{"total":51,"rows":[{"id":"2411.11919","title":"VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation","created":"2024-11-18","cited_by_count":73,"area":"mllm"},{"id":"2501.09425","title":"Vision-Language Models Do Not Understand Negation","created":"2025-01-16","cited_by_count":72,"area":"mllm"},{"id":"2502.13059","title":"SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models","created":"2025-02-18","cited_by_count":66,"area":"mllm"},{"id":"2501.02189","title":"A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges","created":"2025-01-04","cited_by_count":47,"area":"mllm"},{"id":"2412.13180","title":"Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration","created":"2024-12-17","cited_by_count":41,"area":"mllm"},{"id":"2412.03324","title":"A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs","created":"2024-12-04","cited_by_count":35,"area":"mllm"},{"id":"2501.00654","title":"ICONS: Influence Consensus for Vision-Language Data Selection","created":"2024-12-31","cited_by_count":29,"area":"mllm"},{"id":"2502.13928","title":"Symmetrical Visual Contrastive Optimization: Aligning Vision-Language Models with Minimal Contrastive Images","created":"2025-02-19","cited_by_count":14,"area":"mllm"},{"id":"2506.18322","title":"Escaping the SpuriVerse: Can Large Vision-Language Models Generalize Beyond Seen Spurious Correlations?","created":"2025-06-23","cited_by_count":14,"area":"mllm"},{"id":"2603.06148","title":"VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models","created":"2026-03-06","cited_by_count":14,"area":"mllm"},{"id":"2506.05551","title":"When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding","created":"2025-06-05","cited_by_count":12,"area":"mllm"},{"id":"2412.00142","title":"Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features","created":"2024-11-28","cited_by_count":11,"area":"mllm"},{"id":"2510.16926","title":"Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input","created":"2025-10-19","cited_by_count":9,"area":"mllm"},{"id":"2505.17726","title":"Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM","created":"2025-05-23","cited_by_count":7,"area":"mllm"},{"id":"2510.10880","title":"Where on Earth? A Vision-Language Benchmark for Probing Model Geolocation Skills Across Scales","created":"2025-10-13","cited_by_count":5,"area":"mllm"},{"id":"2510.12548","title":"VISaGE: Understanding Visual Generics and Exceptions","created":"2025-10-14","cited_by_count":4,"area":"mllm"},{"id":"2603.17662","title":"FINER: MLLMs Hallucinate under Fine-grained Negative Queries","created":"2026-03-18","cited_by_count":4,"area":"mllm"},{"id":"2507.07106","title":"Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor","created":"2025-07-09","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":"2512.04238","title":"6 Fingers, 1 Kidney: Natural Adversarial Medical Images Reveal Critical Weaknesses of Vision-Language Models","created":"2025-12-03","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":"2411.17794","title":"NEMO: Can Multimodal LLMs Identify Attribute-Modified Objects?","created":"2024-11-26","cited_by_count":2,"area":"mllm"},{"id":"2503.04858","title":"SHAPE : Self-Improved Visual Preference Alignment by Iteratively Generating Holistic Winner","created":"2025-03-06","cited_by_count":2,"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":"2505.14405","title":"Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency","created":"2025-05-20","cited_by_count":2,"area":"mllm"},{"id":"2509.19070","title":"ColorBlindnessEval: Can Vision-Language Models Pass Color Blindness Tests?","created":"2025-09-23","cited_by_count":2,"area":"mllm"},{"id":"2603.23455","title":"DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection","created":"2026-03-24","cited_by_count":2,"area":"mllm"},{"id":"2604.12616","title":"Every Picture Tells a Dangerous Story: