{"total":15,"rows":[{"id":"2505.18434","title":"TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP","created":"2025-05-24","cited_by_count":7,"area":"mllm"},{"id":"2508.11317","title":"Logic Unseen: Revealing the Logical Blindspots of Vision-Language Models","created":"2025-08-15","cited_by_count":7,"area":"mllm"},{"id":"2602.21035","title":"Not Just What's There: Enabling CLIP to Comprehend Negated Visual Descriptions Without Fine-tuning","created":"2026-02-24","cited_by_count":5,"area":"mllm"},{"id":"2507.19064","title":"Negation-Aware Test-Time Adaptation for Vision-Language Models","created":"2025-07-25","cited_by_count":4,"area":"mllm"},{"id":"2511.12331","title":"SpaceVLM: Sub-Space Modeling of Negation in Vision-Language Models","created":"2025-11-15","cited_by_count":3,"area":"mllm"},{"id":"2603.20554","title":"When Negation Is a Geometry Problem in Vision-Language Models","created":"2026-03-20","cited_by_count":2,"area":"mllm"},{"id":"2606.20177","title":"Evaluating and Enhancing Negation Comprehension in Remote Sensing MLLMs","created":"2026-06-18","cited_by_count":2,"area":"mllm"},{"id":"2607.23052","title":"Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models","created":"2026-07-25","cited_by_count":2,"area":"mllm"},{"id":"2511.23170","title":"PowerCLIP: Powerset Alignment for Contrastive Pre-Training","created":"2025-11-28","cited_by_count":1,"area":"mllm"},{"id":"2603.29258","title":"Omni-NegCLIP: Enhancing CLIP with Front-Layer Contrastive Fine-Tuning for Comprehensive Negation Understanding","created":"2026-03-31","cited_by_count":1,"area":"mllm"},{"id":"2606.15821","title":"The Truth Stays in the Family: Enhancing Contextual Grounding via Inherited Truthful Heads in Model Lineages","created":"2026-06-14","cited_by_count":1,"area":"mllm"},{"id":"2607.23271","title":"What CLIP Knows but Cannot Say: Recovering Negation from Frozen Intermediate Features","created":"2026-07-25","cited_by_count":1,"area":"mllm"},{"id":"2602.12498","title":"NAST: Improving Negation Handling in Medical Vision-Language Models through Negation-Aware Selective Training","created":"2026-02-13","cited_by_count":0,"area":"mllm"},{"id":"2604.13313","title":"Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding","created":"2026-04-14","cited_by_count":0,"area":"mllm"},{"id":"2610.17440","title":"Know-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models","created":"2026-10-12","cited_by_count":0,"area":"mllm"}],"next_cursor":null}