{"total":128,"rows":[{"id":"2409.12186","title":"Qwen2.5-Coder Technical Report","created":"2024-09-18","cited_by_count":1203,"area":"lr"},{"id":"2411.02059","title":"TableGPT2: A Large Multimodal Model with Tabular Data Integration","created":"2024-11-04","cited_by_count":72,"area":"mllm"},{"id":"2502.13059","title":"SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models","created":"2025-02-18","cited_by_count":66,"area":"mllm"},{"id":"2411.02937","title":"Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent","created":"2024-11-05","cited_by_count":65,"area":"mllm"},{"id":"2502.02508","title":"Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search","created":"2025-02-04","cited_by_count":53,"area":"lr"},{"id":"2505.05568","title":"Griffin: Towards a Graph-Centric Relational Database Foundation Model","created":"2025-05-08","cited_by_count":47,"area":null},{"id":"2508.00414","title":"Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training","created":"2025-08-01","cited_by_count":42,"area":"agents"},{"id":"2505.12415","title":"Table-R1: Region-based Reinforcement Learning for Table Understanding","created":"2025-05-18","cited_by_count":37,"area":"lr"},{"id":"2506.13752","title":"Steering LLM Thinking with Budget Guidance","created":"2025-06-16","cited_by_count":33,"area":"lr"},{"id":"2502.05036","title":"nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow","created":"2025-02-07","cited_by_count":23,"area":"agents"},{"id":"2412.05210","title":"Evaluating and Aligning CodeLLMs on Human Preference","created":"2024-12-06","cited_by_count":22,"area":"alignment"},{"id":"2503.14140","title":"Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding","created":"2025-03-18","cited_by_count":20,"area":"mllm"},{"id":"2505.15801","title":"VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models","created":"2025-05-21","cited_by_count":20,"area":"lr"},{"id":"2506.09003","title":"SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner","created":"2025-06-10","cited_by_count":20,"area":null},{"id":"2605.20876","title":"Terminal-World: Scaling Terminal-Agent Environments via Agent Skills","created":"2026-05-20","cited_by_count":20,"area":"agents"},{"id":"2607.09996","title":"Who&When Pro: Can LLMs Really Attribute Failures in AI Agents?","created":"2026-07-10","cited_by_count":18,"area":"agents"},{"id":"2411.19504","title":"TQA-Bench: Evaluating LLMs for Multi-Table Question Answering","created":"2024-11-29","cited_by_count":17,"area":null},{"id":"2506.03949","title":"TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering","created":"2025-06-04","cited_by_count":15,"area":null},{"id":"2506.05587","title":"MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark","created":"2025-06-05","cited_by_count":14,"area":"lr"},{"id":"2508.21741","title":"Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance","created":"2025-08-29","cited_by_count":13,"area":null},{"id":"2412.10422","title":"AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework","created":"2024-12-10","cited_by_count":12,"area":"agents"},{"id":"2506.13405","title":"RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis","created":"2025-06-16","cited_by_count":12,"area":null},{"id":"2509.25084","title":"Scaling Generalist Data-Analytic Agents","created":"2025-09-29","cited_by_count":11,"area":"agents"},{"id":"2509.25361","title":"Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling","created":"2025-09-29","cited_by_count":11,"area":"alignment"},{"id":"2510.23045","title":"A Survey of AI Scientists","created":"2025-10-27","cited_by_count":11,"area":null},{"id":"2606.29955","title":"SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows","created":"2026-06-29","cited_by_count":11,"area":"agents"},{"id":"2410.17787","title":"Large Language Models Engineer Too Many Simple Features For Tabular Data","created":"2024-10-23","cited_by_count":10,"area":null},{"id":"2412.11757","title":"SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types","created":"2024-12-16","cited_by_count":10,"area":null},{"id":"2501.14693","title":"Rethinking Table Instruction Tuning","created":"2025-01-24","cited_by_count":10,"area":"lr"},{"id":"2505.15110","title":"RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals","created":"2025-05-21","cited_by_count":10,"area":"lr"},{"id":"2506.11684","title":"MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space","created":"2025-06-13","cited_by_count":9,"area":"lr"},{"id":"2504.06560","title":"NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables","created":"2025-04-09","cited_by_count":8,"area":null},{"id":"2508.05201","title":"FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance","created":"2025-08-07","cited_by_count":8,"area":null},{"id":"2509.02033","title":"StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching","created":"2025-09-02","cited_by_count":8,"area":null},{"id":"2601.03851","title":"Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search","created":"2026-01-07","cited_by_count":8,"area":null},{"id":"2603.01712","title":"FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents","created":"2026-03-02","cited_by_count":8,"area":"agents"},{"id":"2604.10079","title":"Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models","created":"2026-04-11","cited_by_count":8,"area":null},{"id":"2503.12345","title":"General