{"total":165,"rows":[{"id":"2602.11451","title":"LoopFormer: Elastic-Depth Looped Transformers for Latent Reasoning via Shortcut Modulation","created":"2026-02-11","cited_by_count":61,"area":"lr"},{"id":"2604.12946","title":"Parcae: Scaling Laws For Stable Looped Language Models","created":"2026-04-14","cited_by_count":55,"area":null},{"id":"2604.02029","title":"The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook","created":"2026-04-02","cited_by_count":43,"area":null},{"id":"2604.07822","title":"Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers","created":"2026-04-09","cited_by_count":37,"area":"lr"},{"id":"2604.11791","title":"A Mechanistic Analysis of Looped Reasoning Language Models","created":"2026-04-13","cited_by_count":33,"area":"lr"},{"id":"2606.18206","title":"Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers","created":"2026-06-16","cited_by_count":28,"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":"2605.26733","title":"Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models","created":"2026-05-26","cited_by_count":21,"area":"lr"},{"id":"2602.07845","title":"Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of Vision-Language-Action Models via Latent Iterative Reasoning","created":"2026-02-08","cited_by_count":18,"area":"lr"},{"id":"2605.21488","title":"Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning","created":"2026-05-20","cited_by_count":18,"area":"lr"},{"id":"2609.01343","title":"SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers","created":"2026-09-01","cited_by_count":18,"area":"moe"},{"id":"2511.08577","title":"Think-at-Hard: Dynamic Looped Transformers for Improved Reasoning","created":"2025-11-11","cited_by_count":16,"area":"lr"},{"id":"2602.02156","title":"LoopViT: Scaling Visual ARC with Looped Transformers","created":"2026-02-02","cited_by_count":15,"area":"lr"},{"id":"2605.11011","title":"LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models","created":"2026-05-10","cited_by_count":15,"area":"lr"},{"id":"2605.23872","title":"Training-Free Looped Transformers","created":"2026-05-22","cited_by_count":15,"area":"moe"},{"id":"2605.07721","title":"Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models","created":"2026-05-08","cited_by_count":14,"area":"inference"},{"id":"2605.12466","title":"Solve the Loop: Attractor Models for Language and Reasoning","created":"2026-05-12","cited_by_count":13,"area":"lr"},{"id":"2606.31779","title":"Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers","created":"2026-06-30","cited_by_count":13,"area":"lr"},{"id":"2601.21582","title":"Depth-Recurrent Attention Mixtures: Giving Latent Reasoning the Attention it Deserves","created":"2026-01-29","cited_by_count":12,"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":"2604.21254","title":"Hyperloop Transformers","created":"2026-04-23","cited_by_count":12,"area":null},{"id":"2607.20519","title":"Adaptive Depth in Looped Transformers: Diagnosing Learned Halting Gates and Trajectory Readouts","created":"2026-07-08","cited_by_count":12,"area":null},{"id":"2602.08783","title":"Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure","created":"2026-02-09","cited_by_count":11,"area":"lr"},{"id":"2603.08391","title":"Adaptive Loops and Memory in Transformers: Think Harder or Know More?","created":"2026-03-09","cited_by_count":11,"area":"lr"},{"id":"2605.09165","title":"Sparse Layers are Critical to Scaling Looped Language Models","created":"2026-05-09","cited_by_count":11,"area":"moe"},{"id":"2607.22083","title":"Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Model","created":"2026-07-24","cited_by_count":11,"area":"agents"},{"id":"2601.22954","title":"Residual Context Diffusion Language Models","created":"2026-01-30","cited_by_count":10,"area":"diffusion"},{"id":"2603.01914","title":"AdaPonderLM: Gated Pondering Language Models with Token-Wise Adaptive Depth","created":"2026-03-02","cited_by_count":10,"area":"lr"},{"id":"2604.25917","title":"Recursive Multi-Agent Systems","created":"2026-04-28","cited_by_count":10,"area":"agents"},{"id":"2606.04438","title":"LoopMoE: Unifying Iterative Computation with Mixture-of-Experts for Language Modeling","created":"2026-06-03","cited_by_count":10,"area":"moe"},{"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":"2604.04902","title":"Are Latent Reasoning Models Easily Interpretable?","created":"2026-04-06","cited_by_count":9,"area":"lr"},{"id":"2605.20613","title":"HRM-Text: Efficient