects newly available evidence to historical affective interpretations while preserving the original records and the history of belief changes. A compact Trigger determines when memory retrieval is needed, a reasoning agent grounds the relevant historical target, and an evidence-based updater distinguishes between revising a judgment and suspending its application. To study when such retrospection is warranted, we construct RABRBench, spanning compact controlled situations, extended histories, and source-preserving longitudinal conversations. Those tracks pair retrieval decisions with evidence-turn annotations and dependency-aware evaluation units, making selective access to the past an explicit learning and evaluation task. Integrated experiments show that selective retrieval reduces downstream model calls, while controlled correction studies demonstrate persistent evidence support and targeted evidence retraction. Together, RABR and RABRBench establish a framework and benchmark for studying when to revisit past emotional judgments and how to revise them through traceable evidence updates.