Table Question Answering via Answer-Formula Joint Generation","created":"2025-03-16","cited_by_count":7,"area":null},{"id":"2505.23242","title":"ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering","created":"2025-05-29","cited_by_count":7,"area":"lr"},{"id":"2506.04427","title":"Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance","created":"2025-06-04","cited_by_count":7,"area":null},{"id":"2506.19794","title":"Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study","created":"2025-06-24","cited_by_count":7,"area":null},{"id":"2510.20176","title":"Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding","created":"2025-10-23","cited_by_count":7,"area":"agents"},{"id":"2512.20312","title":"TableGPT-R1: Advancing Tabular Reasoning Through Reinforcement Learning","created":"2025-12-23","cited_by_count":7,"area":"lr"},{"id":"2602.09305","title":"Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation","created":"2026-02-10","cited_by_count":7,"area":"lr"},{"id":"2502.00329","title":"CoddLLM: Empowering Large Language Models for Data Analytics","created":"2025-02-01","cited_by_count":6,"area":null},{"id":"2601.04202","title":"TeleTables: A Benchmark for Large Language Models in Telecom Table Interpretation","created":"2025-12-05","cited_by_count":6,"area":null},{"id":"2505.15874","title":"Text-to-Pipeline: Bridging Natural Language and Data Preparation Pipelines","created":"2025-05-21","cited_by_count":5,"area":null},{"id":"2507.22462","title":"IFEvalCode: Controlled Code Generation","created":"2025-07-30","cited_by_count":5,"area":null},{"id":"2508.19813","title":"T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables","created":"2025-08-27","cited_by_count":5,"area":"lr"},{"id":"2510.06217","title":"TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning","created":"2025-10-07","cited_by_count":5,"area":"lr"},{"id":"2510.09671","title":"Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation","created":"2025-10-08","cited_by_count":5,"area":null},{"id":"2511.02219","title":"TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data","created":"2025-11-04","cited_by_count":5,"area":"lr"},{"id":"2601.17777","title":"DPI: Exploiting Parameter Heterogeneity for Interference-Free Fine-Tuning","created":"2026-01-25","cited_by_count":5,"area":null},{"id":"2603.09151","title":"Deep Tabular Research via Continual Experience-Driven Execution","created":"2026-03-10","cited_by_count":5,"area":null},{"id":"2501.19339","title":"PixelWorld: How Far Are We from Perceiving Everything as Pixels?","created":"2025-01-31","cited_by_count":4,"area":"agents"},{"id":"2506.08646","title":"TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning","created":"2025-06-10","cited_by_count":4,"area":"synth"},{"id":"2603.07316","title":"FinSheet-Bench: From Simple Lookups to Complex Reasoning, Where LLMs Break on Financial Spreadsheets","created":"2026-03-07","cited_by_count":4,"area":"lr"},{"id":"2605.30434","title":"LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis","created":"2026-05-28","cited_by_count":4,"area":"agents"},{"id":"2412.07629","title":"Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering","created":"2024-12-10","cited_by_count":3,"area":null},{"id":"2502.14848","title":"GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks","created":"2025-02-20","cited_by_count":3,"area":null},{"id":"2505.23667","title":"Formula-R1: Incentivizing LLM Reasoning over Complex Tables with Numerical Computation via Formula-Driven Reinforcement Learning","created":"2025-05-29","cited_by_count":3,"area":"lr"},{"id":"2506.12071","title":"T$^2$-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation","created":"2025-06-04","cited_by_count":3,"area":"rag"},{"id":"2506.18421","title":"TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models","created":"2025-06-23","cited_by_count":3,"area":"lr"},{"id":"2508.16265","title":"M3TQA: Massively Multilingual Multitask Table Question Answering","created":"2025-08-22","cited_by_count":3,"area":null},{"id":"2509.01312","title":"TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering","created":"2025-09-01","cited_by_count":3,"area":"agents"},{"id":"2510.13835","title":"ConDABench: Interactive Evaluation of Language Models for Data Analysis","created":"2025-10-10","cited_by_count":3,"area":null},{"id":"2511.04584","title":"Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis","created":"2025-11-06","cited_by_count":3,"area":null},{"id":"2511.10659","title":"Information Extraction From Fiscal