{"total":160,"rows":[{"id":"2508.15487","title":"Dream 7B: Diffusion Large Language Models","created":"2025-08-21","cited_by_count":544,"area":"diffusion"},{"id":"2410.17891","title":"Scaling Diffusion Language Models via Adaptation from Autoregressive Models","created":"2024-10-23","cited_by_count":274,"area":"diffusion"},{"id":"2501.00309","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","created":"2024-12-31","cited_by_count":172,"area":"rag"},{"id":"2404.03683","title":"Stream of Search (SoS): Learning to Search in Language","created":"2024-04-01","cited_by_count":145,"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":"2510.06303","title":"SDAR: A Synergistic Diffusion-AutoRegression Paradigm for Scalable Sequence Generation","created":"2025-10-07","cited_by_count":129,"area":"diffusion"},{"id":"2406.14532","title":"RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold","created":"2024-06-20","cited_by_count":99,"area":"lr"},{"id":"2509.01142","title":"Dream-Coder 7B: An Open Diffusion Language Model for Code","created":"2025-09-01","cited_by_count":81,"area":"diffusion"},{"id":"2405.15943","title":"Transformers represent belief state geometry in their residual stream","created":"2024-05-24","cited_by_count":77,"area":null},{"id":"2506.13759","title":"Discrete Diffusion in Large Language and Multimodal Models: A Survey","created":"2025-06-16","cited_by_count":61,"area":"mllm"},{"id":"2404.09516","title":"State Space Model for New-Generation Network Alternative to Transformers: A Survey","created":"2024-04-15","cited_by_count":59,"area":null},{"id":"2501.19309","title":"Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment","created":"2025-01-31","cited_by_count":59,"area":"alignment"},{"id":"2505.20147","title":"FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities","created":"2025-05-26","cited_by_count":46,"area":"mllm"},{"id":"2505.19683","title":"Large Language Models for Planning: A Comprehensive and Systematic Survey","created":"2025-05-26","cited_by_count":40,"area":"lr"},{"id":"2506.14429","title":"LongLLaDA: Unlocking Long Context Capabilities in Diffusion LLMs","created":"2025-06-17","cited_by_count":40,"area":"diffusion"},{"id":"2409.10502","title":"Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles","created":"2024-09-16","cited_by_count":38,"area":"lr"},{"id":"2501.15602","title":"Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning","created":"2025-01-26","cited_by_count":36,"area":"lr"},{"id":"2502.08524","title":"LLM Pretraining with Continuous Concepts","created":"2025-02-12","cited_by_count":36,"area":"lr"},{"id":"2407.14414","title":"System-1.x: Learning to Balance Fast and Slow Planning with Language Models","created":"2024-07-19","cited_by_count":35,"area":null},{"id":"2502.14565","title":"ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verification","created":"2025-02-20","cited_by_count":35,"area":"lr"},{"id":"2408.11865","title":"How Susceptible are LLMs to Influence in Prompts?","created":"2024-08-17","cited_by_count":33,"area":null},{"id":"2502.01612","title":"Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges","created":"2025-02-03","cited_by_count":31,"area":"synth"},{"id":"2511.05963","title":"Next-Latent Prediction Transformers Learn Compact World Models","created":"2025-11-08","cited_by_count":31,"area":"wm"},{"id":"2502.13917","title":"TESS 2: A Large-Scale Generalist Diffusion Language Model","created":"2025-02-19","cited_by_count":30,"area":"diffusion"},{"id":"2504.15266","title":"Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction","created":"2025-04-21","cited_by_count":29,"area":null},{"id":"2510.04573","title":"LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning","created":"2025-10-06","cited_by_count":29,"area":"lr"},{"id":"2411.16679","title":"Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?","created":"2024-11-25","cited_by_count":28,"area":"lr"},{"id":"2406.12830","title":"What Are the Odds? Language Models Are Capable of Probabilistic Reasoning","created":"2024-06-18","cited_by_count":26,"area":"lr"},{"id":"2410.17195","title":"Non-myopic Generation of Language Models for Reasoning and Planning","created":"2024-10-22","cited_by_count":26,"area":"lr"},{"id":"2511.16660","title":"Cognitive Foundations for Reasoning and Their Manifestation in LLMs","created":"2025-11-20","cited_by_count":26,"area":"lr"},{"id":"2412.05152","title":"Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation","created":"2024-12-06","cited_by_count":25,"area":null},{"id":"2407.13934","title":"Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models","created":"2024-06-01","cited_by_count":22,"area":null},{"id":"2409.01369","title":"Imitating Language via Scalable Inverse Reinforcement Learning","created":"2024-09-02","cited_by_count":21,"area":"offlinerl"},{"id":"2506.00290","title":"DLM-One: Diffusion Language Models for One-Step Sequence