{"total":115,"rows":[{"id":"2402.14735","title":"How Transformers Learn Causal Structure with Gradient Descent","created":"2024-02-22","cited_by_count":118,"area":null},{"id":"2403.15371","title":"Can large language models explore in-context?","created":"2024-03-22","cited_by_count":76,"area":null},{"id":"2312.06528","title":"Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context","created":"2023-12-11","cited_by_count":63,"area":null},{"id":"2404.07544","title":"From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples","created":"2024-04-11","cited_by_count":57,"area":null},{"id":"2410.13835","title":"Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs","created":"2024-10-17","cited_by_count":56,"area":null},{"id":"2406.16437","title":"Theory on Mixture-of-Experts in Continual Learning","created":"2024-06-24","cited_by_count":54,"area":"moe"},{"id":"2410.08633","title":"Transformers Provably Solve Parity Efficiently with Chain of Thought","created":"2024-10-11","cited_by_count":50,"area":"lr"},{"id":"2402.01258","title":"Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape","created":"2024-02-02","cited_by_count":46,"area":null},{"id":"2501.16265","title":"Training Dynamics of In-Context Learning in Linear Attention","created":"2025-01-27","cited_by_count":46,"area":null},{"id":"2405.19592","title":"Why Larger Language Models Do In-context Learning Differently?","created":"2024-05-30","cited_by_count":45,"area":null},{"id":"2405.18634","title":"A Theoretical Understanding of Self-Correction through In-context Alignment","created":"2024-05-28","cited_by_count":44,"area":"lr"},{"id":"2401.15530","title":"An Information-Theoretic Analysis of In-Context Learning","created":"2024-01-28","cited_by_count":40,"area":null},{"id":"2408.10147","title":"In-Context Learning with Representations: Contextual Generalization of Trained Transformers","created":"2024-08-19","cited_by_count":38,"area":null},{"id":"2504.10957","title":"When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers","created":"2025-04-15","cited_by_count":38,"area":"safety"},{"id":"2408.12186","title":"Transformers are Minimax Optimal Nonparametric In-Context Learners","created":"2024-08-22","cited_by_count":37,"area":null},{"id":"2402.15607","title":"How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?","created":"2024-02-23","cited_by_count":35,"area":null},{"id":"2403.08081","title":"Mechanics of Next Token Prediction with Self-Attention","created":"2024-03-12","cited_by_count":34,"area":null},{"id":"2502.21212","title":"Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought","created":"2025-02-28","cited_by_count":32,"area":"lr"},{"id":"2411.02544","title":"Pretrained transformer efficiently learns low-dimensional target functions in-context","created":"2024-11-04","cited_by_count":31,"area":null},{"id":"2601.13474","title":"Preconditioning Benefits of Spectral Orthogonalization in Muon","created":"2026-01-20","cited_by_count":30,"area":null},{"id":"2408.14511","title":"Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods","created":"2024-08-25","cited_by_count":29,"area":"lr"},{"id":"2402.14180","title":"Linear Transformers are Versatile In-Context Learners","created":"2024-02-21","cited_by_count":28,"area":null},{"id":"2405.04669","title":"Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics","created":"2024-05-07","cited_by_count":27,"area":"lr"},{"id":"2403.03183","title":"How Well Can Transformers Emulate In-context Newton's Method?","created":"2024-03-05","cited_by_count":25,"area":null},{"id":"2410.09695","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","created":"2024-10-13","cited_by_count":23,"area":null},{"id":"2402.11639","title":"In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness","created":"2024-02-18","cited_by_count":22,"area":null},{"id":"2406.06893","title":"Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot","created":"2024-06-11","cited_by_count":22,"area":null},{"id":"2410.11268","title":"Bypassing the Exponential Dependency: Looped Transformers Efficiently Learn In-context by Multi-step Gradient Descent","created":"2024-10-15","cited_by_count":22,"area":null},{"id":"2401.07187","title":"A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models","created":"2024-01-14","cited_by_count":20,"area":null},{"id":"2502.13913","title":"How Do LLMs Perform Two-Hop Reasoning in Context?","created":"2025-02-19","cited_by_count":20,"area":"lr"},{"id":"2403.08699","title":"Implicit Regularization of Gradient Flow on One-Layer Softmax Attention","created":"2024-03-13","cited_by_count":19,"area":null},{"id":"2401.17426","title":"Superiority of Multi-Head Attention in In-Context Linear Regression","created":"2024-01-30","cited_by_count":17,"area":null},{"id":"2411.10830","title":"One-Layer Transformer Provably Learns One-Nearest Neighbor In