{"total":83,"rows":[{"id":"2407.16574","title":"TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human Feedback","created":"2024-07-23","cited_by_count":43,"area":"lr"},{"id":"2503.11650","title":"Centaur: Robust End-to-End Autonomous Driving with Test-Time Training","created":"2025-03-14","cited_by_count":42,"area":"driving"},{"id":"2410.12790","title":"Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models","created":"2024-10-16","cited_by_count":38,"area":"mllm"},{"id":"2504.11195","title":"R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning","created":"2025-04-15","cited_by_count":33,"area":"mllm"},{"id":"2407.13588","title":"Robust Calibration of Large Vision-Language Adapters","created":"2024-07-18","cited_by_count":28,"area":null},{"id":"2410.20346","title":"Historical Test-time Prompt Tuning for Vision Foundation Models","created":"2024-10-27","cited_by_count":22,"area":null},{"id":"2503.09248","title":"Bayesian Test-Time Adaptation for Vision-Language Models","created":"2025-03-12","cited_by_count":22,"area":"mllm"},{"id":"2606.32026","title":"AdaJEPA: An Adaptive Latent World Model","created":"2026-06-30","cited_by_count":20,"area":"wm"},{"id":"2503.12096","title":"O-TPT: Orthogonality Constraints for Calibrating Test-time Prompt Tuning in Vision-Language Models","created":"2025-03-15","cited_by_count":15,"area":"mllm"},{"id":"2506.17307","title":"Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation","created":"2025-06-18","cited_by_count":14,"area":null},{"id":"2601.06748","title":"On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning","created":"2026-01-11","cited_by_count":14,"area":"vla"},{"id":"2412.06014","title":"Post-hoc Probabilistic Vision-Language Models","created":"2024-12-08","cited_by_count":13,"area":"mllm"},{"id":"2412.06082","title":"Are foundation models for computer vision good conformal predictors?","created":"2024-12-08","cited_by_count":13,"area":"mllm"},{"id":"2507.03657","title":"Dynamic Multimodal Prototype Learning in Vision-Language Models","created":"2025-07-04","cited_by_count":13,"area":"mllm"},{"id":"2503.12866","title":"SCAP: Transductive Test-Time Adaptation via Supportive Clique-based Attribute Prompting","created":"2025-03-17","cited_by_count":12,"area":"mllm"},{"id":"2502.14604","title":"Noisy Test-Time Adaptation in Vision-Language Models","created":"2025-02-20","cited_by_count":10,"area":"mllm"},{"id":"2506.08712","title":"ConfPO: Exploiting Policy Model Confidence for Critical Token Selection in Preference Optimization","created":"2025-06-10","cited_by_count":10,"area":"alignment"},{"id":"2510.06783","title":"TTRV: Test-Time Reinforcement Learning for Vision Language Models","created":"2025-10-08","cited_by_count":10,"area":"mllm"},{"id":"2507.07620","title":"ViLU: Learning Vision-Language Uncertainties for Failure Prediction","created":"2025-07-10","cited_by_count":9,"area":"mllm"},{"id":"2509.15178","title":"Unleashing the Potential of Multimodal LLMs for Zero-Shot Spatio-Temporal Video Grounding","created":"2025-09-18","cited_by_count":9,"area":"mllm"},{"id":"2412.18303","title":"Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model","created":"2024-12-24","cited_by_count":8,"area":"mllm"},{"id":"2506.17503","title":"Trustworthy Few-Shot Transfer of Medical VLMs through Split Conformal Prediction","created":"2025-06-20","cited_by_count":8,"area":"mllm"},{"id":"2507.06973","title":"Free on the Fly: Enhancing Flexibility in Test-Time Adaptation with Online EM","created":"2025-07-09","cited_by_count":8,"area":"mllm"},{"id":"2502.02069","title":"LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models","created":"2025-02-04","cited_by_count":7,"area":"mllm"},{"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":"2502.06019","title":"Noise is an Efficient Learner for Zero-Shot Vision-Language Models","created":"2025-02-09","cited_by_count":6,"area":"mllm"},{"id":"2503.09394","title":"Bidirectional Prototype-Reward co-Evolution for Test-Time Adaptation of Vision-Language Models","created":"2025-03-12","cited_by_count":6,"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":"2508.15568","title":"Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment","created":"2025-08-21","cited_by_count":6,"area":null},{"id":"2603.21309","title":"Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos","created":"2026-03-22","cited_by_count":5,"area":null},{"id":"2607.01111","title":"FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement","created":"2026-07-01","cited_by_count":5,"area":null},{"id":"2410.14729","title":"Is Less More? Exploring Token Condensation as Training-free Test-time Adaptation","created":"2024-10-16","cited_by_count":4,"area":null},{"id":"2507.19064","title":"Negation-Aware Test-Time Adaptation for Vision-Language Models","created":"2025-07-25","cited_by_count":4,"area":"mllm"},{"id":"2408.05769","title":"LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech Recognition","created":"2024-08-11","cited_by_count":3,"area":null},{"id":"2412.09718","title":"BayesAdapter: enhanced uncertainty estimation in CLIP few-shot adaptation","created":"2024-12-12","cited_by_count":3,"area":"mllm"},{"id":"2508.05898","title":"ETTA: Efficient Test-Time Adaptation for Vision-Language Models through Dynamic Embedding Updates","created":"2025-08-07","cited_by_count":3,"area":"mllm"},{"id":"2509.15226","title":"Calibration-Aware Prompt Learning for Medical Vision-Language Models","created":"2025-09-18","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":"2510.26441","title":"A-TPT: Angular Diversity Calibration Properties for Test-Time Prompt Tuning of Vision-Language Models","created":"2025-10-30","cited_by_count":3,"area":"mllm"},{"id":"2511.09809","title":"Test-Time Spectrum-Aware Latent Steering for Zero-Shot Generalization in Vision-Language