Generation","created":"2025-05-30","cited_by_count":20,"area":"diffusion"},{"id":"2412.04703","title":"Transformers Struggle to Learn to Search","created":"2024-12-06","cited_by_count":19,"area":null},{"id":"2502.06258","title":"Emergent Response Planning in LLMs","created":"2025-02-10","cited_by_count":19,"area":"lr"},{"id":"2512.22615","title":"Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone","created":"2025-12-27","cited_by_count":18,"area":"mllm"},{"id":"2405.15821","title":"Reinforcing Language Agents via Policy Optimization with Action Decomposition","created":"2024-05-23","cited_by_count":16,"area":"agents"},{"id":"2505.10518","title":"Multi-Token Prediction Needs Registers","created":"2025-05-15","cited_by_count":16,"area":null},{"id":"2405.06680","title":"Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning","created":"2024-05-05","cited_by_count":15,"area":"lr"},{"id":"2502.20548","title":"$Q\\sharp$: Provably Optimal Distributional RL for LLM Post-Training","created":"2025-02-27","cited_by_count":15,"area":"lr"},{"id":"2510.14751","title":"Beyond Multi-Token Prediction: Pretraining LLMs with Future Summaries","created":"2025-10-16","cited_by_count":15,"area":null},{"id":"2410.17241","title":"Frontiers in Intelligent Colonoscopy","created":"2024-10-22","cited_by_count":14,"area":null},{"id":"2502.11075","title":"Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models","created":"2025-02-16","cited_by_count":14,"area":null},{"id":"2504.03930","title":"Have Large Language Models Learned to Reason? A Characterization via 3-SAT Phase Transition","created":"2025-04-04","cited_by_count":14,"area":"lr"},{"id":"2505.12629","title":"Enhancing Latent Computation in Transformers with Latent Tokens","created":"2025-05-19","cited_by_count":14,"area":null},{"id":"2605.06548","title":"Continuous Latent Diffusion Language Model","created":"2026-05-07","cited_by_count":14,"area":"diffusion"},{"id":"2411.02083","title":"Regress, Don't Guess -- A Regression-like Loss on Number Tokens for Language Models","created":"2024-11-04","cited_by_count":13,"area":null},{"id":"2502.12465","title":"Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification","created":"2025-02-18","cited_by_count":13,"area":"offlinerl"},{"id":"2410.23506","title":"The Belief State Transformer","created":"2024-10-30","cited_by_count":12,"area":null},{"id":"2412.05117","title":"Transformers Can Navigate Mazes With Multi-Step Prediction","created":"2024-12-06","cited_by_count":11,"area":null},{"id":"2412.10675","title":"Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation","created":"2024-12-14","cited_by_count":11,"area":null},{"id":"2502.19805","title":"Implicit Search via Discrete Diffusion: A Study on Chess","created":"2025-02-27","cited_by_count":11,"area":"diffusion"},{"id":"2505.05755","title":"Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions","created":"2025-05-09","cited_by_count":11,"area":null},{"id":"2602.14233","title":"Evaluating LLMs in Finance Requires Explicit Bias Consideration","created":"2026-02-15","cited_by_count":11,"area":null},{"id":"2502.04667","title":"Compositional Generalization from Learned Skills via CoT Training: A Theoretical and Structural Analysis for Reasoning","created":"2025-02-07","cited_by_count":10,"area":"lr"},{"id":"2503.21801","title":"Efficient Joint Prediction of Multiple Future Tokens","created":"2025-03-24","cited_by_count":10,"area":null},{"id":"2409.11143","title":"Semformer: Transformer Language Models with Semantic Planning","created":"2024-09-17","cited_by_count":9,"area":null},{"id":"2412.04318","title":"The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation","created":"2024-12-05","cited_by_count":9,"area":null},{"id":"2507.15865","title":"From Reasoning to Super-Intelligence: A Search-Theoretic Perspective","created":"2025-07-13","cited_by_count":9,"area":"lr"},{"id":"2511.21338","title":"Masks Can Be Distracting: On Context Comprehension in Diffusion Language Models","created":"2025-11-26","cited_by_count":9,"area":"diffusion"},{"id":"2603.02203","title":"Tool Verification for Test-Time Reinforcement Learning","created":"2026-03-02","cited_by_count":9,"area":"lr"},{"id":"2410.13779","title":"The Mystery of the Pathological Path-star Task for Language Models","created":"2024-10-17","cited_by_count":8,"area":null},{"id":"2505.13791","title":"Scalable Autoregressive 3D Molecule Generation","created":"2025-05-20","cited_by_count":8,"area":"diffusion"},{"id":"2506.03616","title":"Learning to Insert [PAUSE] Tokens for Better Reasoning","created":"2025-06-04","cited_by_count":8,"area":"lr"},{"id":"2510.01274","title":"TraceDet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Models","created":"2025-09-30","cited_by_count":8,"area":null},{"id":"2510.06499","title":"Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels","created":"2025-10-07","cited_by_count":8,"area":"offlinerl"},{"id":"2512.15605","title":"Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction","created":"2025-12-17","cited_by_count":8,"area":null},{"id":"2405.16847","title":"TokenUnify: