A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance. In daily use, useful cues come from what the agent observes and how the user interacts with the agent, and the agent must carry them forward from the current request to similar future tasks. Existing memory benchmarks usually test dialogue recall or task improvement in isolation, leaving the trajectory from streaming observations to later assistance largely untested. We introduce StreamMemBench, a streaming benchmark that constructs a two-step task sequence around each evidence anchor from EgoLife egocentric streams. The initial task tests evidence use, while the follow-up task tests whether feedback and interaction experience are reused. Four metrics diagnose evidence recall, initial evidence use, feedback incorporation, and follow-up reuse. Experiments with eight memory systems across two backbones show that current systems often fail to use observed evidence or turn feedback into reliable follow-up behavior, even when evidence is stored or feedback is incorporated locally.

Understanding online tutoring requires more than recognizing isolated events. A model must connect what a teacher says or does to how the learner responds and how the teacher subsequently adapts. Existing video benchmarks emphasize event and temporal understanding, while educational-dialogue benchmarks are largely transcript-based, leaving this interactional contingency underexplored. We introduce VIRA-Bench, a diagnostic benchmark for short-horizon pedagogical contingency in one-to-one tutoring videos. Grounded in the Initiation–Response–Follow-up structure and contingent teaching theory, VIRA-Bench contains 1,005 clips and 1,322 multiple-choice questions organized into three layers: Observable Evidence Grounding, Interaction Contingency Reasoning, and Support-Response Modeling. Items are built through researcher-led evidence auditing and expert verification to keep questions tied to observable interaction evidence. Across 11 multimodal large language models, the best model reaches 61.3% accuracy, compared with 91.0% on a stratified human subset, with interaction contingency reasoning emerging as the most consistent weakness. Controlled evidence interventions reveal distinct dependencies across layers. Removing video hurts observable grounding most, while disrupting interaction order hurts contingency reasoning most. Additional controls show that this distinction survives free-response evaluation and cannot be explained by question-option shortcuts or video–transcript misalignment. By separating visual grounding from order-sensitive interaction reasoning, VIRA-Bench exposes diagnostic differences that aggregate video-QA scores obscure.

Online video content moderation is critical for protecting minors. Violations are increasingly implicit and context-dependent: a swimwear is innocuous in a vlog but high-risk with suggestive posing. Yet, traditional binary systems fundamentally fail to capture this continuous severity spectrum, directly leading to both excessive false positives for context-heavy samples and missing deeply implicit violations. We introduce KUROKO, a content moderation system that acts as a behind-the-scenes assistant, driving a paradigm shift in content moderation from binary classification to severity-aware ranking. Our contributions are threefold: (1) Human-Centric Severity Ranking: KUROKO shifts from rigid binary divides to a continuous risk spectrum, producing nuanced, expert-level rationales closely aligning with human judgment. (2) Verifiable Alignment and Interpretability: By integrating a severity-aware data engine and a verifiable reinforcement learning approach, we replace black-box decisions with transparent, actionable diagnostics. (3) Cost-Efficient Deployment: We engineer a coarse-to-fine inference architecture equipped with a lightweight recall classifier, enabling compact models to rival proprietary VLMs at a fraction of the computational cost. Extensive offline and online A/B tests demonstrate KUROKO’s advantages over traditional binary baselines and its competitiveness with leading commercial APIs at a fraction of their scale. Online, KUROKO yields more human-confirmed enforcement actions under an equal human-review budget, while lowering violative exposure and user negative feedback.

Object hallucinations in Large Vision-Language Models (LVLMs) significantly impede their real-world applicability. As the primary component for accurately interpreting visual information, the choice of visual encoder is pivotal. We hypothesize that the diverse training paradigms employed by different visual encoders instill them with distinct inductive biases, which leads to their diverse hallucination performances. Existing benchmarks typically focus on coarse-grained hallucination detection and fail to capture the diverse hallucinations elaborated in our hypothesis. To systematically analyze these effects, we introduce VHBench-10, a comprehensive benchmark for evaluating LVLMs across ten fine-grained hallucination categories. Our evaluations confirm encoders exhibit unique hallucination characteristics. Building on these insights and the suboptimality of simple feature fusion, we propose VisionWeaver, a novel Context-Aware Routing Network. It employs global visual features to generate routing signals, dynamically aggregating visual features from multiple specialized experts. Comprehensive experiments confirm the effectiveness of VisionWeaver in significantly reducing hallucinations and improving overall model performance. Our code and benchmark are available at https://github.com/whwangovo/VisionWeaver.