Pretraining Beyond Scaling","created":"2026-05-20","cited_by_count":9,"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":"2602.10520","title":"Prioritize the Process, Not Just the Outcome: Rewarding Latent Thought Trajectories Improves Reasoning in Looped Language Models","created":"2026-02-11","cited_by_count":8,"area":"lr"},{"id":"2602.11683","title":"ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces","created":"2026-02-12","cited_by_count":8,"area":"lr"},{"id":"2607.16051","title":"Loop the Loopies!","created":"2026-07-17","cited_by_count":8,"area":"moe"},{"id":"2602.01705","title":"LaDi-RL: Latent Diffusion Reasoning Prevents Entropy Collapse in Reinforcement Learning","created":"2026-02-02","cited_by_count":7,"area":"lr"},{"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":"2607.13491","title":"DeepLoop: Depth Scaling for Looped Transformers","created":"2026-07-15","cited_by_count":7,"area":null},{"id":"2607.15178","title":"T^2MLR: Transformer with Temporal Middle-Layer Recurrence","created":"2026-07-16","cited_by_count":7,"area":null},{"id":"2607.15456","title":"Looped Latent Attention: Cross-Loop KV Compression for Looped Transformers","created":"2026-07-16","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":"2603.17837","title":"The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning","created":"2026-03-18","cited_by_count":6,"area":null},{"id":"2605.16343","title":"LoopQ: Quantization for Looped Language Models","created":"2026-05-08","cited_by_count":6,"area":null},{"id":"2605.18797","title":"Simply Stabilizing the Loop via Fully Looped Transformer","created":"2026-05-11","cited_by_count":6,"area":null},{"id":"2608.09444","title":"Depth-adaptive Inference of Looped Language Models via Continuous Depth Batching","created":"2026-08-10","cited_by_count":6,"area":null},{"id":"2609.01924","title":"Looped Transformers under the Jacobian Lens: Does the Global Workspace Survive Recurrence?","created":"2026-09-01","cited_by_count":6,"area":null},{"id":"2602.09080","title":"Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models","created":"2026-02-09","cited_by_count":5,"area":"mllm"},{"id":"2602.10097","title":"Step-resolved data attribution for looped transformers","created":"2026-02-10","cited_by_count":5,"area":null},{"id":"2606.24898","title":"Dense Supervision Is Not Enough: The Readout Blind Spot in Looped Language Models","created":"2026-06-12","cited_by_count":5,"area":null},{"id":"2608.08888","title":"Full-bandwidth transformer","created":"2026-08-09","cited_by_count":5,"area":null},{"id":"2602.04925","title":"Internalizing LLM Reasoning via Discovery and Replay of Latent Actions","created":"2026-02-04","cited_by_count":4,"area":"lr"},{"id":"2602.08220","title":"Pretraining with Token-Level Adaptive Latent Chain-of-Thought","created":"2026-02-09","cited_by_count":4,"area":"lr"},{"id":"2602.10229","title":"Latent Thoughts Tuning: Bridging Context and Reasoning with Fused Information in Latent Tokens","created":"2026-02-10","cited_by_count":4,"area":"lr"},{"id":"2604.17892","title":"LEPO: Latent Reasoning Policy Optimization for Large Language Models","created":"2026-04-20","cited_by_count":4,"area":"lr"},{"id":"2606.06447","title":"Latent Reasoning with Normalizing Flows","created":"2026-06-04","cited_by_count":4,"area":"lr"},{"id":"2606.16825","title":"Tying the Loop -- Tied Expert Layers in Mixture-of-Experts Language Models","created":"2026-06-15","cited_by_count":4,"area":"moe"},{"id":"2606.29983","title":"Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping","created":"2026-06-29","cited_by_count":4,"area":null},{"id":"2607.00341","title":"DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning","created":"2026-07-01","cited_by_count":4,"area":"lr"},{"id":"2607.08186","title":"Hidden Decoding at Scale: Latent Computation Scaling for Large Language Models","created":"2026-07-09","cited_by_count":4,"area":null},{"id":"2608.15062","title":"Gated Recurrent Transformers: Expressive Depth through Recurrent Modulation","created":"2026-08-15","cited_by_count":4,"area":null},{"id":"2608.18230","title":"Allocating Recurrent Compute in Looped Language Models","created":"2026-08-18","cited_by_count":4,"area":"lr"},{"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":"2603.23998","title":"Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping","created":"2026-03-25","cited_by_count":3,"area":null},{"id":"2604.17121","title":"The Topological Trouble With