Documents Using LLMs","created":"2025-11-03","cited_by_count":3,"area":null},{"id":"2512.12084","title":"FloodSQL-Bench: A Retrieval-Augmented Benchmark for Geospatially-Grounded Text-to-SQL","created":"2025-12-12","cited_by_count":3,"area":"lr"},{"id":"2601.22530","title":"Enhancing Table Reasoning with Deterministic Table-State Rewards","created":"2026-01-30","cited_by_count":3,"area":"lr"},{"id":"2603.29112","title":"GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification","created":"2026-03-31","cited_by_count":3,"area":null},{"id":"2501.14717","title":"What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects","created":"2025-01-24","cited_by_count":2,"area":null},{"id":"2505.15094","title":"SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Models","created":"2025-05-21","cited_by_count":2,"area":null},{"id":"2507.18442","title":"AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data","created":"2025-07-24","cited_by_count":2,"area":"lr"},{"id":"2604.14010","title":"Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning","created":"2026-04-15","cited_by_count":2,"area":null},{"id":"2604.22239","title":"Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA","created":"2026-04-24","cited_by_count":2,"area":null},{"id":"2607.12650","title":"Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs","created":"2026-07-14","cited_by_count":2,"area":"lr"},{"id":"2502.19209","title":"Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation","created":"2025-02-26","cited_by_count":1,"area":"rag"},{"id":"2504.20047","title":"HCT-QA: A Benchmark for Question Answering on Human-Centric Tables","created":"2025-03-09","cited_by_count":1,"area":null},{"id":"2506.20747","title":"Towards Probabilistic Question Answering Over Tabular Data","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2507.06138","title":"Coding Triangle: How Does Large Language Model Understand Code?","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2507.10934","title":"Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models","created":"2025-07-15","cited_by_count":1,"area":null},{"id":"2507.23227","title":"Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs","created":"2025-07-31","cited_by_count":1,"area":null},{"id":"2508.06110","title":"PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion","created":"2025-08-08","cited_by_count":1,"area":"lr"},{"id":"2511.17238","title":"Lost in Translation and Noise: A Deep Dive into the Failure Modes of VLMs on Real-World Tables","created":"2025-11-21","cited_by_count":1,"area":null},{"id":"2601.07280","title":"ReasonTabQA: A Comprehensive Benchmark for Table Question Answering from Real World Industrial Scenarios","created":"2026-01-12","cited_by_count":1,"area":"lr"},{"id":"2601.07696","title":"Exploring the Meta-level Reasoning of Large Language Models via a Tool-based Multi-hop Tabular Question Answering Task","created":"2026-01-12","cited_by_count":1,"area":"lr"},{"id":"2602.09642","title":"MATA: Multi-Agent Framework for Reliable and Flexible Table Question Answering","created":"2026-02-10","cited_by_count":1,"area":"agents"},{"id":"2604.13201","title":"InfiniteScienceGym: An Unbounded, Procedurally-Generated Benchmark for Scientific Analysis","created":"2026-04-14","cited_by_count":1,"area":null},{"id":"2605.01018","title":"WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild","created":"2026-05-01","cited_by_count":1,"area":null},{"id":"2605.18851","title":"STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning","created":"2026-05-13","cited_by_count":1,"area":"lr"},{"id":"2606.32029","title":"When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors","created":"2026-06-30","cited_by_count":1,"area":null},{"id":"2607.06482","title":"Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities","created":"2026-07-07","cited_by_count":1,"area":null},{"id":"2609.25655","title":"From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs","created":"2026-09-22","cited_by_count":1,"area":"moe"},{"id":"2505.06605","title":"Using External knowledge to Enhanced PLM for Semantic Matching","created":"2025-05-10","cited_by_count":0,"area":null},{"id":"2505.06607","title":"Boosting Neural Language Inference via Cascaded Interactive Reasoning","created":"2025-05-10","cited_by_count":0,"area":"lr"},{"id":"2505.21409","title":"RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models","created":"2025-05-27","cited_by_count":0,"area":null},{"id":"2512.06859","title":"JT-DA: Enhancing Data Analysis with Tool-Integrated Table Reasoning Large Language Models","created":"2025-12-07","cited_by_count":0,"area":"lr"},{"id":"2601.03137","title":"Accurate Table Question Answering with Accessible LLMs","created":"2026-01-06","cited_by_count":0,"area":null},{"id":"2602.08548","title":"How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Location","created":"2026-02-09","cited_by_count":0,"area":null}],"next_cursor":"100"}