Scaling Up Autoregressive Pretraining for Neuron Segmentation","created":"2024-05-27","cited_by_count":7,"area":null},{"id":"2410.14655","title":"Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens","created":"2024-10-18","cited_by_count":7,"area":"synth"},{"id":"2510.03971","title":"What Can You Do When You Have Zero Rewards During RL?","created":"2025-10-04","cited_by_count":7,"area":"lr"},{"id":"2602.06019","title":"Multi-Token Prediction via Self-Distillation","created":"2026-02-05","cited_by_count":7,"area":"inference"},{"id":"2509.23186","title":"Understanding and Enhancing the Planning Capability of Language Models via Multi-Token Prediction","created":"2025-09-27","cited_by_count":6,"area":null},{"id":"2510.20280","title":"Context-level Language Modeling by Learning Predictive Context Embeddings","created":"2025-10-23","cited_by_count":6,"area":null},{"id":"2601.15158","title":"Outcome-Based RL Provably Leads Transformers to Reason, but Only With the Right Data","created":"2026-01-21","cited_by_count":6,"area":"lr"},{"id":"2604.12493","title":"Latent Planning Emerges with Scale","created":"2026-04-14","cited_by_count":6,"area":null},{"id":"2406.17404","title":"Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training","created":"2024-06-25","cited_by_count":5,"area":"inference"},{"id":"2502.13533","title":"Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models","created":"2025-02-19","cited_by_count":5,"area":null},{"id":"2504.11336","title":"Looking beyond the next token","created":"2025-04-15","cited_by_count":5,"area":null},{"id":"2511.05397","title":"EveryDayVLA: A Vision-Language-Action Model for Affordable Robotic Manipulation","created":"2025-11-07","cited_by_count":5,"area":"vla"},{"id":"2603.04948","title":"$\\nabla$-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent Space","created":"2026-03-05","cited_by_count":5,"area":"lr"},{"id":"2604.00375","title":"Locally Confident, Globally Stuck: The Quality-Exploration Dilemma in Diffusion Language Models","created":"2026-04-01","cited_by_count":5,"area":"lr"},{"id":"2508.12903","title":"A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models","created":"2025-08-18","cited_by_count":4,"area":null},{"id":"2509.15536","title":"SAMPO:Scale-wise Autoregression with Motion PrOmpt for generative world models","created":"2025-09-19","cited_by_count":4,"area":"wm"},{"id":"2510.02224","title":"Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models","created":"2025-10-02","cited_by_count":4,"area":null},{"id":"2510.19990","title":"Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond","created":"2025-10-22","cited_by_count":4,"area":"lr"},{"id":"2512.17065","title":"XLM: A Python package for non-autoregressive language models","created":"2025-12-18","cited_by_count":4,"area":null},{"id":"2601.16971","title":"Auto-Regressive Masked Diffusion Models","created":"2026-01-23","cited_by_count":4,"area":"diffusion"},{"id":"2602.19980","title":"Discrete Diffusion Models Exploit Asymmetry to Solve Lookahead Planning Tasks","created":"2026-02-23","cited_by_count":4,"area":"diffusion"},{"id":"2605.23454","title":"ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning","created":"2026-05-22","cited_by_count":4,"area":null},{"id":"2406.18505","title":"Mental Modeling of Reinforcement Learning Agents by Language Models","created":"2024-06-26","cited_by_count":3,"area":"agents"},{"id":"2409.07889","title":"BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding","created":"2024-09-12","cited_by_count":3,"area":null},{"id":"2412.07629","title":"Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering","created":"2024-12-10","cited_by_count":3,"area":null},{"id":"2503.10542","title":"Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More","created":"2025-03-13","cited_by_count":3,"area":null},{"id":"2507.09955","title":"DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models","created":"2025-07-14","cited_by_count":3,"area":"moe"},{"id":"2508.19228","title":"Predicting the Order of Upcoming Tokens Improves Language Modeling","created":"2025-08-26","cited_by_count":3,"area":null},{"id":"2509.20336","title":"Uncovering Graph Reasoning in Decoder-only Transformers with Circuit Tracing","created":"2025-09-24","cited_by_count":3,"area":"lr"},{"id":"2602.19281","title":"Limited Reasoning Space: The cage of long-horizon reasoning in LLMs","created":"2026-02-22","cited_by_count":3,"area":"lr"},{"id":"2604.06155","title":"Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement","created":"2026-04-07","cited_by_count":3,"area":"wm"},{"id":"2607.01218","title":"The State-Prediction Separation Hypothesis","created":"2026-07-01","cited_by_count":3,"area":null}],"next_cursor":"100"}