Large vision-language models (LVLMs) remain prone to hallucination. While recent visual Direct Preference Optimization (DPO) methods try to fix this by contrasting positive images with heuristic or offline negatives for the same correct response. We show that such negatives are mostly "trivial": the model already assigns near-zero probability to the correct response, so the resulting gradient is uninformative or even spuriously misdirected. We propose OViP, an on-policy framework that synthesizes negative images on the fly from the model’s own hallucinated responses via a diffusion prior, producing semantically misaligned yet still high-likelihood negatives. Across five hallucination and four general benchmarks, OViP achieves better hallucination mitigation on LLaVA-1.5 series and Qwen2.5-VL-3B, while being 2 times more compute-efficient than SIMA and incurring no degradation on general capabilities.

LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract preferences from noisy interaction histories, and select among competing alternatives. Existing benchmarks rarely test this capability, as they often rely on user-refined queries or simplified histories. We introduce personalized product search (PPS), a testbed for agentic personalization under raw queries and diverse histories. We construct **A**gent **Pe**rsonalized **B**enchmark (**APeB**) from action logs, pairing underspecified intents with rich histories and user-viewed candidate items. Evaluating state-of-the-art LLMs with multi-step agent workflows, we find that models handle explicit queries well but struggle with early-stage queries requiring intent and preference discovery. Rubric analysis attributes this gap mainly to ineffective history use. A simple history-aware query-refinement pipeline, VQRA, yields consistent gains, highlighting the need for dedicated history-utilization modules in personalized agents.