Evidence-Preserving Interpretation for Personalized LLM Agents

Personalized LLM agents must turn past interactions into responses that fit a user's current needs. Retrieved passages preserve the original history but leave the agent to work out what they imply for the current request. Compressed memories state an implication but can drop the context that shows whose preference it is and whether the user has withdrawn it. We propose Evince (EVidence-preserving INterpretation of Conversational Excerpts), a memory framework that interprets retrieved history for the current request. A learned interpreter states the relevant preference, its subject, and its withdrawal status, and a frozen LLM agent receives this note together with the unchanged excerpts. To align training with deployment, retrieval-aligned training replaces an annotated training input with the selector's actual retrieval whenever that retrieval contains the annotated user turns, keeping every preference target unchanged. On PersonaMem-v2 with 32k and 128k histories, Evince outperforms seven baselines in overall accuracy with two LLM agents, by up to 9.46 points over the strongest, while the agent reads far fewer tokens than with the full history. Ablations show that the note improves accuracy with the retrieved evidence held fixed, that retrieval-aligned training adds further gains, and that evidence and interpretation play complementary roles.