{"total":43,"rows":[{"id":"2503.01743","title":"Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs","created":"2025-03-03","cited_by_count":521,"area":"mllm"},{"id":"2405.17247","title":"An Introduction to Vision-Language Modeling","created":"2024-05-27","cited_by_count":178,"area":"mllm"},{"id":"2403.09792","title":"Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models","created":"2024-03-14","cited_by_count":136,"area":"mllm"},{"id":"2403.09513","title":"AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting","created":"2024-03-14","cited_by_count":125,"area":"mllm"},{"id":"2406.12030","title":"SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language Model","created":"2024-06-17","cited_by_count":85,"area":"mllm"},{"id":"2407.07403","title":"A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends","created":"2024-07-10","cited_by_count":84,"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":"2402.00357","title":"Safety of Multimodal Large Language Models on Images and Texts","created":"2024-02-01","cited_by_count":75,"area":"mllm"},{"id":"2502.14881","title":"A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations","created":"2025-02-14","cited_by_count":67,"area":"mllm"},{"id":"2410.06625","title":"ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time","created":"2024-10-09","cited_by_count":60,"area":"mllm"},{"id":"2406.07057","title":"MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models","created":"2024-06-11","cited_by_count":53,"area":"mllm"},{"id":"2411.16721","title":"Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks","created":"2024-11-23","cited_by_count":48,"area":"mllm"},{"id":"2409.18142","title":"A Survey on Multimodal Benchmarks: In the Era of Large AI Models","created":"2024-09-21","cited_by_count":27,"area":"mllm"},{"id":"2410.09047","title":"Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models","created":"2024-10-11","cited_by_count":23,"area":"mllm"},{"id":"2404.05264","title":"Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security","created":"2024-04-08","cited_by_count":19,"area":"mllm"},{"id":"2505.11454","title":"HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation","created":"2025-05-16","cited_by_count":19,"area":"mllm"},{"id":"2410.00296","title":"VLMGuard: Bootstrapping Malicious Prompt Detectors from Unlabeled Vision-Language Prompts in the Wild","created":"2024-10-01","cited_by_count":18,"area":"mllm"},{"id":"2503.11832","title":"Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning","created":"2025-03-14","cited_by_count":16,"area":"mllm"},{"id":"2407.08583","title":"The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective","created":"2024-07-11","cited_by_count":15,"area":"mllm"},{"id":"2508.12815","title":"Learning to Steer: Input-dependent Steering for Multimodal LLMs","created":"2025-08-18","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":"2505.23793","title":"USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models","created":"2025-05-26","cited_by_count":10,"area":"mllm"},{"id":"2412.04903","title":"EACO: Enhancing Alignment in Multimodal LLMs via Critical Observation","created":"2024-12-06","cited_by_count":8,"area":"mllm"},{"id":"2504.01444","title":"PiCo: Jailbreaking Multimodal Large Language Models via Pictorial Code Contextualization","created":"2025-04-02","cited_by_count":8,"area":"mllm"},{"id":"2506.09353","title":"DAVSP: Safety Alignment for Large Vision-Language Models via Deep Aligned Visual Safety Prompt","created":"2025-06-11","cited_by_count":8,"area":"mllm"},{"id":"2412.12722","title":"Defending LVLMs Against Vision Attacks through Partial-Perception Supervision","created":"2024-12-17","cited_by_count":7,"area":"mllm"},{"id":"2501.02029","title":"Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large Vision-Language Models","created":"2025-01-03","cited_by_count":7,"area":"mllm"},{"id":"2504.18053","title":"DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models","created":"2025-04-25","cited_by_count":6,"area":"mllm"},{"id":"2502.04757","title":"ELITE: Enhanced Language-Image Toxicity Evaluation for Safety","created":"2025-02-07","cited_by_count":5,"area":"mllm"},{"id":"2510.02677","title":"ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks","created":"2025-10-03","cited_by_count":5,"area":"mllm"},{"id":"2504.03735","title":"Misaligned Roles, Misplaced Images: Structural Input Perturbations Expose Multimodal Alignment Blind Spots","created":"2025-04-01","cited_by_count":4,"area":"mllm"},{"id":"2508.15370","title":"Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation","created":"2025-08-21","cited_by_count":4,"area":"mllm"},{"id":"2505.24208","title":"Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap","created":"2025-05-30","cited_by_count":3,"area":"mllm"},{"id":"2603.22882","title":"TreeTeaming: Autonomous Red-Teaming of Vision-Language Models via Hierarchical Strategy Exploration","created":"2026-03-24","cited_by_count":3,"area":"mllm"},{"id":"2501.09039","title":"Playing Devil's Advocate: Unmasking Toxicity and Vulnerabilities in Large Vision-Language Models","created":"2025-01-14","cited_by_count":2,"area":"mllm"},{"id":"2505.23065","title":"SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services","created":"2025-05-29","cited_by_count":2,"area":"mllm"},{"id":"2507.11968","title":"Watch, Listen, Understand, Mislead: Tri-modal Adversarial Attacks on Short Videos for Content Appropriateness Evaluation","created":"2025-07-16","cited_by_count":2,"area":"mllm"},{"id":"2603.22094","title":"Principled Steering via Null-space Projection for Jailbreak Defense in Vision-Language Models","created":"2026-03-23","cited_by_count":2,"area":"mllm"},{"id":"2603.27240","title":"Diagnosing and Repairing Unsafe Channels in Vision-Language Models via Causal Discovery and Dual-Modal Safety Subspace Projection","created":"2026-03-28","cited_by_count":2,"area":"mllm"},{"id":"2503.11742","title":"Safe Vision-Language Models via Unsafe Weights Manipulation","created":"2025-03-14","cited_by_count":1,"area":"mllm"},{"id":"2506.12336","title":"Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding","created":"2025-06-14","cited_by_count":1,"area":"mllm"},{"id":"2602.07931","title":"Which private attributes do VLMs agree on and predict well?","created":"2026-02-08","cited_by_count":0,"area":"mllm"},{"id":"2609.31746","title":"VisionPsy-Nano: Improving Accuracy, Efficiency, and Reliability in On-Device Vision-Language Models","created":"2026-09-23","cited_by_count":0,"area":"mllm"}],"next_cursor":null}