{"total":29,"rows":[{"id":"2509.23661","title":"LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training","created":"2025-09-28","cited_by_count":244,"area":"mllm"},{"id":"2502.10391","title":"MM-RLHF: The Next Step Forward in Multimodal LLM Alignment","created":"2025-02-14","cited_by_count":83,"area":"mllm"},{"id":"2505.02835","title":"R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning","created":"2025-05-05","cited_by_count":65,"area":"mllm"},{"id":"2502.14191","title":"Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision Language Models","created":"2025-02-20","cited_by_count":44,"area":"mllm"},{"id":"2509.00676","title":"LLaVA-Critic-R1: Your Critic Model is Secretly a Strong Policy Model","created":"2025-08-31","cited_by_count":35,"area":"mllm"},{"id":"2509.16127","title":"BaseReward: A Strong Baseline for Multimodal Reward Model","created":"2025-09-19","cited_by_count":15,"area":"mllm"},{"id":"2511.21662","title":"Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following","created":"2025-11-26","cited_by_count":15,"area":"mllm"},{"id":"2503.14504","title":"Aligning Multimodal LLM with Human Preference: A Survey","created":"2025-03-18","cited_by_count":13,"area":"mllm"},{"id":"2503.06260","title":"From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models","created":"2025-03-08","cited_by_count":11,"area":"mllm"},{"id":"2602.11124","title":"PhyCritic: Multimodal Critic Models for Physical AI","created":"2026-02-11","cited_by_count":10,"area":"mllm"},{"id":"2604.25235","title":"VLM Judges Can Rank but Cannot Score: Task-Dependent Uncertainty in Multimodal Evaluation","created":"2026-04-28","cited_by_count":8,"area":"mllm"},{"id":"2512.05145","title":"Self-Improving VLM Judges Without Human Annotations","created":"2025-12-02","cited_by_count":7,"area":"mllm"},{"id":"2602.00846","title":"Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis","created":"2026-01-31","cited_by_count":7,"area":"mllm"},{"id":"2509.00484","title":"VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding","created":"2025-08-30","cited_by_count":6,"area":"mllm"},{"id":"2505.23693","title":"VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC Videos","created":"2025-05-29","cited_by_count":4,"area":"mllm"},{"id":"2603.16600","title":"Rationale Matters: Learning Transferable Rubrics via Proxy-Guided Critique for VLM Reward Models","created":"2026-03-17","cited_by_count":4,"area":"mllm"},{"id":"2604.18164","title":"MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge","created":"2026-04-20","cited_by_count":4,"area":"mllm"},{"id":"2502.13487","title":"Transferring Textual Preferences to Vision-Language Understanding through Model Merging","created":"2025-02-19","cited_by_count":3,"area":"mllm"},{"id":"2510.16333","title":"RL makes MLLMs see better than SFT","created":"2025-10-18","cited_by_count":3,"area":"mllm"},{"id":"2603.00546","title":"Advancing Multimodal Judge Models through a Capability-Oriented Benchmark and MCTS-Driven Data Generation","created":"2026-02-28","cited_by_count":3,"area":"mllm"},{"id":"2604.17768","title":"When Vision-Language Models Judge Without Seeing: Exposing Informativeness Bias","created":"2026-04-20","cited_by_count":3,"area":"mllm"},{"id":"2605.09269","title":"DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification","created":"2026-05-10","cited_by_count":3,"area":"mllm"},{"id":"2503.09348","title":"MOAT: Evaluating LMMs for Capability Integration and Instruction Grounding","created":"2025-03-12","cited_by_count":2,"area":"mllm"},{"id":"2511.09067","title":"MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique","created":"2025-11-12","cited_by_count":2,"area":"mllm"},{"id":"2502.15027","title":"InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback","created":"2025-02-20","cited_by_count":1,"area":"mllm"},{"id":"2510.00766","title":"Are Large Vision-Language Models Ready to Guide Blind and Low-Vision Individuals?","created":"2025-10-01","cited_by_count":1,"area":"mllm"},{"id":"2510.21849","title":"TowerVision: Understanding and Improving Multilinguality in Vision-Language Models","created":"2025-10-22","cited_by_count":1,"area":"mllm"},{"id":"2608.07920","title":"Forged Peer Judgments Mislead Multimodal LLM Judge Panels: Source-Blind Anchoring and Panel-Consensus Verification","created":"2026-08-08","cited_by_count":0,"area":"mllm"},{"id":"2609.05401","title":"Same Trajectory, Contradictory Rewards (ROBORMBENCH): Paraphrase Fragility in Vision Language Reward Models","created":"2026-09-04","cited_by_count":0,"area":"mllm"}],"next_cursor":null}