{"total":34,"rows":[{"id":"2412.06769","title":"Training Large Language Models to Reason in a Continuous Latent Space","created":"2024-12-09","cited_by_count":797,"area":"lr"},{"id":"2508.06471","title":"GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models","created":"2025-08-08","cited_by_count":467,"area":"lr"},{"id":"2410.14157","title":"Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning","created":"2024-10-18","cited_by_count":135,"area":"lr"},{"id":"2505.07608","title":"MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining","created":"2025-05-12","cited_by_count":121,"area":"lr"},{"id":"2511.08923","title":"TiDAR: Think in Diffusion, Talk in Autoregression","created":"2025-11-12","cited_by_count":37,"area":"lr"},{"id":"2502.08524","title":"LLM Pretraining with Continuous Concepts","created":"2025-02-12","cited_by_count":36,"area":"lr"},{"id":"2505.07538","title":"Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning","created":"2025-05-12","cited_by_count":33,"area":"lr"},{"id":"2506.08889","title":"SeerAttention-R: Sparse Attention Adaptation for Long Reasoning","created":"2025-06-10","cited_by_count":30,"area":"lr"},{"id":"2506.11029","title":"Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model","created":"2025-05-20","cited_by_count":24,"area":"lr"},{"id":"2409.08561","title":"Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding","created":"2024-09-13","cited_by_count":19,"area":"lr"},{"id":"2510.24051","title":"Pie: A Programmable Serving System for Emerging LLM Applications","created":"2025-10-28","cited_by_count":14,"area":"lr"},{"id":"2503.07154","title":"Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms","created":"2025-03-10","cited_by_count":12,"area":"lr"},{"id":"2602.03769","title":"Reasoning with Latent Tokens in Diffusion Language Models","created":"2026-02-03","cited_by_count":9,"area":"lr"},{"id":"2509.01649","title":"Distilled Pretraining: A modern lens of Data, In-Context Learning and Test-Time Scaling","created":"2025-09-01","cited_by_count":7,"area":"lr"},{"id":"2502.06282","title":"Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE","created":"2025-02-10","cited_by_count":6,"area":"lr"},{"id":"2601.21358","title":"Latent Chain-of-Thought as Planning: Decoupling Reasoning from Verbalization","created":"2026-01-29","cited_by_count":6,"area":"lr"},{"id":"2602.01469","title":"P-EAGLE: Parallel-Drafting EAGLE with Scalable Training","created":"2026-02-01","cited_by_count":6,"area":"lr"},{"id":"2606.15079","title":"Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale","created":"2026-06-13","cited_by_count":6,"area":"lr"},{"id":"2603.15051","title":"Thinking in Latents: Adaptive Anchor Refinement for Implicit Reasoning in LLMs","created":"2026-03-16","cited_by_count":5,"area":"lr"},{"id":"2505.07858","title":"Scaling Laws for Speculative Decoding","created":"2025-05-08","cited_by_count":4,"area":"lr"},{"id":"2508.16745","title":"Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling","created":"2025-08-22","cited_by_count":4,"area":"lr"},{"id":"2510.09426","title":"KORMo: Korean Open Reasoning Model for Everyone","created":"2025-10-10","cited_by_count":4,"area":"lr"},{"id":"2503.11486","title":"A Review of DeepSeek Models' Key Innovative Techniques","created":"2025-03-14","cited_by_count":3,"area":"lr"},{"id":"2605.12460","title":"Multi-Stream LLMs: Unblocking Language Models with Parallel Streams of Thoughts, Inputs and Outputs","created":"2026-05-12","cited_by_count":3,"area":"lr"},{"id":"2609.07108","title":"Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-Training","created":"2026-09-07","cited_by_count":3,"area":"lr"},{"id":"2512.10430","title":"T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground","created":"2025-12-11","cited_by_count":2,"area":"lr"},{"id":"2605.25745","title":"Selective Latent Thinking: Adaptive Compression of LLM Reasoning Chains","created":"2026-05-25","cited_by_count":2,"area":"lr"},{"id":"2605.28184","title":"Joint Training of Multi-Token Prediction in Reinforcement Learning via Optimal Coefficient Calibration","created":"2026-05-27","cited_by_count":2,"area":"lr"},{"id":"2406.12620","title":"What Makes Two Language Models Think Alike?","created":"2024-06-18","cited_by_count":1,"area":"lr"},{"id":"2603.20219","title":"Thinking into the Future: Latent Lookahead Training for Transformers","created":"2026-03-03","cited_by_count":1,"area":"lr"},{"id":"2604.14889","title":"MemoSight: Unifying Context Compression and Multi Token Prediction for Reasoning Acceleration","created":"2026-04-16","cited_by_count":1,"area":"lr"},{"id":"2606.24937","title":"The Hitchhiker's Guide to Agentic AI: From Foundations to Systems","created":"2026-06-22","cited_by_count":0,"area":"lr"},{"id":"2609.15296","title":"Reason What Matters: Retrieval-Grounded Reasoning for Universal Multimodal Embeddings","created":"2026-09-14","cited_by_count":0,"area":"lr"},{"id":"2609.25442","title":"WeightBridge: An Efficient Weight Transfer Library for Reinforcement Learning","created":"2026-09-21","cited_by_count":0,"area":"lr"}],"next_cursor":null}