Context","created":"2024-11-16","cited_by_count":16,"area":null},{"id":"2410.11778","title":"On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures","created":"2024-10-15","cited_by_count":15,"area":null},{"id":"2408.04532","title":"How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression","created":"2024-08-08","cited_by_count":14,"area":null},{"id":"2410.02167","title":"Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis","created":"2024-10-03","cited_by_count":14,"area":"lr"},{"id":"2502.04204","title":"Short-length Adversarial Training Helps LLMs Defend Long-length Jailbreak Attacks: Theoretical and Empirical Evidence","created":"2025-02-06","cited_by_count":14,"area":"safety"},{"id":"2409.17335","title":"Non-asymptotic Convergence of Training Transformers for Next-token Prediction","created":"2024-09-25","cited_by_count":13,"area":null},{"id":"2410.16540","title":"A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration","created":"2024-10-21","cited_by_count":13,"area":"lr"},{"id":"2504.04308","title":"Gating is Weighting: Understanding Gated Linear Attention through In-context Learning","created":"2025-04-06","cited_by_count":12,"area":null},{"id":"2402.13572","title":"AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures","created":"2024-02-21","cited_by_count":11,"area":null},{"id":"2411.07538","title":"Unraveling the Gradient Descent Dynamics of Transformers","created":"2024-11-12","cited_by_count":11,"area":null},{"id":"2505.00926","title":"How Transformers Learn Regular Language Recognition: A Theoretical Study on Training Dynamics and Implicit Bias","created":"2025-05-02","cited_by_count":11,"area":null},{"id":"2506.10887","title":"Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers","created":"2025-06-12","cited_by_count":10,"area":"lr"},{"id":"2410.09605","title":"Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis","created":"2024-10-12","cited_by_count":9,"area":null},{"id":"2410.14581","title":"Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection","created":"2024-10-18","cited_by_count":9,"area":null},{"id":"2311.00226","title":"Transformers are Provably Optimal In-context Estimators for Wireless Communications","created":"2023-11-01","cited_by_count":8,"area":null},{"id":"2406.17167","title":"Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis","created":"2024-06-24","cited_by_count":8,"area":null},{"id":"2410.01265","title":"Transformers Handle Endogeneity in In-Context Linear Regression","created":"2024-10-02","cited_by_count":8,"area":null},{"id":"2410.11474","title":"How Transformers Get Rich: Approximation and Dynamics Analysis","created":"2024-10-15","cited_by_count":8,"area":null},{"id":"2506.07440","title":"Federated In-Context Learning: Iterative Refinement for Improved Answer Quality","created":"2025-06-09","cited_by_count":8,"area":null},{"id":"2501.18187","title":"On the Role of Transformer Feed-Forward Layers in Nonlinear In-Context Learning","created":"2025-01-30","cited_by_count":7,"area":null},{"id":"2409.12293","title":"In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning","created":"2024-09-18","cited_by_count":6,"area":null},{"id":"2410.13981","title":"On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery","created":"2024-10-17","cited_by_count":6,"area":null},{"id":"2505.14808","title":"Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective","created":"2025-05-20","cited_by_count":6,"area":null},{"id":"2506.05200","title":"Transformers Meet In-Context Learning: A Universal Approximation Theory","created":"2025-06-05","cited_by_count":6,"area":null},{"id":"2506.05249","title":"On the Convergence of Gradient Descent on Learning Transformers with Residual Connections","created":"2025-06-05","cited_by_count":6,"area":null},{"id":"2402.00743","title":"Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data","created":"2024-02-01","cited_by_count":5,"area":null},{"id":"2405.16845","title":"On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability","created":"2024-05-27","cited_by_count":5,"area":null},{"id":"2410.23610","title":"Global Convergence in Training Large-Scale Transformers","created":"2024-10-31","cited_by_count":5,"area":null},{"id":"2502.06007","title":"Transformers versus the EM Algorithm in Multi-class Clustering","created":"2025-02-09","cited_by_count":5,"area":null},{"id":"2502.15609","title":"On the Robustness of Transformers against Context Hijacking for Linear Classification","created":"2025-02-21","cited_by_count":4,"area":null},{"id":"2505.19531","title":"Minimalist Softmax Attention Provably Learns Constrained Boolean Functions","created":"2025-05-26","cited_by_count":4,"area":null},{"id":"2507.20443","title":"Provable In-Context Learning of Nonlinear Regression with Transformers","created":"2025-07-28","cited_by_count":4,"area":null},{"id":"2510.10425","title":"Softmax $\\geq$ Linear: Transformers may learn to classify in-context by kernel gradient descent","created":"2025-10-12","cited_by_count":4,"area":null},{"id":"2511.07372","title":"Provable Benefit of Curriculum in Transformer Tree-Reasoning Post-Training","created":"2025-11-10","cited_by_count":4,"area":"lr"},{"id":"2406.00131","title":"From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When","created":"2024-05-31","cited_by_count":3,"area":null},{"id":"2502.07553","title":"Transformers