{"total":34,"rows":[{"id":"2507.10524","title":"Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation","created":"2025-07-14","cited_by_count":133,"area":null},{"id":"2410.20672","title":"Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA","created":"2024-10-28","cited_by_count":78,"area":null},{"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":"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":"2410.09375","title":"Looped ReLU MLPs May Be All You Need as Practical Programmable Computers","created":"2024-10-12","cited_by_count":19,"area":null},{"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":"2410.21698","title":"On the Role of Depth and Looping for In-Context Learning with Task Diversity","created":"2024-10-29","cited_by_count":11,"area":null},{"id":"2606.18524","title":"On the Residual Scaling of Looped Transformers: Stability and Transferability","created":"2026-06-16","cited_by_count":10,"area":null},{"id":"2605.20670","title":"LT2: Linear-Time Looped Transformers","created":"2026-05-20","cited_by_count":7,"area":null},{"id":"2607.13491","title":"DeepLoop: Depth Scaling for Looped Transformers","created":"2026-07-15","cited_by_count":7,"area":null},{"id":"2410.12783","title":"Context-Scaling versus Task-Scaling in In-Context Learning","created":"2024-10-16","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":"2607.20594","title":"When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers","created":"2026-07-22","cited_by_count":6,"area":null},{"id":"2410.11279","title":"Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study","created":"2024-10-15","cited_by_count":4,"area":null},{"id":"2410.11687","title":"Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks","created":"2024-10-15","cited_by_count":3,"area":null},{"id":"2505.14042","title":"Adversarially Pretrained Transformers May Be Universally Robust In-Context Learners","created":"2025-05-20","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":"2602.00620","title":"Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference","created":"2026-01-31","cited_by_count":3,"area":null},{"id":"2605.09867","title":"Continuous Latent Contexts Enable Efficient Online Learning in Transformers","created":"2026-05-11","cited_by_count":3,"area":null},{"id":"2501.15665","title":"StagFormer: Time Staggering Transformer Decoding for RunningLayers In Parallel","created":"2025-01-26","cited_by_count":2,"area":null},{"id":"2410.19931","title":"Provable optimal transport with transformers: The essence of depth and prompt engineering","created":"2024-10-25","cited_by_count":1,"area":null},{"id":"2512.14531","title":"VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse","created":"2025-12-16","cited_by_count":1,"area":null},{"id":"2605.06609","title":"Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2606.06814","title":"The Effect of Training Task Diversity on In-Context Learning through the Lens of Low-Dimensional Subspaces","created":"2026-06-05","cited_by_count":1,"area":null},{"id":"2607.02800","title":"Induction Heads Interpolate N-Grams","created":"2026-07-02","cited_by_count":1,"area":null},{"id":"2502.19787","title":"In-Context Learning with Hypothesis-Class Guidance","created":"2025-02-27","cited_by_count":0,"area":null},{"id":"2510.18638","title":"Optimality and NP-Hardness of Transformers in Learning Markovian Dynamical Functions","created":"2025-10-21","cited_by_count":0,"area":null},{"id":"2603.29057","title":"LA-Sign: Looped Transformers with Geometry-aware Alignment for Skeleton-based Sign Language Recognition","created":"2026-03-30","cited_by_count":0,"area":null},{"id":"2604.11912","title":"How Transformers Learn to Plan via Multi-Token Prediction","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2609.37379","title":"Looped Transformers as Optimizers","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.38426","title":"LoopVL: Recurrent Visual Intelligence","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.39892","title":"Shared Weights, Selected Computations: How Looped Transformers Route What Each Loop Does","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.08694","title":"GeneICL: A Tabular Foundation Model for Bulk Transcriptomics","created":"2026-10-06","cited_by_count":0,"area":null},{"id":"2610.14456","title":"Learning to Extrapolate in Depth with Looped Transformers","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}