{"total":34,"rows":[{"id":"2504.05299","title":"SmolVLM: Redefining small and efficient multimodal models","created":"2025-04-07","cited_by_count":237,"area":"mllm"},{"id":"2501.12386","title":"InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling","created":"2025-01-21","cited_by_count":211,"area":"mllm"},{"id":"2509.24871","title":"StreamForest: Efficient Online Video Understanding with Persistent Event Memory","created":"2025-09-29","cited_by_count":78,"area":"mllm"},{"id":"2503.18478","title":"Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding","created":"2025-03-24","cited_by_count":49,"area":"mllm"},{"id":"2605.25979","title":"LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence","created":"2026-05-25","cited_by_count":39,"area":"mllm"},{"id":"2505.14682","title":"UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation","created":"2025-05-20","cited_by_count":32,"area":"mllm"},{"id":"2506.03340","title":"Seeing the Arrow of Time in Large Multimodal Models","created":"2025-06-03","cited_by_count":30,"area":"mllm"},{"id":"2501.13919","title":"Temporal Preference Optimization for Long-Form Video Understanding","created":"2025-01-23","cited_by_count":29,"area":"mllm"},{"id":"2506.21862","title":"LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs","created":"2025-06-27","cited_by_count":26,"area":"mllm"},{"id":"2506.05332","title":"Unleashing Hour-Scale Video Training for Long Video-Language Understanding","created":"2025-06-05","cited_by_count":23,"area":"mllm"},{"id":"2512.04540","title":"VideoMem: Enhancing Ultra-Long Video Understanding via Adaptive Memory Management","created":"2025-12-04","cited_by_count":19,"area":"mllm"},{"id":"2510.23095","title":"Revisiting Multimodal Positional Encoding in Vision-Language Models","created":"2025-10-27","cited_by_count":16,"area":"mllm"},{"id":"2512.04000","title":"Divide, then Ground: Adapting Frame Selection to Query Types for Long-Form Video Understanding","created":"2025-12-03","cited_by_count":11,"area":"mllm"},{"id":"2505.24158","title":"Threading Keyframe with Narratives: MLLMs as Strong Long Video Comprehenders","created":"2025-05-30","cited_by_count":10,"area":"mllm"},{"id":"2603.12254","title":"Attend Before Attention: Efficient and Scalable Video Understanding via Autoregressive Gazing","created":"2026-03-12","cited_by_count":9,"area":"mllm"},{"id":"2603.29252","title":"Scaling the Long Video Understanding of Multimodal Large Language Models via Visual Memory Mechanism","created":"2026-03-31","cited_by_count":9,"area":"mllm"},{"id":"2504.15270","title":"An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes","created":"2025-04-21","cited_by_count":7,"area":"mllm"},{"id":"2504.18152","title":"ActionArt: Advancing Multimodal Large Models for Fine-Grained Human-Centric Video Understanding","created":"2025-04-25","cited_by_count":7,"area":"mllm"},{"id":"2503.20472","title":"From Trial to Triumph: Advancing Long Video Understanding via Visual Context Sample Scaling and Self-reward Alignment","created":"2025-03-26","cited_by_count":6,"area":"mllm"},{"id":"2504.14391","title":"How Well Can General Vision-Language Models Learn Medicine By Watching Public Educational Videos?","created":"2025-04-19","cited_by_count":6,"area":"mllm"},{"id":"2501.05711","title":"From My View to Yours: Learning Egocentric Cues from Exocentric Video using Privileged Egocentric Supervision","created":"2025-01-10","cited_by_count":4,"area":"mllm"},{"id":"2511.16595","title":"TimeViper: A Hybrid Mamba-Transformer Vision-Language Model for Efficient Long Video Understanding","created":"2025-11-20","cited_by_count":4,"area":"mllm"},{"id":"2511.17793","title":"Attention Guided Alignment in Efficient Vision-Language Models","created":"2025-11-21","cited_by_count":3,"area":"mllm"},{"id":"2602.22716","title":"SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs","created":"2026-02-26","cited_by_count":2,"area":"mllm"},{"id":"2509.02807","title":"PixFoundation 2.0: Do Video Multi-Modal LLMs Use Motion in Visual Grounding?","created":"2025-09-02","cited_by_count":1,"area":"mllm"},{"id":"2605.02130","title":"From Where Things Are to What They Are For: Benchmarking Spatial-Functional Intelligence in Multimodal LLMs","created":"2026-05-04","cited_by_count":1,"area":"mllm"},{"id":"2605.31598","title":"Linear Scaling Video VLMs for Long Video Understanding","created":"2026-05-29","cited_by_count":1,"area":"mllm"},{"id":"2502.00241","title":"Mordal: Automated Pretrained Model Selection for Vision Language Models","created":"2025-02-01","cited_by_count":0,"area":"mllm"},{"id":"2503.19850","title":"FALCONEye: Finding Answers and Localizing Content in ONE-hour-long videos with multi-modal LLMs","created":"2025-03-25","cited_by_count":0,"area":"mllm"},{"id":"2605.08974","title":"Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models","created":"2026-05-09","cited_by_count":0,"area":"mllm"},{"id":"2605.21625","title":"Flat-Pack Bench: Evaluating Spatio-Temporal Understanding in Large Vision-Language Models through Furniture Assembly","created":"2026-05-20","cited_by_count":0,"area":"mllm"},{"id":"2605.22078","title":"Enhancing Visual Token Representations for Video Large Language Models via Training-Free Spatial-Temporal Pooling and Gridding","created":"2026-05-21","cited_by_count":0,"area":"mllm"},{"id":"2609.29607","title":"STRAND: Benchmarking and Improving Object-Centric Spatio-Temporal Monitoring in Video Large Language Models","created":"2026-08-28","cited_by_count":0,"area":"mllm"},{"id":"2610.16118","title":"Elephant in the Fridge: Constant-Memory Frame Packing for Long Video Understanding","created":"2026-10-12","cited_by_count":0,"area":"mllm"}],"next_cursor":null}