{"total":69,"rows":[{"id":"2505.19590","title":"Learning to Reason without External Rewards","created":"2025-05-26","cited_by_count":210,"area":"lr"},{"id":"2601.07372","title":"Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models","created":"2026-01-12","cited_by_count":86,"area":"moe"},{"id":"2509.21880","title":"No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping","created":"2025-09-26","cited_by_count":50,"area":"lr"},{"id":"2506.05233","title":"MesaNet: Sequence Modeling by Locally Optimal Test-Time Training","created":"2025-06-05","cited_by_count":47,"area":null},{"id":"2509.24663","title":"InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation","created":"2025-09-29","cited_by_count":32,"area":null},{"id":"2502.20082","title":"LongRoPE2: Near-Lossless LLM Context Window Scaling","created":"2025-02-27","cited_by_count":24,"area":null},{"id":"2412.17483","title":"A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression","created":"2024-12-23","cited_by_count":17,"area":"inference"},{"id":"2411.13476","title":"When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training","created":"2024-11-20","cited_by_count":15,"area":null},{"id":"2505.24187","title":"Beyond Exponential Decay: Rethinking Error Accumulation in Large Language Models","created":"2025-05-30","cited_by_count":12,"area":"lr"},{"id":"2509.24552","title":"Short window attention enables long-term memorization","created":"2025-09-29","cited_by_count":11,"area":null},{"id":"2511.21016","title":"Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression","created":"2025-11-26","cited_by_count":11,"area":null},{"id":"2601.07220","title":"The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?","created":"2026-01-12","cited_by_count":11,"area":null},{"id":"2505.11166","title":"SoLoPO: Unlocking Long-Context Capabilities in LLMs via Short-to-Long Preference Optimization","created":"2025-05-16","cited_by_count":10,"area":"alignment"},{"id":"2601.22950","title":"Perplexity Cannot Always Tell Right from Wrong","created":"2026-01-30","cited_by_count":9,"area":null},{"id":"2504.12491","title":"Can Pre-training Indicators Reliably Predict Fine-tuning Outcomes of LLMs?","created":"2025-04-16","cited_by_count":7,"area":null},{"id":"2505.11254","title":"Delta Attention: Fast and Accurate Sparse Attention Inference by Delta Correction","created":"2025-05-16","cited_by_count":7,"area":null},{"id":"2506.02921","title":"A Controllable Examination for Long-Context Language Models","created":"2025-06-03","cited_by_count":7,"area":null},{"id":"2508.09848","title":"PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts","created":"2025-08-13","cited_by_count":7,"area":"lr"},{"id":"2606.20068","title":"Process-Verified Reinforcement Learning for Theorem Proving via Lean","created":"2026-06-18","cited_by_count":7,"area":"lr"},{"id":"2504.21463","title":"RWKV-X: A Linear Complexity Hybrid Language Model","created":"2025-04-30","cited_by_count":6,"area":null},{"id":"2505.23473","title":"EVOREFUSE: Evolutionary Prompt Optimization for Evaluation and Mitigation of LLM Over-Refusal to Pseudo-Malicious Instructions","created":"2025-05-29","cited_by_count":6,"area":"alignment"},{"id":"2510.10023","title":"Skill-Targeted Adaptive Training","created":"2025-10-11","cited_by_count":6,"area":null},{"id":"2602.02555","title":"Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards","created":"2026-01-30","cited_by_count":6,"area":"lr"},{"id":"2605.19577","title":"GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment","created":"2026-05-19","cited_by_count":6,"area":null},{"id":"2510.02330","title":"EntropyLong: Effective Long-Context Training via Predictive Uncertainty","created":"2025-09-26","cited_by_count":5,"area":null},{"id":"2510.05862","title":"Revisiting Long-context Modeling from Context Denoising Perspective","created":"2025-10-07","cited_by_count":5,"area":null},{"id":"2511.02833","title":"In Good GRACEs: Principled Teacher Selection for Knowledge Distillation","created":"2025-11-04","cited_by_count":5,"area":"distill"},{"id":"2606.06479","title":"Pretraining Recurrent Networks without Recurrence","created":"2026-06-04","cited_by_count":5,"area":null},{"id":"2505.00065","title":"ConSens: Assessing context grounding in open-book question answering","created":"2025-04-30","cited_by_count":4,"area":null},{"id":"2506.11769","title":"Long-Short Alignment for Effective Long-Context Modeling in LLMs","created":"2025-06-13","cited_by_count":4,"area":null},{"id":"2605.10544","title":"Where Does Long-Context Supervision Actually Go? Effective-Context Exposure Balancing","created":"2026-05-11","cited_by_count":4,"area":null},{"id":"2511.00505","title":"Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledge","created":"2025-11-01","cited_by_count":3,"area":"rag"},{"id":"2511.05313","title":"Controllably Efficient Language Models","created":"2025-11-07","cited_by_count":3,"area":null},{"id":"2602.09276","title":"Effective Reasoning Chains Reduce Intrinsic Dimensionality","created":"2026-02-09","cited_by_count":3,"area":"lr"},{"id":"2603.02876","title":"Eval4Sim: An