Transformers","created":"2026-04-18","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":"2605.16638","title":"TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens","created":"2026-05-15","cited_by_count":3,"area":"lr"},{"id":"2606.01243","title":"Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention","created":"2026-05-31","cited_by_count":3,"area":"lr"},{"id":"2606.18208","title":"Looped World Models","created":"2026-06-16","cited_by_count":3,"area":"wm"},{"id":"2606.20075","title":"What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis","created":"2026-06-18","cited_by_count":3,"area":"lr"},{"id":"2608.03624","title":"LoopMTP: A looped transformer guided by latent multi-token prediction","created":"2026-08-04","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":"2605.06546","title":"Efficient Pre-Training with Token Superposition","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2605.30202","title":"A Dual-Path Architecture for Scaling Compute and Capacity in LLMs","created":"2026-05-28","cited_by_count":2,"area":null},{"id":"2605.30757","title":"Chain-of-Thought and Compressed Looped Transformers: A Memory-Budget Separation","created":"2026-05-29","cited_by_count":2,"area":"lr"},{"id":"2605.30813","title":"Incremental BPE Tokenization","created":"2026-05-29","cited_by_count":2,"area":null},{"id":"2606.29712","title":"Why Struggle with Continuous Latents? Interpretable Discrete Latent Reasoning via Rendered Compression","created":"2026-06-29","cited_by_count":2,"area":"lr"},{"id":"2607.10128","title":"Energy-guided Recursive Model","created":"2026-07-11","cited_by_count":2,"area":"lr"},{"id":"2607.14427","title":"Per-Token Fixed-Point Convergence in Depth-Recurrent Transformers","created":"2026-07-15","cited_by_count":2,"area":null},{"id":"2607.16972","title":"Training Continuous Chain of Thought Models: A Tale of Two Regimes","created":"2026-07-18","cited_by_count":2,"area":"lr"},{"id":"2608.18171","title":"Looped Language Models Improve Compositional Tool Calling","created":"2026-08-17","cited_by_count":2,"area":"agents"},{"id":"2609.15160","title":"T-LoopFormer: Token-Level Elastic-Depth Looped Transformers for Latent Reasoning with Dynamic Routing","created":"2026-09-14","cited_by_count":2,"area":"inference"},{"id":"2609.17184","title":"LoopSpec: Pipelined Self-Speculative Decoding for Looped Transformers","created":"2026-09-15","cited_by_count":2,"area":"inference"},{"id":"2609.29812","title":"FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates","created":"2026-09-24","cited_by_count":2,"area":"inference"},{"id":"2511.18105","title":"AdaPerceiver: Transformers with Adaptive Width, Depth, and Tokens","created":"2025-11-22","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":"2512.22955","title":"Diversity or Precision? A Deep Dive into Next Token Prediction","created":"2025-12-28","cited_by_count":1,"area":"lr"},{"id":"2602.08100","title":"Emergent Search and Backtracking in Latent Reasoning Models","created":"2026-02-08","cited_by_count":1,"area":"lr"},{"id":"2603.09221","title":"Beyond Test-Time Memory: State-Space Optimal Control for LLM Reasoning","created":"2026-03-10","cited_by_count":1,"area":"lr"},{"id":"2604.17224","title":"LASER: Low-Rank Activation SVD for Efficient Recursion","created":"2026-04-19","cited_by_count":1,"area":"lr"},{"id":"2604.18637","title":"NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence","created":"2026-04-19","cited_by_count":1,"area":null},{"id":"2605.09630","title":"Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models","created":"2026-05-10","cited_by_count":1,"area":"inference"},{"id":"2605.09948","title":"LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models","created":"2026-05-11","cited_by_count":1,"area":"vla"},{"id":"2605.26106","title":"Looped Diffusion Language Models","created":"2026-05-25","cited_by_count":1,"area":"diffusion"},{"id":"2607.10110","title":"CHASE: Cache-Hole-Adapted Skip Exit for Looped State-Space Language Models","created":"2026-07-11","cited_by_count":1,"area":"lr"},{"id":"2608.24136","title":"Steering Recurrent Reasoners at Inference Time with Readout Feedback","created":"2026-08-25","cited_by_count":1,"area":null},{"id":"2609.16665","title":"Right Direction, Wrong Step: Geometric Analysis of Finite-Step Failure in Looped Transformers","created":"2026-09-15","cited_by_count":1,"area":"lr"},{"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}],"next_cursor":"100"}