{"total":26,"rows":[{"id":"2506.18504","title":"Generalizing vision-language models to novel domains: A comprehensive survey","created":"2025-06-23","cited_by_count":7,"area":"mllm"},{"id":"2508.01225","title":"Multi-Cache Enhanced Prototype Learning for Test-Time Generalization of Vision-Language Models","created":"2025-08-02","cited_by_count":6,"area":"mllm"},{"id":"2506.12706","title":"NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models","created":"2025-06-15","cited_by_count":3,"area":"mllm"},{"id":"2510.09473","title":"D-TPT: Dimensional Entropy Maximization for Calibrating Test-Time Prompt Tuning in Vision-Language Models","created":"2025-10-10","cited_by_count":3,"area":"mllm"},{"id":"2510.22785","title":"Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models","created":"2025-10-26","cited_by_count":3,"area":"mllm"},{"id":"2606.14299","title":"What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective","created":"2026-06-12","cited_by_count":3,"area":"mllm"},{"id":"2512.16523","title":"TTP: Test-Time Padding for Adversarial Detection and Robust Adaptation on Vision-Language Models","created":"2025-12-18","cited_by_count":2,"area":"mllm"},{"id":"2601.19210","title":"Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP","created":"2026-01-27","cited_by_count":2,"area":"mllm"},{"id":"2512.21815","title":"High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models","created":"2025-12-26","cited_by_count":1,"area":"mllm"},{"id":"2601.12443","title":"Adversarial Defense in Vision-Language Models: An Overview","created":"2026-01-18","cited_by_count":1,"area":"mllm"},{"id":"2602.07027","title":"Fair Context Learning for Evidence-Balanced Test-Time Adaptation in Vision-Language Models","created":"2026-02-02","cited_by_count":1,"area":"mllm"},{"id":"2603.26984","title":"A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models","created":"2026-03-27","cited_by_count":1,"area":"mllm"},{"id":"2603.29410","title":"AGFT: Alignment-Guided Fine-Tuning for Zero-Shot Adversarial Robustness of Vision-Language Models","created":"2026-03-31","cited_by_count":1,"area":"mllm"},{"id":"2604.11576","title":"Finetune Like You Pretrain: Boosting Zero-shot Adversarial Robustness in Vision-language Models","created":"2026-04-13","cited_by_count":1,"area":"mllm"},{"id":"2606.06938","title":"When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness","created":"2026-06-05","cited_by_count":1,"area":"mllm"},{"id":"2603.12799","title":"What Makes VLMs Robust? Towards Reconciling Robustness and Accuracy in Vision-Language Models","created":"2026-03-13","cited_by_count":0,"area":"mllm"},{"id":"2603.28568","title":"XSPA: Crafting Imperceptible X-Shaped Sparse Adversarial Perturbations for Transferable Attacks on VLMs","created":"2026-03-30","cited_by_count":0,"area":"mllm"},{"id":"2604.01010","title":"PDA: Text-Augmented Defense Framework for Robust Vision-Language Models against Adversarial Image Attacks","created":"2026-04-01","cited_by_count":0,"area":"mllm"},{"id":"2605.15584","title":"AGC: Adaptive Geodesic Correction for Adversarial Robustness on Vision-Language Models","created":"2026-05-15","cited_by_count":0,"area":"mllm"},{"id":"2605.17577","title":"TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models","created":"2026-05-17","cited_by_count":0,"area":"mllm"},{"id":"2605.25922","title":"Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models","created":"2026-05-25","cited_by_count":0,"area":"mllm"},{"id":"2606.03713","title":"Investigating Adversarial Robustness of Multi-modal Large Language Models","created":"2026-06-02","cited_by_count":0,"area":"mllm"},{"id":"2606.06943","title":"SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models","created":"2026-06-05","cited_by_count":0,"area":"mllm"},{"id":"2606.23132","title":"T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models","created":"2026-06-22","cited_by_count":0,"area":"mllm"},{"id":"2608.17306","title":"Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models","created":"2026-08-18","cited_by_count":0,"area":"mllm"},{"id":"2609.35002","title":"Still There, No Longer Seen: Exposing Compression-Induced Risk in Large Vision-Language Models","created":"2026-09-28","cited_by_count":0,"area":"mllm"}],"next_cursor":null}