Models","created":"2025-11-12","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":"2501.17595","title":"Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models","created":"2025-01-29","cited_by_count":2,"area":null},{"id":"2503.18334","title":"Mitigating Cache Noise in Test-Time Adaptation for Large Vision-Language Models","created":"2025-03-24","cited_by_count":2,"area":"mllm"},{"id":"2506.01247","title":"Beyond Interpretability: When, Why, and How Sparse Autoencoders Enable Label-Free Visual Steering","created":"2025-06-02","cited_by_count":2,"area":"mechinterp"},{"id":"2506.22819","title":"Prompting without Panic: Attribute-aware, Zero-shot, Test-Time Calibration","created":"2025-06-28","cited_by_count":2,"area":"mllm"},{"id":"2510.02750","title":"Bayesian Test-time Adaptation for Object Recognition and Detection with Vision-language Models","created":"2025-10-03","cited_by_count":2,"area":"mllm"},{"id":"2510.19802","title":"Class-Aware Prototype Learning with Negative Contrast for Test-Time Adaptation of Vision-Language Models","created":"2025-10-22","cited_by_count":2,"area":"mllm"},{"id":"2601.08476","title":"Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models","created":"2026-01-13","cited_by_count":2,"area":"mllm"},{"id":"2601.08617","title":"SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning","created":"2026-01-13","cited_by_count":2,"area":"mllm"},{"id":"2604.12403","title":"Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning","created":"2026-04-14","cited_by_count":2,"area":"mllm"},{"id":"2604.27715","title":"Improving Calibration in Test-Time Prompt Tuning for Vision-Language Models via Data-Free Flatness-Aware Prompt Pretraining","created":"2026-04-30","cited_by_count":2,"area":"mllm"},{"id":"2608.27004","title":"MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models","created":"2026-08-27","cited_by_count":2,"area":"mllm"},{"id":"2412.07077","title":"Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling","created":"2024-12-10","cited_by_count":1,"area":"mllm"},{"id":"2501.18864","title":"Flatness Guided Test-Time Adaptation for Vision-Language Models","created":"2025-01-31","cited_by_count":1,"area":"mllm"},{"id":"2501.19060","title":"Shift-Aware Calibration for Fine-Tuned CLIP: Leveraging Image-Text Alignment","created":"2025-01-31","cited_by_count":1,"area":"mllm"},{"id":"2505.12912","title":"Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption","created":"2025-05-19","cited_by_count":1,"area":"mllm"},{"id":"2511.15311","title":"Adapt-As-You-Walk Through the Clouds: Training-Free Online Test-Time Adaptation of 3D Vision-Language Foundation Models","created":"2025-11-19","cited_by_count":1,"area":"mllm"},{"id":"2601.23253","title":"Training-Free Test-Time Adaptation with Brownian Distance Covariance in Vision-Language Models","created":"2026-01-30","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":"2602.23653","title":"ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models","created":"2026-02-27","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":"2508.00447","title":"CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text","created":"2025-08-01","cited_by_count":0,"area":"mllm"},{"id":"2511.11690","title":"Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models","created":"2025-11-12","cited_by_count":0,"area":"mllm"},{"id":"2512.12571","title":"Measurement Plasticity: Sensor-Level Adaptation for Vision-Language Models","created":"2025-12-14","cited_by_count":0,"area":"mllm"},{"id":"2601.09661","title":"LiteEmbed: Adapting CLIP to Rare Classes","created":"2026-01-14","cited_by_count":0,"area":"mllm"},{"id":"2602.19024","title":"Towards Calibrating Prompt Tuning of Vision-Language Models","created":"2026-02-22","cited_by_count":0,"area":"mllm"},{"id":"2603.07911","title":"Beyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition","created":"2026-03-09","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":"2604.13546","title":"Learning Inference Concurrency in DynamicGate MLP Structural and Mathematical Justification","created":"2026-04-15","cited_by_count":0,"area":null},{"id":"2604.26221","title":"Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation","created":"2026-04-29","cited_by_count":0,"area":null},{"id":"2605.08156","title":"LAGO: Language-Guided Adaptive Object-Region Focus for Zero-Shot Visual-Text Alignment","created":"2026-05-04","cited_by_count":0,"area":null},{"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":"2606.06943","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":"2607.02988","title":"Cross-device Collaborative Test-time Adaptation with Zeroth-order Optimization and Model Merging","created":"2026-07-03","cited_by_count":0,"area":null},{"id":"2608.06467","title":"Test-Time Adaptation with Online Personalized Energy-Based Cache for Fine-Grained Video Expression Recognition","created":"2026-08-06","cited_by_count":0,"area":null},{"id":"2608.19890","title":"Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2608.25653","title":"Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models","created":"2026-08-26","cited_by_count":0,"area":"mllm"},{"id":"2608.30230","title":"Rethinking the Test-Time Prompt Tuning Objective from the Perspective of Calibration","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.16858","title":"TecoPrompt: Temporal-Conservative Prompt Learning for Vision-Language Models","created":"2026-09-15","cited_by_count":0,"area":"mllm"},{"id":"2609.17386","title":"Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning","created":"2026-09-15","cited_by_count":0,"area":null},{"id":"2609.32489","title":"When Helpful Text Hurts: Option-Redirecting Bias in Vision-Language Models","created":"2026-09-26","cited_by_count":0,"area":"mllm"},{"id":"2610.06147","title":"Efficient Test-time Adaptation through Candidate Verification and Divergence Shifts","created":"2026-10-05","cited_by_count":0,"area":"mllm"}],"next_cursor":null}