{"list":"other","total":84,"rows":[{"id":"2211.14730","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","created":"2022-11-27","cited_by_count":2039},{"id":"2205.13504","title":"Are Transformers Effective for Time Series Forecasting?","created":"2022-05-26","cited_by_count":1424},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1602.07576","title":"Group Equivariant Convolutional Networks","created":"2016-02-24","cited_by_count":1165},{"id":"1703.00564","title":"MoleculeNet: A Benchmark for Molecular Machine Learning","created":"2017-03-02","cited_by_count":1051},{"id":"2205.06175","title":"A Generalist Agent","created":"2022-05-12","cited_by_count":808},{"id":"2401.06209","title":"Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs","created":"2024-01-11","cited_by_count":735},{"id":"2212.07677","title":"Transformers learn in-context by gradient descent","created":"2022-12-15","cited_by_count":732},{"id":"2208.01066","title":"What Can Transformers Learn In-Context? A Case Study of Simple Function Classes","created":"2022-08-01","cited_by_count":715},{"id":"2310.01728","title":"Time-LLM: Time Series Forecasting by Reprogramming Large Language Models","created":"2023-10-03","cited_by_count":700},{"id":"2211.15661","title":"What learning algorithm is in-context learning? Investigations with linear models","created":"2022-11-28","cited_by_count":684},{"id":"2207.01848","title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","created":"2022-07-05","cited_by_count":674},{"id":"2302.11939","title":"One Fits All:Power General Time Series Analysis by Pretrained LM","created":"2023-02-23","cited_by_count":632},{"id":"2107.14795","title":"Perceiver IO: A General Architecture for Structured Inputs & Outputs","created":"2021-07-30","cited_by_count":629},{"id":"2404.12390","title":"BLINK: Multimodal Large Language Models Can See but Not Perceive","created":"2024-04-18","cited_by_count":550},{"id":"2305.18654","title":"Faith and Fate: Limits of Transformers on Compositionality","created":"2023-05-29","cited_by_count":503},{"id":"2310.07820","title":"Large Language Models Are Zero-Shot Time Series Forecasters","created":"2023-10-11","cited_by_count":500},{"id":"2302.07842","title":"Augmented Language Models: a Survey","created":"2023-02-15","cited_by_count":403},{"id":"2308.16911","title":"PointLLM: Empowering Large Language Models to Understand Point Clouds","created":"2023-08-31","cited_by_count":333},{"id":"2109.01247","title":"Do Prompt-Based Models Really Understand the Meaning of their Prompts?","created":"2021-09-02","cited_by_count":326},{"id":"2401.03065","title":"CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution","created":"2024-01-05","cited_by_count":300},{"id":"2210.10723","title":"TabLLM: Few-shot Classification of Tabular Data with Large Language Models","created":"2022-10-19","cited_by_count":277},{"id":"2305.10037","title":"Can Language Models Solve Graph Problems in Natural Language?","created":"2023-05-17","cited_by_count":252},{"id":"2310.13023","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","created":"2023-10-19","cited_by_count":226},{"id":"2106.09553","title":"Large-Scale Chemical Language Representations Capture Molecular Structure and Properties","created":"2021-06-17","cited_by_count":206},{"id":"2402.08170","title":"LLaGA: Large Language and Graph Assistant","created":"2024-02-13","cited_by_count":181},{"id":"2308.08493","title":"Time Travel in LLMs: Tracing Data Contamination in Large Language Models","created":"2023-08-16","cited_by_count":177},{"id":"2310.04560","title":"Talk like a Graph: Encoding Graphs for Large Language Models","created":"2023-10-06","cited_by_count":176},{"id":"2406.16964","title":"Are Language Models Actually Useful for Time Series Forecasting?","created":"2024-06-22","cited_by_count":168},{"id":"2206.06565","title":"LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks","created":"2022-06-14","cited_by_count":148},{"id":"2511.08667","title":"TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models","created":"2025-11-11","cited_by_count":132},{"id":"2505.23719","title":"TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning","created":"2025-05-29","cited_by_count":120},{"id":"2406.14852","title":"Is A Picture Worth A Thousand Words? Delving Into Spatial Reasoning for Vision Language Models","created":"2024-06-21","cited_by_count":113},{"id":"2410.18164","title":"TabDPT: Scaling Tabular Foundation Models on Real Data","created":"2024-10-23","cited_by_count":113},{"id":"2403.07300","title":"CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning","created":"2024-03-12","cited_by_count":108},{"id":"2207.10551","title":"Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?","created":"2022-07-21","cited_by_count":103},{"id":"2310.11829","title":"Graph Foundation Models: Concepts, Opportunities and Challenges","created":"2023-10-18","cited_by_count":103},{"id":"2203.02167","title":"SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models","created":"2022-03-04","cited_by_count":91},{"id":"2402.02216","title":"Position: Graph Foundation Models are Already Here","created":"2024-02-03","cited_by_count":86},{"id":"2402.16785","title":"CARTE: Pretraining and Transfer for Tabular Learning","created":"2024-02-26","cited_by_count":76},{"id":"2406.12031","title":"Large Scale Transfer Learning for Tabular Data via Language Modeling","created":"2024-06-17","cited_by_count":76},{"id":"2405.08011","title":"A Survey of Large Language Models for