{"total":69,"rows":[{"id":"2504.10479","title":"InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models","created":"2025-04-14","cited_by_count":1834,"area":null},{"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":"2501.12380","title":"MMVU: Measuring Expert-Level Multi-Discipline Video Understanding","created":"2025-01-21","cited_by_count":137,"area":null},{"id":"2504.05506","title":"ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering","created":"2025-04-07","cited_by_count":108,"area":null},{"id":"2505.13259","title":"From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery","created":"2025-05-19","cited_by_count":93,"area":null},{"id":"2506.18902","title":"jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval","created":"2025-06-23","cited_by_count":76,"area":null},{"id":"2603.20633","title":"Seed1.8 Model Card: Towards Generalized Real-World Agency","created":"2026-03-21","cited_by_count":50,"area":null},{"id":"2603.27538","title":"LongCat-Next: Lexicalizing Modalities as Discrete Tokens","created":"2026-03-29","cited_by_count":36,"area":null},{"id":"2601.09668","title":"STEP3-VL-10B Technical Report","created":"2026-01-14","cited_by_count":32,"area":null},{"id":"2604.24954","title":"Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence","created":"2026-04-27","cited_by_count":30,"area":null},{"id":"2503.19903","title":"Scaling Vision Pre-Training to 4K Resolution","created":"2025-03-25","cited_by_count":28,"area":null},{"id":"2411.04075","title":"M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models","created":"2024-11-06","cited_by_count":27,"area":null},{"id":"2602.23166","title":"AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios","created":"2026-02-26","cited_by_count":23,"area":null},{"id":"2407.11229","title":"Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness","created":"2024-07-15","cited_by_count":17,"area":null},{"id":"2506.15569","title":"SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification","created":"2025-06-18","cited_by_count":17,"area":null},{"id":"2511.03929","title":"NVIDIA Nemotron Nano V2 VL","created":"2025-11-06","cited_by_count":17,"area":null},{"id":"2603.13032","title":"Multimodal OCR: Parse Anything from Documents","created":"2026-03-13","cited_by_count":14,"area":null},{"id":"2505.20538","title":"AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy","created":"2025-05-26","cited_by_count":13,"area":null},{"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":"2510.23045","title":"A Survey of AI Scientists","created":"2025-10-27","cited_by_count":11,"area":null},{"id":"2503.18435","title":"On the Perception Bottleneck of VLMs for Chart Understanding","created":"2025-03-24","cited_by_count":10,"area":null},{"id":"2507.10787","title":"Can Multimodal Foundation Models Understand Schematic Diagrams? An Empirical Study on Information-Seeking QA over Scientific Papers","created":"2025-07-14","cited_by_count":10,"area":null},{"id":"2510.02423","title":"RefineShot: Rethinking Cinematography Understanding with Foundational Skill Evaluation","created":"2025-10-02","cited_by_count":10,"area":null},{"id":"2510.16641","title":"MultiVerse: A Multi-Turn Conversation Benchmark for Evaluating Large Vision and Language Models","created":"2025-10-18","cited_by_count":10,"area":null},{"id":"2512.13168","title":"Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows","created":"2025-12-15","cited_by_count":10,"area":null},{"id":"2601.02316","title":"DatBench: Discriminative, Faithful, and Efficient VLM Evaluations","created":"2026-01-05","cited_by_count":10,"area":null},{"id":"2509.19768","title":"CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition","created":"2025-09-24","cited_by_count":9,"area":null},{"id":"2510.23538","title":"JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence","created":"2025-10-27","cited_by_count":9,"area":null},{"id":"2509.22437","title":"Chimera: Diagnosing Shortcut Learning in Visual-Language Understanding","created":"2025-09-26","cited_by_count":7,"area":null},{"id":"2601.21694","title":"ChartE$^{3}$: A Comprehensive Benchmark for End-to-End Chart Editing","created":"2026-01-29","cited_by_count":5,"area":null},{"id":"2605.12882","title":"CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence","created":"2026-05-13","cited_by_count":5,"area":null},{"id":"2602.05943","title":"Orthogonal Model Merging","created":"2026-02-05","cited_by_count":4,"area":null},{"id":"2502.18791","title":"Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs","created":"2025-02-26","cited_by_count":3,"area":null},{"id":"2507.01627","title":"Chart Question Answering from Real-World Analytical Narratives","created":"2025-07-02","cited_by_count":3,"area":null},{"id":"2507.08719","title":"Multilingual Multimodal Software Developer for Code Generation","created":"2025-07-11","cited_by_count":3,"area":null},{"id":"2510.05016","title":"Large Language Models