{"total":24,"rows":[{"id":"2604.12946","title":"Parcae: Scaling Laws For Stable Looped Language Models","created":"2026-04-14","cited_by_count":55,"area":null},{"id":"2604.21106","title":"How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models","created":"2026-04-22","cited_by_count":24,"area":null},{"id":"2604.21254","title":"Hyperloop Transformers","created":"2026-04-23","cited_by_count":12,"area":null},{"id":"2605.06510","title":"Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models","created":"2026-05-07","cited_by_count":10,"area":null},{"id":"2601.10242","title":"Loop as a Bridge: Can Looped Transformers Truly Link Representation Space and Natural Language Outputs?","created":"2026-01-15","cited_by_count":8,"area":null},{"id":"2601.16979","title":"A Scalable Measure of Loss Landscape Curvature for Analyzing the Training Dynamics of LLMs","created":"2026-01-23","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":"2602.08864","title":"Understanding Dynamic Compute Allocation in Recurrent Transformers","created":"2026-02-09","cited_by_count":6,"area":null},{"id":"2608.11233","title":"Retrofitting Recurrent Depth into a Pretrained Language Model: Installation, Extrapolation, Transfer, and Retention at Two Parameter Budgets","created":"2026-07-31","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":"2604.17121","title":"The Topological Trouble With Transformers","created":"2026-04-18","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":"2604.02051","title":"Ouroboros: Dynamic Weight Generation for Recursive Transformers via Input-Conditioned LoRA Modulation","created":"2026-04-02","cited_by_count":1,"area":null},{"id":"2609.21383","title":"Prediction Dynamics in Depth-Recurrent Language Models","created":"2026-09-18","cited_by_count":1,"area":null},{"id":"2609.30820","title":"Quantizing Looped Transformers: Feedback Exposure and Calibration Blindness","created":"2026-09-25","cited_by_count":1,"area":null},{"id":"2605.26099","title":"Do Language Models Need Sleep? Offline Recurrence for Improved Online Inference","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2609.34218","title":"Loop Dropout: Regularizing Shared Updates in Looped Language Models","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.34538","title":"Shallow Queries, Mature Values: Depth-Asynchronous Self-Speculation for Looped Transformers","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":"2609.36636","title":"What Makes Recurrence Effective in Looped Language Models?","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.37631","title":"Procedural Core: A Compact Recurrent Initialization for Vision Transformers","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.39277","title":"A Tilted Bowl Is Not a Slippery Slope: Compressing Looped Models","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.05265","title":"Loopy: Low-Bit Quantization Framework for Looped Language Models","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.14252","title":"Adaptive-Depth Is a Finishing Stage","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}