AI RESEARCH
Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation
arXiv CS.CV
•
ArXi:2604.09368v1 Announce Type: cross Large language model (LLM) agents are increasingly deployed as scalable user simulators for recommender system evaluation. Yet existing simulators perceive recommendations through text or structured metadata rather than the visual interfaces real users browse-a critical gap, since attention over recommendation layouts is both visually driven and highly personalized. We investigate whether aligning a vision-language model's (VLM's) visual attention with user-specific gaze patterns can improve simulation fidelity.