Graphs","created":"2024-05-10","cited_by_count":72},{"id":"2506.15692","title":"MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement","created":"2025-05-27","cited_by_count":58},{"id":"2507.06952","title":"What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models","created":"2025-07-09","cited_by_count":47},{"id":"2505.15116","title":"Graph Foundation Models: A Comprehensive Survey","created":"2025-05-21","cited_by_count":44},{"id":"2308.13916","title":"Exploring Large Language Models for Knowledge Graph Completion","created":"2023-08-26","cited_by_count":43},{"id":"2503.14434","title":"LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers","created":"2025-03-18","cited_by_count":38},{"id":"2408.12186","title":"Transformers are Minimax Optimal Nonparametric In-Context Learners","created":"2024-08-22","cited_by_count":37},{"id":"2404.06209","title":"Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models","created":"2024-04-09","cited_by_count":36},{"id":"2311.00237","title":"The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis","created":"2023-11-01","cited_by_count":34},{"id":"2402.14547","title":"OmniPred: Language Models as Universal Regressors","created":"2024-02-22","cited_by_count":34},{"id":"2410.12360","title":"Towards Neural Scaling Laws for Time Series Foundation Models","created":"2024-10-16","cited_by_count":33},{"id":"2408.08656","title":"LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs","created":"2024-08-16","cited_by_count":25},{"id":"2510.10981","title":"In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning","created":"2025-10-13","cited_by_count":22},{"id":"2606.30410","title":"Beyond IID: How General Are Tabular Foundation Models, Really?","created":"2026-06-29","cited_by_count":20},{"id":"2502.17504","title":"Protein Large Language Models: A Comprehensive Survey","created":"2025-02-21","cited_by_count":18},{"id":"2506.05574","title":"When can in-context learning generalize out of task distribution?","created":"2025-06-05","cited_by_count":16},{"id":"2510.13654","title":"Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challenges","created":"2025-10-15","cited_by_count":16},{"id":"2402.02212","title":"A Survey to Recent Progress Towards Understanding In-Context Learning","created":"2024-02-03","cited_by_count":14},{"id":"2603.06148","title":"VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models","created":"2026-03-06","cited_by_count":14},{"id":"2402.02054","title":"Towards Neural Scaling Laws on Graphs","created":"2024-02-03","cited_by_count":13},{"id":"2501.19215","title":"Strassen Attention, Split VC Dimension and Compositionality in Transformers","created":"2025-01-31","cited_by_count":12},{"id":"2412.19994","title":"From Generalist to Specialist: A Survey of Large Language Models for Chemistry","created":"2024-12-28","cited_by_count":11},{"id":"2602.03916","title":"SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?","created":"2026-02-03","cited_by_count":11},{"id":"2310.13829","title":"Universal Representation of Permutation-Invariant Functions on Vectors and Tensors","created":"2023-10-20","cited_by_count":10},{"id":"2310.03266","title":"UniPredict: Large Language Models are Universal Tabular Classifiers","created":"2023-10-05","cited_by_count":9},{"id":"2511.10234","title":"Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners","created":"2025-11-13","cited_by_count":6},{"id":"2411.04324","title":"Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning","created":"2024-11-06","cited_by_count":5},{"id":"2602.04031","title":"The Illusion of Generalization in Tabular Language Models","created":"2026-02-03","cited_by_count":4},{"id":"2502.08662","title":"RoToR: Towards More Reliable Responses for Order-Invariant Inputs","created":"2025-02-10","cited_by_count":3},{"id":"2507.09440","title":"Transformers Don't In-Context Learn Least Squares Regression","created":"2025-07-13","cited_by_count":3},{"id":"2503.01215","title":"Architectural and Inferential Inductive Biases For Exchangeable Sequence Modeling","created":"2025-03-03","cited_by_count":2},{"id":"2606.09731","title":"Tight Sample Complexity of Transformers","created":"2026-06-08","cited_by_count":2},{"id":"2608.01684","title":"GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks","created":"2026-08-03","cited_by_count":2},{"id":"2410.04751","title":"Intriguing Properties of Large Language and Vision Models","created":"2024-10-07","cited_by_count":1},{"id":"2410.09298","title":"DeepOSets: Non-Autoregressive In-Context Learning with Permutation-Invariance Inductive Bias","created":"2024-10-11","cited_by_count":1},{"id":"2510.01163","title":"How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off","created":"2025-10-01","cited_by_count":1},{"id":"2601.10820","title":"Towards Reliable ML Feature Engineering via Planning in Constrained-Topology of LLM Agents","created":"2026-01-15","cited_by_count":1},{"id":"2603.12545","title":"Spatial Reasoning is Not a Free Lunch: A Controlled Study on LLaVA","created":"2026-03-13","cited_by_count":1},{"id":"2606.09484","title":"Detecting Differences Is Not Understanding Structure: Large Language Models Fail at Graph Isomorphism","created":"2026-06-08","cited_by_count":1},{"id":"2606.19560","title":"Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting","created":"2026-06-17","cited_by_count":1},{"id":"2607.13115","title":"Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools","created":"2026-07-14","cited_by_count":1},{"id":"2608.02412","title":"Why Large Language Models Fail at Tabular Prediction","created":"2026-08-03","cited_by_count":1}],"next_cursor":null}