Evaluation Framework for Persona Simulation","created":"2026-03-03","cited_by_count":3,"area":null},{"id":"2603.06595","title":"Rethinking Personalization in Large Language Models at the Token Level","created":"2026-02-04","cited_by_count":3,"area":"lr"},{"id":"2603.18426","title":"Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression","created":"2026-03-19","cited_by_count":3,"area":null},{"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":"2507.07050","title":"Discrete Diffusion Models for Language Generation","created":"2025-07-02","cited_by_count":2,"area":"diffusion"},{"id":"2509.19628","title":"Multimodal Language Models with Modality-Specific Experts for Financial Forecasting from Interleaved Sequences of Text and Time Series","created":"2025-09-23","cited_by_count":2,"area":"mllm"},{"id":"2510.00034","title":"Review of Hallucination Understanding in Large Language and Vision Models","created":"2025-09-26","cited_by_count":2,"area":null},{"id":"2605.14037","title":"Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility","created":"2026-05-13","cited_by_count":2,"area":"lr"},{"id":"2607.07678","title":"How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization","created":"2026-07-08","cited_by_count":2,"area":null},{"id":"2509.25562","title":"IRIS: Intrinsic Reward Image Synthesis","created":"2025-09-29","cited_by_count":1,"area":"alignment"},{"id":"2512.08082","title":"Short-Context Dominance: How Much Local Context Natural Language Actually Needs?","created":"2025-12-08","cited_by_count":1,"area":null},{"id":"2601.20255","title":"HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench","created":"2026-01-28","cited_by_count":1,"area":null},{"id":"2602.11212","title":"Towards Compressive and Scalable Recurrent Memory","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2602.11871","title":"DMAP: A Distribution Map for Text","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2604.05324","title":"A Theoretical Framework for Statistical Evaluability of Generative Models","created":"2026-04-07","cited_by_count":1,"area":null},{"id":"2605.13684","title":"Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale","created":"2026-05-13","cited_by_count":1,"area":null},{"id":"2605.30628","title":"The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliability","created":"2026-05-28","cited_by_count":1,"area":null},{"id":"2607.00908","title":"Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization","created":"2026-07-01","cited_by_count":1,"area":null},{"id":"2607.19345","title":"Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning","created":"2026-07-21","cited_by_count":1,"area":"lr"},{"id":"2609.26368","title":"HySparse2: Hybrid Sparse Attention with Two-Level KV Sharing","created":"2026-09-22","cited_by_count":1,"area":"inference"},{"id":"2510.04130","title":"On the Limitations and Capabilities of Position Embeddings for Length Generalization","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2602.04099","title":"Rethinking Perplexity: Revealing the Impact of Input Length on Perplexity Evaluation in LLMs","created":"2026-02-04","cited_by_count":0,"area":"lr"},{"id":"2604.07809","title":"PolicyLong: Towards On-Policy Context Extension","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2604.18311","title":"On the Importance and Evaluation of Narrativity in Natural Language AI Explanations","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2605.00826","title":"Understanding the Performance Plateau in Text-to-Video Retrieval: A Comprehensive Empirical and Linguistic Analysis","created":"2026-03-07","cited_by_count":0,"area":null},{"id":"2605.24667","title":"When Mean CE Fails: Median CE Can Better Track Language Model Quality","created":"2026-05-23","cited_by_count":0,"area":null},{"id":"2607.21752","title":"Parameter-free Adaptive Sparse Attention via Compression-Based Content Selection","created":"2026-07-23","cited_by_count":0,"area":null},{"id":"2608.10296","title":"Cracks in the Foundation: Seemingly Minor Architectural Choices Impact Long Context Extension","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2609.09157","title":"Learning Length-Extrapolatable Recurrent Models","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2609.20734","title":"On-Demand Attention: Language Models Know When to Recall","created":"2026-09-17","cited_by_count":0,"area":"agents"},{"id":"2609.32991","title":"What Should Data Teach? Moving Bottlenecks Across Circuit, Store, and Use","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.33405","title":"Decoupling Token Roles in Autoregressive Pretraining","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.36569","title":"From Checkpoint Variation to Selection Gains in Supervised Fine-Tuning","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.10114","title":"Mechanics of Long-Context Hybrid Models Part 1.1: From Hybrid Attention to Hybrid Position","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.17191","title":"FreeSolo: Linear-time Sparse Attention with Zero-Shot Length Extrapolation","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}