{"total":56,"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":"2509.09677","title":"The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs","created":"2025-09-11","cited_by_count":64,"area":null},{"id":"2410.01405","title":"On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding","created":"2024-10-02","cited_by_count":36,"area":null},{"id":"2606.18206","title":"Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers","created":"2026-06-16","cited_by_count":28,"area":null},{"id":"2510.24824","title":"Parallel Loop Transformer for Efficient Test-Time Computation Scaling","created":"2025-10-28","cited_by_count":24,"area":null},{"id":"2507.07996","title":"Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs","created":"2025-07-10","cited_by_count":16,"area":null},{"id":"2506.09251","title":"Extrapolation by Association: Length Generalization Transfer in Transformers","created":"2025-06-10","cited_by_count":13,"area":null},{"id":"2602.11698","title":"SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion","created":"2026-02-12","cited_by_count":12,"area":null},{"id":"2506.14202","title":"DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation","created":"2025-06-17","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":"2606.06574","title":"Skip a Layer or Loop It? Learning Program-of-Layers in LLMs","created":"2026-06-04","cited_by_count":9,"area":null},{"id":"2510.07739","title":"MeSH: Memory-as-State-Highways for Recursive Transformers","created":"2025-10-09","cited_by_count":8,"area":null},{"id":"2510.27015","title":"Quantitative Bounds for Length Generalization in Transformers","created":"2025-10-30","cited_by_count":8,"area":null},{"id":"2602.16490","title":"From Growing to Looping: A Unified View of Iterative Computation in LLMs","created":"2026-02-18","cited_by_count":7,"area":null},{"id":"2605.20670","title":"LT2: Linear-Time Looped Transformers","created":"2026-05-20","cited_by_count":7,"area":null},{"id":"2605.16343","title":"LoopQ: Quantization for Looped Language Models","created":"2026-05-08","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":"2602.10097","title":"Step-resolved data attribution for looped transformers","created":"2026-02-10","cited_by_count":5,"area":null},{"id":"2606.25008","title":"Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients","created":"2026-06-23","cited_by_count":5,"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":"2510.14826","title":"To Infinity and Beyond: Tool-Use Unlocks Length Generalization in State Space Models","created":"2025-10-16","cited_by_count":4,"area":null},{"id":"2606.29983","title":"Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping","created":"2026-06-29","cited_by_count":4,"area":null},{"id":"2505.01855","title":"Intra-Layer Recurrence in Transformers for Language Modeling","created":"2025-05-03","cited_by_count":3,"area":null},{"id":"2511.00907","title":"Transformers as Intrinsic Optimizers: Forward Inference through the Energy Principle","created":"2025-11-02","cited_by_count":3,"area":null},{"id":"2602.01651","title":"On the Spatiotemporal Dynamics of Generalization in Neural Networks","created":"2026-02-02","cited_by_count":3,"area":null},{"id":"2604.18839","title":"One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Models","created":"2026-04-20","cited_by_count":3,"area":null},{"id":"2604.01577","title":"Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization","created":"2026-04-02","cited_by_count":2,"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":"2601.14096","title":"Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2603.20801","title":"Large Neighborhood Search meets Iterative Neural Constraint Heuristics","created":"2026-03-21","cited_by_count":1,"area":null},{"id":"2605.26035","title":"Length Generalization with Log-Depth Recurrent Units","created":"2026-05-25","cited_by_count":1,"area":null},{"id":"2609.31360","title":"Programs-of-Layers in LLMs through the Lens of Cortical Areas","created":"2026-09-25","cited_by_count":1,"area":null},{"id":"2610.00673","title":"Closing the Loop: Practical Training Recipes for Looped Language Models","created":"2026-09-30","cited_by_count":1,"area":null},{"id":"2502.00197","title":"Learning Model Successors","created":"2025-01-31","cited_by_count":0,"area":null},{"id":"2506.00920","title":"Position as Probability: Self-Supervised Transformers that Think Past Their Training for Length Extrapolation","created":"2025-06-01","cited_by_count":0,"area":null},{"id":"2508.08298","title":"Channel-Wise MLPs Improve the Generalization of Recurrent Convolutional Networks","created":"2025-08-06","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":"2603.29069","title":"On the Mirage of Long-Range Dependency, with an Application to Integer Multiplication","created":"2026-03-30","cited_by_count":0,"area":null},{"id":"2604.15306","title":"Generalization in LLM Problem Solving: The Case of the Shortest Path","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2605.30229","title":"Anti Mode-Collapse in Mean-Field Transformer via Auxiliary Variables","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.20737","title":"Repeated Shared Access Enables Grokking, but Edit Propagation Depends on an Addressable Memory","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2607.17710","title":"Planning with Transformers: Chain of Computation and Structured Context Windows","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2608.24319","title":"Can a Dynamic Internal Field Govern a Transformer's Cognition? Certifiability, not Superiority, in Homeostatic Compute Control","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.11913","title":"Distance generalization in transformers: why bother with positional encoding?","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.36108","title":"LoopICL: Looping a single transformer block to solve tabular tasks","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36585","title":"Transformers Stop Thinking Too Early, and a Tiny LoRA Fixes It","created":"2026-09-29","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":"2610.00541","title":"Random Recursive Models","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.02185","title":"Decoding Looped Transformers Better for (Almost) Free","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.14030","title":"Rotate the Loop: Blockwise Conformality for Looped Transformers","created":"2026-09-30","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},{"id":"2610.14998","title":"Recurrent In-Context Learning for Tabular Foundation Models","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.18174","title":"Looped Reasoning Finishes Earlier Than You Think","created":"2026-10-13","cited_by_count":0,"area":null},{"id":"2610.19768","title":"Learning to Compute Is Not Learning to Retain or Reuse in Looped Transformers","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}