{"total":56,"rows":[{"id":"2403.05525","title":"DeepSeek-VL: Towards Real-World Vision-Language Understanding","created":"2024-03-08","cited_by_count":733,"area":null},{"id":"2403.02691","title":"InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents","created":"2024-03-05","cited_by_count":538,"area":"agents"},{"id":"2407.11691","title":"VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models","created":"2024-07-16","cited_by_count":455,"area":null},{"id":"2407.12772","title":"LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models","created":"2024-07-17","cited_by_count":353,"area":"mllm"},{"id":"2501.00321","title":"OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning","created":"2024-12-31","cited_by_count":173,"area":"mllm"},{"id":"2402.05935","title":"SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models","created":"2024-02-08","cited_by_count":131,"area":"mllm"},{"id":"2409.15272","title":"OmniBench: Towards The Future of Universal Omni-Language Models","created":"2024-09-23","cited_by_count":102,"area":"mllm"},{"id":"2411.17558","title":"Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey","created":"2024-11-26","cited_by_count":76,"area":"mllm"},{"id":"2411.15296","title":"MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs","created":"2024-11-22","cited_by_count":72,"area":"mllm"},{"id":"2406.12753","title":"OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI","created":"2024-06-18","cited_by_count":71,"area":"lr"},{"id":"2502.13059","title":"SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models","created":"2025-02-18","cited_by_count":66,"area":"mllm"},{"id":"2406.06007","title":"CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models","created":"2024-06-10","cited_by_count":64,"area":"mllm"},{"id":"2406.08418","title":"OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text","created":"2024-06-12","cited_by_count":59,"area":null},{"id":"2506.04207","title":"Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning","created":"2025-06-04","cited_by_count":53,"area":"lr"},{"id":"2408.15769","title":"A Survey on Evaluation of Multimodal Large Language Models","created":"2024-08-28","cited_by_count":46,"area":"lr"},{"id":"2504.21277","title":"Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models","created":"2025-04-30","cited_by_count":44,"area":"lr"},{"id":"2504.08748","title":"A Survey of Multimodal Retrieval-Augmented Generation","created":"2025-03-26","cited_by_count":37,"area":"rag"},{"id":"2502.02871","title":"Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning","created":"2025-02-05","cited_by_count":33,"area":"lr"},{"id":"2501.00654","title":"ICONS: Influence Consensus for Vision-Language Data Selection","created":"2024-12-31","cited_by_count":29,"area":"mllm"},{"id":"2502.00698","title":"MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models","created":"2025-02-02","cited_by_count":29,"area":null},{"id":"2407.03791","title":"M5 -- A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks","created":"2024-07-04","cited_by_count":27,"area":"mllm"},{"id":"2409.18142","title":"A Survey on Multimodal Benchmarks: In the Era of Large AI Models","created":"2024-09-21","cited_by_count":27,"area":"mllm"},{"id":"2412.18619","title":"Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey","created":"2024-12-16","cited_by_count":27,"area":null},{"id":"2409.01790","title":"Training on the Benchmark Is Not All You Need","created":"2024-09-03","cited_by_count":25,"area":null},{"id":"2403.07304","title":"Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models","created":"2024-03-12","cited_by_count":24,"area":"mllm"},{"id":"2506.10857","title":"VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos","created":"2025-06-12","cited_by_count":24,"area":"lr"},{"id":"2407.00468","title":"MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation","created":"2024-06-29","cited_by_count":18,"area":"lr"},{"id":"2506.03922","title":"HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models","created":"2025-06-04","cited_by_count":17,"area":"mllm"},{"id":"2407.12023","title":"CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models","created":"2024-06-28","cited_by_count":15,"area":null},{"id":"2407.17331","title":"Multi-label