Provably Learn Sparse XOR with Polylogarithmic Parameters","created":"2025-02-11","cited_by_count":3,"area":null},{"id":"2502.17024","title":"Towards Auto-Regressive Next-Token Prediction: In-Context Learning Emerges from Generalization","created":"2025-02-24","cited_by_count":3,"area":null},{"id":"2502.20681","title":"Disentangling Feature Structure: A Mathematically Provable Two-Stage Training Dynamics in Transformers","created":"2025-02-28","cited_by_count":3,"area":null},{"id":"2507.13540","title":"Provable Low-Frequency Bias of In-Context Learning of Representations","created":"2025-07-17","cited_by_count":3,"area":null},{"id":"2509.25741","title":"Test time training enhances in-context learning of nonlinear functions","created":"2025-09-30","cited_by_count":3,"area":null},{"id":"2512.10656","title":"Token Sample Complexity of Attention","created":"2025-12-11","cited_by_count":3,"area":null},{"id":"2512.11784","title":"Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective","created":"2025-12-12","cited_by_count":3,"area":null},{"id":"2605.05176","title":"Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer","created":"2026-05-06","cited_by_count":3,"area":null},{"id":"2408.02288","title":"Spin glass model of in-context learning","created":"2024-08-05","cited_by_count":2,"area":null},{"id":"2410.09411","title":"Towards the Effect of Examples on In-Context Learning: A Theoretical Case Study","created":"2024-10-12","cited_by_count":2,"area":null},{"id":"2501.15708","title":"StaICC: Standardized Evaluation for Classification Task in In-context Learning","created":"2025-01-27","cited_by_count":2,"area":null},{"id":"2509.23779","title":"Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression","created":"2025-09-28","cited_by_count":2,"area":"ssm"},{"id":"2510.00399","title":"How Can Mamba Learn In Context with Outliers and Generalize Provably?","created":"2025-10-01","cited_by_count":2,"area":"ssm"},{"id":"2510.25542","title":"Transformers Provably Learn Directed Acyclic Graphs via Kernel-Guided Mutual Information","created":"2025-10-29","cited_by_count":2,"area":null},{"id":"2511.21056","title":"Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation","created":"2025-11-26","cited_by_count":2,"area":null},{"id":"2511.23239","title":"Towards Understanding Transformers in Learning Random Walks","created":"2025-11-28","cited_by_count":2,"area":null},{"id":"2601.10058","title":"Unlabeled Data Can Provably Enhance In-Context Learning of Transformers","created":"2026-01-15","cited_by_count":2,"area":null},{"id":"2602.02470","title":"Breaking the Reversal Curse in Autoregressive Language Models via Identity Bridge","created":"2026-02-02","cited_by_count":2,"area":"lr"},{"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":"2602.19143","title":"Incremental Learning of Sparse Attention Patterns in Transformers","created":"2026-02-22","cited_by_count":2,"area":null},{"id":"2604.10946","title":"Learning to Adapt: In-Context Learning Beyond Stationarity","created":"2026-04-13","cited_by_count":2,"area":null},{"id":"2605.28600","title":"Transformers Provably Learn to Internalize Chain-of-Thought","created":"2026-05-27","cited_by_count":2,"area":"lr"},{"id":"2410.09701","title":"Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models","created":"2024-10-13","cited_by_count":1,"area":null},{"id":"2410.14183","title":"In-context Learning for Mixture of Linear Regressions: Existence, Generalization and Training Dynamics","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2505.15009","title":"How Transformers Learn In-Context Recall Tasks? Optimality, Training Dynamics and Generalization","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2510.12026","title":"Mamba Can Learn Low-Dimensional Targets In-Context via Test-Time Feature Learning","created":"2025-10-14","cited_by_count":1,"area":"ssm"},{"id":"2510.19797","title":"Transformers are almost optimal metalearners for linear classification","created":"2025-10-22","cited_by_count":1,"area":null},{"id":"2601.16514","title":"Finite-Time Analysis of Gradient Descent for Shallow Transformers","created":"2026-01-23","cited_by_count":1,"area":null},{"id":"2601.16873","title":"Provably Learning Attention with Queries","created":"2026-01-23","cited_by_count":1,"area":null},{"id":"2602.23197","title":"Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models","created":"2026-02-26","cited_by_count":1,"area":null},{"id":"2604.06440","title":"Visual prompting reimagined: The power of the Activation Prompts","created":"2026-04-07","cited_by_count":1,"area":null},{"id":"2604.08564","title":"Attention-Based Sampler for Diffusion Language Models","created":"2026-03-18","cited_by_count":1,"area":"diffusion"},{"id":"2604.12817","title":"Understanding and Improving Continuous Adversarial Training for LLMs via In-context Learning Theory","created":"2026-04-14","cited_by_count":1,"area":"safety"}],"next_cursor":"100"}