{"total":71,"rows":[{"id":"2503.21776","title":"Video-R1: Reinforcing Video Reasoning in MLLMs","created":"2025-03-27","cited_by_count":484,"area":"lr"},{"id":"2403.16999","title":"Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning","created":"2024-03-25","cited_by_count":280,"area":"lr"},{"id":"2406.19389","title":"OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding","created":"2024-06-27","cited_by_count":127,"area":"lr"},{"id":"2407.11325","title":"VISA: Reasoning Video Object Segmentation via Large Language Models","created":"2024-07-16","cited_by_count":127,"area":"lr"},{"id":"2409.19603","title":"One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos","created":"2024-09-29","cited_by_count":100,"area":"lr"},{"id":"2403.18406","title":"An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering Using a VLM","created":"2024-03-27","cited_by_count":97,"area":"lr"},{"id":"2505.12434","title":"VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-Tuning","created":"2025-05-18","cited_by_count":93,"area":"lr"},{"id":"2508.04416","title":"Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning","created":"2025-08-06","cited_by_count":91,"area":"lr"},{"id":"2411.14794","title":"VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection","created":"2024-11-22","cited_by_count":65,"area":"lr"},{"id":"2504.09641","title":"TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning","created":"2025-04-13","cited_by_count":62,"area":"lr"},{"id":"2506.07464","title":"DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO","created":"2025-06-09","cited_by_count":61,"area":"lr"},{"id":"2506.16141","title":"GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning","created":"2025-06-19","cited_by_count":47,"area":"lr"},{"id":"2503.07523","title":"VisRL: Intention-Driven Visual Perception via Reinforced Reasoning","created":"2025-03-10","cited_by_count":39,"area":"lr"},{"id":"2412.01694","title":"Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation","created":"2024-12-02","cited_by_count":37,"area":"lr"},{"id":"2506.09839","title":"OctoNav: Towards Generalist Embodied Navigation","created":"2025-06-11","cited_by_count":34,"area":"lr"},{"id":"2506.06097","title":"VideoChat-A1: Thinking with Long Videos by Chain-of-Shot Reasoning","created":"2025-06-06","cited_by_count":32,"area":"lr"},{"id":"2505.24718","title":"Reinforcing Video Reasoning with Focused Thinking","created":"2025-05-30","cited_by_count":31,"area":"lr"},{"id":"2510.14032","title":"Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understanding","created":"2025-10-15","cited_by_count":31,"area":"lr"},{"id":"2405.03690","title":"How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs","created":"2024-05-06","cited_by_count":30,"area":"lr"},{"id":"2409.18938","title":"From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding","created":"2024-09-27","cited_by_count":30,"area":"lr"},{"id":"2501.08549","title":"The Devil is in Temporal Token: High Quality Video Reasoning Segmentation","created":"2025-01-15","cited_by_count":26,"area":"lr"},{"id":"2506.01908","title":"Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency","created":"2025-06-02","cited_by_count":26,"area":"lr"},{"id":"2507.13019","title":"Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities","created":"2025-07-17","cited_by_count":25,"area":"lr"},{"id":"2412.00161","title":"STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training","created":"2024-11-29","cited_by_count":22,"area":"lr"},{"id":"2503.19622","title":"Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation","created":"2025-03-25","cited_by_count":20,"area":"lr"},{"id":"2501.04670","title":"Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs","created":"2025-01-08","cited_by_count":16,"area":"lr"},{"id":"2508.19542","title":"CVBench: Benchmarking Cross-Video Synergies for Complex Multimodal Reasoning","created":"2025-08-27","cited_by_count":16,"area":"lr"},{"id":"2409.02828","title":"ExpLLM: Towards Chain of Thought for Facial Expression Recognition","created":"2024-09-04","cited_by_count":15,"area":"lr"},{"id":"2603.01455","title":"From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents","created":"2026-03-02","cited_by_count":15,"area":"lr"},{"id":"2503.21056","title":"Online Reasoning Video Segmentation with Just-in-Time Digital Twins","created":"2025-03-27","cited_by_count":11,"area":"lr"},{"id":"2501.01426","title":"Unifying Specialized Visual Encoders for Video Language Models","created":"2025-01-02","cited_by_count":10,"area":"lr"},{"id":"2607.24904","title":"Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model","created":"2026-07-27","cited_by_count":10,"area":"lr"},{"id":"2505.18561","title":"CoT-RVS: Zero-Shot Chain-of-Thought Reasoning Segmentation for Videos","created":"2025-05-24","cited_by_count":9,"area":"lr"},{"id":"2504.04550","title":"Advancing Egocentric Video Question Answering with Multimodal Large Language Models","created":"2025-04-06","cited_by_count":8,"area":"lr"},{"id":"2508.07470","title":"AURA: A Fine-Grained Benchmark and Decomposed Metric for Audio-Visual Reasoning","created":"2025-08-10","cited_by_count":8,"area":"lr"},{"id":"2504.15415","title":"IV-Bench: A Benchmark for Image-Grounded Video Perception and Reasoning in Multimodal LLMs","created":"2025-04-21","cited_by_count":7,"area":"lr"},{"id":"2409.03206","title":"TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations","created":"2024-09-05","cited_by_count":6,"area":"lr"},{"id":"2503.09158","title":"FaVChat: Hierarchical