{"total":35,"rows":[{"id":"2411.02359","title":"DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution","created":"2024-11-04","cited_by_count":117,"area":"lr"},{"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":"2502.13842","title":"Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking","created":"2025-02-19","cited_by_count":57,"area":"lr"},{"id":"2511.07384","title":"Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence","created":"2025-11-10","cited_by_count":47,"area":"lr"},{"id":"2405.18711","title":"Calibrating Reasoning in Language Models with Internal Consistency","created":"2024-05-29","cited_by_count":28,"area":"lr"},{"id":"2505.18962","title":"System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts","created":"2025-05-25","cited_by_count":27,"area":"lr"},{"id":"2510.07358","title":"Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughts","created":"2025-10-08","cited_by_count":24,"area":"lr"},{"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":"2509.23314","title":"Two-Scale Latent Dynamics for Recurrent-Depth Transformers","created":"2025-09-27","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":"2503.10905","title":"Learning to Inference Adaptively for Multimodal Large Language Models","created":"2025-03-13","cited_by_count":12,"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":"2601.18091","title":"From LLMs to LRMs: Rethinking Pruning for Reasoning-Centric Models","created":"2026-01-26","cited_by_count":10,"area":"lr"},{"id":"2605.06908","title":"Same Signal, Opposite Meaning: Direction-Informed Adaptive Learning for LLM Agents","created":"2026-05-07","cited_by_count":8,"area":"lr"},{"id":"2506.03700","title":"AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism","created":"2025-06-04","cited_by_count":7,"area":"lr"},{"id":"2510.00219","title":"Thoughtbubbles: an Unsupervised Method for Parallel Thinking in Latent Space","created":"2025-09-30","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":"2603.02023","title":"PonderLM-3: Adaptive Token-Wise Pondering with Differentiable Masking","created":"2026-03-02","cited_by_count":4,"area":"lr"},{"id":"2510.13879","title":"Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production","created":"2025-10-13","cited_by_count":2,"area":"lr"},{"id":"2606.03092","title":"The Shadow Price of Reasoning: Economic Perspective on Optimal Budget Allocation for LLMs","created":"2026-06-02","cited_by_count":2,"area":"lr"},{"id":"2411.15281","title":"ElastiFormer: Learned Redundancy Reduction in Transformer via Self-Distillation","created":"2024-11-22","cited_by_count":1,"area":"lr"},{"id":"2605.28919","title":"CosmicFish-HRM: Adaptive Reasoning via Hierarchical Recurrent Mechanisms in Compact Language Models","created":"2026-05-27","cited_by_count":1,"area":"lr"},{"id":"2606.01838","title":"LayerRoute: Input-Conditioned Adaptive Layer Skipping via LoRA Fine-Tuning for Agentic Language Models","created":"2026-06-01","cited_by_count":1,"area":"lr"},{"id":"2606.02842","title":"Spectral-Progressive Thought Flow for Lightweight Multimodal Reasoning","created":"2026-06-01","cited_by_count":1,"area":"lr"},{"id":"2609.27373","title":"Attention Routing Stabilizes Early: Working-Set Inference for Recurrent Language Models","created":"2026-09-23","cited_by_count":1,"area":"lr"},{"id":"2609.35748","title":"Improving Test-Time Scaling with Adaptive Looped Transformers","created":"2026-09-28","cited_by_count":1,"area":"lr"},{"id":"2510.14211","title":"LiteStage: Latency-aware Layer Skipping for Multi-stage Reasoning","created":"2025-10-16","cited_by_count":0,"area":"lr"},{"id":"2607.08775","title":"HALO: Hybrid Adaptive Latent Reasoning for Language Models","created":"2026-05-03","cited_by_count":0,"area":"lr"},{"id":"2607.09600","title":"Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation","created":"2026-07-10","cited_by_count":0,"area":"lr"},{"id":"2607.21291","title":"Adaptive Depth Sparse Framework: Similarity-Driven Resource Allocation for Pre-Trained LLMs","created":"2026-07-23","cited_by_count":0,"area":"lr"},{"id":"2608.06014","title":"Iterate or Widen? When Test-Time Refinement Helps LiDAR Scene Completion: A Controlled Study of Evidence Geometry, Training Coverage, and Compute","created":"2026-08-06","cited_by_count":0,"area":"lr"},{"id":"2610.04751","title":"Dynamic Routing as a New Dimension for Test-time Versatility of LLMs","created":"2026-10-03","cited_by_count":0,"area":"lr"},{"id":"2610.06949","title":"AdaLoop: Adaptive-Depth Latent Reasoning for Audio Language Models","created":"2026-10-03","cited_by_count":0,"area":"lr"},{"id":"2610.17679","title":"Dynamic K-Head: Difficulty-Supervised Budget Selection for Continuous Latent Reasoning","created":"2026-10-13","cited_by_count":0,"area":"lr"}],"next_cursor":null}