Memory-Augmented Multi-Agent Jailbreak Attacks on VLMs","created":"2026-04-14","cited_by_count":2,"area":"mllm"},{"id":"2608.07088","title":"RoRA: Role-Oriented Regional Allocation for Visual Token Pruning in MLLMs","created":"2026-08-07","cited_by_count":2,"area":"mllm"},{"id":"2509.14735","title":"Decoupled Proxy Alignment: Mitigating Language Prior Conflict for Multimodal Alignment in MLLM","created":"2025-09-18","cited_by_count":1,"area":"mllm"},{"id":"2511.03774","title":"Contamination Detection for VLMs using Multi-Modal Semantic Perturbation","created":"2025-11-05","cited_by_count":1,"area":"mllm"},{"id":"2602.11733","title":"Adapting Vision-Language Models for E-commerce Understanding at Scale","created":"2026-02-12","cited_by_count":1,"area":"mllm"},{"id":"2603.24181","title":"Unlocking Few-Shot Capabilities in LVLMs via Prompt Conditioning and Head Selection","created":"2026-03-25","cited_by_count":1,"area":"mllm"},{"id":"2605.22168","title":"Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability","created":"2026-05-21","cited_by_count":1,"area":"mllm"},{"id":"2605.29615","title":"DiffSpot: Can VLMs Spot Fine-Grained Visual Differences in Web Interfaces?","created":"2026-05-28","cited_by_count":1,"area":"mllm"},{"id":"2607.05264","title":"SteelBench: Evaluating Vision-Language Models in Real-World Industrial Environments","created":"2026-07-06","cited_by_count":1,"area":"mllm"},{"id":"2608.04244","title":"SIGNPOST-Bench: Benchmarking Text-Vision Conflict Resolution in Multimodal Large Language Models","created":"2026-08-04","cited_by_count":1,"area":"mllm"},{"id":"2609.11310","title":"Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models","created":"2026-09-10","cited_by_count":1,"area":"mllm"},{"id":"2609.22588","title":"Seeing is not Enough: Vision-Language Models Perceive Evidence but Fail to Act","created":"2026-09-18","cited_by_count":1,"area":"mllm"},{"id":"2510.21794","title":"Token-Level Inference-Time Alignment for Vision-Language Models","created":"2025-10-20","cited_by_count":0,"area":"mllm"},{"id":"2601.12430","title":"System-Mediated Attention Imbalances Make Vision-Language Models Say Yes","created":"2026-01-18","cited_by_count":0,"area":"mllm"},{"id":"2602.18729","title":"MiSCHiEF: A Benchmark in Minimal-Pairs of Safety and Culture for Holistic Evaluation of Fine-Grained Image-Caption Alignment","created":"2026-02-21","cited_by_count":0,"area":"mllm"},{"id":"2603.06600","title":"FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures","created":"2026-02-17","cited_by_count":0,"area":"mllm"},{"id":"2603.11689","title":"Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks","created":"2026-03-12","cited_by_count":0,"area":"mllm"},{"id":"2607.02853","title":"Prior Bias in Vision Language Models on UML Diagram Interpretation","created":"2026-07-03","cited_by_count":0,"area":"mllm"},{"id":"2607.15565","title":"Ask Twice, Look Twice: Prompt Echoing Resolves the Question-First Paradox in Vision-Language Models","created":"2026-07-17","cited_by_count":0,"area":"mllm"},{"id":"2608.09772","title":"PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Models","created":"2026-08-10","cited_by_count":0,"area":"mllm"},{"id":"2608.30653","title":"Fine-Grained Multi Image Object Hallucination Benchmark","created":"2026-08-31","cited_by_count":0,"area":"mllm"},{"id":"2609.13296","title":"Harnessing Image Question Dependence for Better VLM Test-time Reinforcement Learning","created":"2026-09-09","cited_by_count":0,"area":"mllm"},{"id":"2609.32185","title":"Contamination, Prior, or Evidence? Decomposing and Training Evidence Use in Whole-Slide Vision-Language Models","created":"2026-09-26","cited_by_count":0,"area":"mllm"},{"id":"2610.13548","title":"Auditing VLM Benchmarks from Existing Evaluation Runs","created":"2026-10-09","cited_by_count":0,"area":"mllm"}],"next_cursor":null}