Prompt-Query Guided Facial Video Understanding with Data-Efficient GRPO","created":"2025-03-12","cited_by_count":6,"area":"lr"},{"id":"2509.03501","title":"Strefer: Empowering Video LLMs with Space-Time Referring and Reasoning via Synthetic Instruction Data","created":"2025-09-03","cited_by_count":6,"area":"lr"},{"id":"2511.06256","title":"VLDrive: Vision-Augmented Lightweight MLLMs for Efficient Language-grounded Autonomous Driving","created":"2025-11-09","cited_by_count":6,"area":"lr"},{"id":"2505.08455","title":"VCRBench: Exploring Long-form Causal Reasoning Capabilities of Large Video Language Models","created":"2025-05-13","cited_by_count":5,"area":"lr"},{"id":"2509.11796","title":"FineQuest: Adaptive Knowledge-Assisted Sports Video Understanding via Agent-of-Thoughts Reasoning","created":"2025-09-15","cited_by_count":5,"area":"lr"},{"id":"2509.26278","title":"ProfVLM: A lightweight video-language model for multi-view proficiency estimation","created":"2025-09-30","cited_by_count":4,"area":"lr"},{"id":"2510.09274","title":"MomentSeg: Moment-Centric Sampling for Enhanced Video Pixel Understanding","created":"2025-10-10","cited_by_count":4,"area":"lr"},{"id":"2512.09354","title":"Video-QTR: Query-Driven Temporal Reasoning Framework for Lightweight Video Understanding","created":"2025-12-10","cited_by_count":4,"area":"lr"},{"id":"2603.27593","title":"STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding","created":"2026-03-29","cited_by_count":4,"area":"lr"},{"id":"2501.03675","title":"SMIR: Efficient Synthetic Data Pipeline To Improve Multi-Image Reasoning","created":"2025-01-07","cited_by_count":3,"area":"lr"},{"id":"2601.01095","title":"NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding","created":"2026-01-03","cited_by_count":3,"area":"lr"},{"id":"2506.22880","title":"Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder","created":"2025-06-28","cited_by_count":2,"area":"lr"},{"id":"2509.13161","title":"Enhancing Video Large Language Models with Structured Multi-Video Collaborative Reasoning","created":"2025-09-16","cited_by_count":2,"area":"lr"},{"id":"2510.17045","title":"Video Reasoning without Training","created":"2025-10-19","cited_by_count":2,"area":"lr"},{"id":"2512.16461","title":"SNOW: Spatio-Temporal Scene Understanding with World Knowledge for Open-World Embodied Reasoning","created":"2025-12-18","cited_by_count":2,"area":"lr"},{"id":"2605.01324","title":"Beyond Perceptual Shortcuts: Causal-Inspired Debiasing Optimization for Generalizable Video Reasoning in Lightweight MLLMs","created":"2026-05-02","cited_by_count":2,"area":"lr"},{"id":"2607.01737","title":"ReQuest: Rethinking-based Question-Aware Frame Selection for Long-Form Video QA","created":"2026-07-02","cited_by_count":2,"area":"lr"},{"id":"2607.16107","title":"Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos","created":"2026-07-17","cited_by_count":2,"area":"lr"},{"id":"2406.10923","title":"Investigating Video Reasoning Capability of Large Language Models with Tropes in Movies","created":"2024-06-16","cited_by_count":1,"area":"lr"},{"id":"2506.06928","title":"How Important are Videos for Training Video LLMs?","created":"2025-06-07","cited_by_count":1,"area":"lr"},{"id":"2602.21137","title":"UDVideoQA: A Traffic Video Question Answering Dataset for Multi-Object Spatio-Temporal Reasoning in Urban Dynamics","created":"2026-02-24","cited_by_count":1,"area":"lr"},{"id":"2603.27060","title":"VIRST: Video-Instructed Reasoning Assistant for SpatioTemporal Segmentation","created":"2026-03-28","cited_by_count":1,"area":"lr"},{"id":"2605.13228","title":"ReTool-Video: Recursive Tool-Using Video Agents with Meta-Augmented Tool Grounding","created":"2026-05-13","cited_by_count":1,"area":"lr"},{"id":"2605.26014","title":"STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models","created":"2026-05-25","cited_by_count":1,"area":"lr"},{"id":"2606.18441","title":"Reasoning as Intersection: Consensus-Frame Alignment for Visual Focus in Video-MLLMs","created":"2026-06-16","cited_by_count":1,"area":"lr"},{"id":"2506.11558","title":"DaMO: A Data-Efficient Multimodal Orchestrator for Temporal Reasoning with Video LLMs","created":"2025-06-13","cited_by_count":0,"area":"lr"},{"id":"2601.01804","title":"V-CORE: Temporally Consistent Video Understanding for Video-LLM","created":"2026-01-05","cited_by_count":0,"area":"lr"},{"id":"2603.27960","title":"Towards Efficient Large Vision-Language Models: A Comprehensive Survey on Inference Strategies","created":"2026-03-30","cited_by_count":0,"area":"lr"},{"id":"2604.02689","title":"Efficient3D: A Unified Framework for Adaptive and Debiased Token Reduction in 3D MLLMs","created":"2026-04-03","cited_by_count":0,"area":"lr"},{"id":"2607.10744","title":"Traj-VLN: Learning Pixel-Space Interaction via Autoregressive Trajectory Generation","created":"2026-07-12","cited_by_count":0,"area":"lr"},{"id":"2609.28956","title":"MoVISA: Multi-Token Reasoning for Video Object Segmentation","created":"2026-09-24","cited_by_count":0,"area":"lr"},{"id":"2609.28991","title":"Beneath the Scores: Rethinking Hallucination Evaluation for Video Understanding Models","created":"2026-09-24","cited_by_count":0,"area":"lr"},{"id":"2609.34044","title":"SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models","created":"2026-09-28","cited_by_count":0,"area":"lr"},{"id":"2610.01754","title":"Cog-VADU: A Training-Free Cognitive Reasoning Framework for Video Anomaly Detection and Understanding","created":"2026-10-01","cited_by_count":0,"area":"lr"}],"next_cursor":null}