Cluster Discrimination for Visual Representation Learning","created":"2024-07-24","cited_by_count":14,"area":null},{"id":"2409.13730","title":"VisScience: An Extensive Benchmark for Evaluating K12 Educational Multi-modal Scientific Reasoning","created":"2024-09-10","cited_by_count":13,"area":"lr"},{"id":"2510.18915","title":"UNO-Bench: A Unified Benchmark for Exploring the Compositional Law Between Uni-modal and Omni-modal in Omni Models","created":"2025-10-21","cited_by_count":13,"area":null},{"id":"2410.06555","title":"ING-VP: MLLMs cannot Play Easy Vision-based Games Yet","created":"2024-10-09","cited_by_count":11,"area":"mllm"},{"id":"2508.08189","title":"Reinforcement Learning for Large Model: A Survey","created":"2025-08-11","cited_by_count":9,"area":"deeprl"},{"id":"2402.03173","title":"MULTI: Multimodal Understanding Leaderboard with Text and Images","created":"2024-02-05","cited_by_count":7,"area":"mllm"},{"id":"2406.13923","title":"PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents","created":"2024-06-20","cited_by_count":7,"area":"mllm"},{"id":"2409.06851","title":"LIME: Less Is More for MLLM Evaluation","created":"2024-09-10","cited_by_count":7,"area":"mllm"},{"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":"2410.10112","title":"Can We Predict Performance of Large Models across Vision-Language Tasks?","created":"2024-10-14","cited_by_count":6,"area":"mllm"},{"id":"2503.16566","title":"REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models","created":"2025-03-20","cited_by_count":5,"area":"mllm"},{"id":"2505.21389","title":"AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs","created":"2025-05-27","cited_by_count":4,"area":"mllm"},{"id":"2507.18140","title":"MathOPEval: A Fine-grained Evaluation Benchmark for Visual Operations of MLLMs in Mathematical Reasoning","created":"2025-07-24","cited_by_count":4,"area":"lr"},{"id":"2506.01085","title":"Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection","created":"2025-06-01","cited_by_count":3,"area":"mllm"},{"id":"2507.21773","title":"AgriEval: A Comprehensive Chinese Agricultural Benchmark for Large Language Models","created":"2025-07-29","cited_by_count":3,"area":null},{"id":"2507.10281","title":"Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence","created":"2025-07-14","cited_by_count":2,"area":"agents"},{"id":"2605.12154","title":"MM-OptBench: A Solver-Grounded Benchmark for Multimodal Optimization Modeling","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2604.09907","title":"From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping","created":"2026-04-10","cited_by_count":1,"area":"lr"},{"id":"2508.13680","title":"VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark","created":"2025-08-19","cited_by_count":0,"area":"lr"},{"id":"2508.21143","title":"The Percept-V Challenge: Can Multimodal LLMs Crack Simple Perception Problems?","created":"2025-08-28","cited_by_count":0,"area":"mllm"},{"id":"2510.11652","title":"ACADREASON: Exploring the Limits of Reasoning Models with Academic Research Problems","created":"2025-10-13","cited_by_count":0,"area":"lr"},{"id":"2601.10305","title":"DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset","created":"2026-01-15","cited_by_count":0,"area":"mllm"},{"id":"2601.21342","title":"Ostrakon-VL: Towards Domain-Expert MLLM for Food-Service and Retail Stores","created":"2026-01-29","cited_by_count":0,"area":"mllm"},{"id":"2606.08959","title":"ChinaHeritaQA: A Culturally-Grounded Visual Question Answering Dataset for World Heritage Sites in China","created":"2026-06-08","cited_by_count":0,"area":"lr"},{"id":"2606.21618","title":"CulMind: Benchmarking Multimodal Understanding and Reasoning in Chinese Cultural Heritage","created":"2026-06-19","cited_by_count":0,"area":"lr"},{"id":"2608.06501","title":"Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding","created":"2026-08-06","cited_by_count":0,"area":null},{"id":"2609.37515","title":"Hierarchical Compression of Vision-Language Model Benchmarks","created":"2026-09-29","cited_by_count":0,"area":"mllm"}],"next_cursor":null}