Achieve Gold Medal Performance at the International Olympiad on Astronomy & Astrophysics (IOAA)","created":"2025-10-06","cited_by_count":3,"area":null},{"id":"2510.26781","title":"ChartAB: A Benchmark for Chart Grounding & Dense Alignment","created":"2025-10-30","cited_by_count":3,"area":null},{"id":"2511.05542","title":"ConnectomeBench: Can LLMs Proofread the Connectome?","created":"2025-10-31","cited_by_count":3,"area":null},{"id":"2606.17574","title":"DeepInsight: A Unified Evaluation Infrastructure Across the Physical AI Stack","created":"2026-06-16","cited_by_count":3,"area":null},{"id":"2510.20784","title":"A Coherence-Based Measure of AGI","created":"2025-10-23","cited_by_count":2,"area":null},{"id":"2602.04872","title":"Multi-layer Cross-attention is Provably Optimal for Multi-modal In-context Learning","created":"2026-02-04","cited_by_count":2,"area":null},{"id":"2603.12249","title":"SciMDR: Advancing Scientific Multimodal Document Reasoning","created":"2026-03-12","cited_by_count":2,"area":null},{"id":"2604.05557","title":"EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents","created":"2026-04-07","cited_by_count":2,"area":null},{"id":"2606.10640","title":"ChartLens: A Dual-Branch Framework for Chart Data Correction and Factual Summary Refinement","created":"2026-06-09","cited_by_count":2,"area":null},{"id":"2607.11192","title":"GDP.pdf: Benchmarking Grounded Multimodal Reasoning over Professional PDF Documents","created":"2026-07-13","cited_by_count":2,"area":null},{"id":"2608.20958","title":"TLive-Omni: An Omni-Modal Understanding Model for E-Commerce Live Streaming","created":"2026-08-21","cited_by_count":2,"area":null},{"id":"2503.15176","title":"A Review on Large Language Models for Visual Analytics","created":"2025-03-19","cited_by_count":1,"area":null},{"id":"2605.15691","title":"SEED: Targeted Data Selection by Weighted Independent Set","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2606.01049","title":"Beyond Captions: Context-Grounded Reconstruction for Biomedical Multimodal Continued Pretraining","created":"2026-05-31","cited_by_count":1,"area":null},{"id":"2608.13558","title":"OmniScientist: An Omni-Modal Omni-Discipline AI Scientist","created":"2026-08-13","cited_by_count":1,"area":null},{"id":"2609.35718","title":"Hard Vision, Easy Vision: What GPT-6 Astra Reveals Across Computer Vision","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2512.19173","title":"CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2602.01527","title":"Toward a Machine Bertin: Why Visualization Needs Design Principles for Machine Cognition","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2602.11674","title":"Benchmark Health Index: A Systematic Framework for Benchmarking the Benchmarks of LLMs","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2603.06677","title":"Chart Deep Research in LVLMs via Parallel Relative Policy Optimization","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2603.25293","title":"DAGverse: Building Document-Grounded Semantic DAGs from Scientific Papers","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2605.00294","title":"What Don't You Understand? Using Large Language Models to Identify and Characterize Student Misconceptions About Challenging Topics","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2605.16274","title":"ChartDesign: Towards LLM Designer of Data Visualization","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2606.17423","title":"Martingale Doppelg\\\"anger-Eval: An Identification Framework for Auditing Candlestick Understanding in Vision-Language Models","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2607.16165","title":"An Exam for Active Observers","created":"2026-07-17","cited_by_count":0,"area":null},{"id":"2607.27084","title":"SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2608.04926","title":"Consistency-Driven Co-Evolution for Self-Supervised Cross-Representation Learning","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.10677","title":"Chartography: A Benchmark for Professional Chart Understanding","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2608.27716","title":"PCFBench: A Diagnostic Benchmark for Product Carbon Footprint Estimation","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2609.24172","title":"LegendBench: A Diagnostic Benchmark for Legend Understanding with Counterfactual Interventions","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.34480","title":"When Does an Image Determine the Answer? Benchmarking Visual Answerability across Charts and Scenes","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36190","title":"FigAct: Turning Scientific Figures into Active Canvases for Explanation","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.14173","title":"Do you \"see\" what I \"see\"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.20538","title":"PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}