{"data":{"id":1,"backendId":"830b7908-2aa8-43f7-ab1b-121c13a802ca","title":"Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs","summary":"arXiv:2603.00024v1 Announce Type: new Abstract: Large Language Models (LLMs) are prone to sycophantic behavior, uncritically conforming to user beliefs. As models increasingly condition responses on user-specific context (personality traits, preferences, conversation history), they gain information to tailor agreement more effectively. Understanding how personalization modulates sycophancy is critical, yet systematic evaluation across models and contexts remains limited. We present a rigorous ev","analysis":"This original research addresses a critical bottleneck in AI agent development: the trade-off between user-centricity and model integrity.","category":"technology","strategicTrack":"ai_agents","capitalRelevance":{"social":3,"cultural":4,"economic":5,"physical":1,"symbolic":4,"temporal":6,"informational":8,"psychological":7,"technological":9},"tags":["LLM","Sycophancy","Personalization","AI Alignment","Epistemic Independence"],"qualityScore":10,"valueScore":8,"interestScore":8,"potentialScore":9,"uniquenessScore":9,"sourceCount":1,"confidence":5,"detectedAt":"2026-03-04T10:19:19.823Z","createdAt":"2026-03-04 13:49:26"}}