{"total":120,"rows":[{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132,"area":"lr"},{"id":"2205.11916","title":"Large Language Models are Zero-Shot Reasoners","created":"2022-05-24","cited_by_count":4374,"area":"lr"},{"id":"2202.12837","title":"Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?","created":"2022-02-25","cited_by_count":1434,"area":"lr"},{"id":"2203.14465","title":"STaR: Bootstrapping Reasoning With Reasoning","created":"2022-03-28","cited_by_count":940,"area":"lr"},{"id":"2412.19437","title":"DeepSeek-V3 Technical Report","created":"2024-12-27","cited_by_count":811,"area":"lr"},{"id":"2304.06767","title":"RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment","created":"2023-04-13","cited_by_count":686,"area":"lr"},{"id":"2401.02954","title":"DeepSeek LLM: Scaling Open-Source Language Models with Longtermism","created":"2024-01-05","cited_by_count":642,"area":"lr"},{"id":"2310.10631","title":"Llemma: An Open Language Model For Mathematics","created":"2023-10-16","cited_by_count":388,"area":"lr"},{"id":"2501.00663","title":"Titans: Learning to Memorize at Test Time","created":"2024-12-31","cited_by_count":328,"area":"lr"},{"id":"2506.04178","title":"OpenThoughts: Data Recipes for Reasoning Models","created":"2025-06-04","cited_by_count":304,"area":"lr"},{"id":"2307.02477","title":"Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks","created":"2023-07-05","cited_by_count":246,"area":"lr"},{"id":"2502.17416","title":"Reasoning with Latent Thoughts: On the Power of Looped Transformers","created":"2025-02-24","cited_by_count":240,"area":"lr"},{"id":"2310.06786","title":"OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text","created":"2023-10-10","cited_by_count":155,"area":"lr"},{"id":"2404.03683","title":"Stream of Search (SoS): Learning to Search in Language","created":"2024-04-01","cited_by_count":145,"area":"lr"},{"id":"2504.07934","title":"SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement","created":"2025-04-10","cited_by_count":145,"area":"lr"},{"id":"2202.07206","title":"Impact of Pretraining Term Frequencies on Few-Shot Reasoning","created":"2022-02-15","cited_by_count":142,"area":"lr"},{"id":"2212.10535","title":"A Survey of Deep Learning for Mathematical Reasoning","created":"2022-12-20","cited_by_count":124,"area":"lr"},{"id":"2410.23123","title":"On Memorization of Large Language Models in Logical Reasoning","created":"2024-10-30","cited_by_count":117,"area":"lr"},{"id":"2407.20311","title":"Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process","created":"2024-07-29","cited_by_count":108,"area":"lr"},{"id":"2310.01714","title":"Large Language Models as Analogical Reasoners","created":"2023-10-03","cited_by_count":107,"area":"lr"},{"id":"2305.15054","title":"A Mechanistic Interpretation of Arithmetic Reasoning in Language Models using Causal Mediation Analysis","created":"2023-05-24","cited_by_count":89,"area":"lr"},{"id":"2307.09476","title":"Overthinking the Truth: Understanding how Language Models Process False Demonstrations","created":"2023-07-18","cited_by_count":81,"area":"lr"},{"id":"2504.04823","title":"Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models","created":"2025-04-07","cited_by_count":73,"area":"lr"},{"id":"2210.12023","title":"A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models","created":"2022-10-21","cited_by_count":68,"area":"lr"},{"id":"2309.02144","title":"Making Large Language Models Better Reasoners with Alignment","created":"2023-09-05","cited_by_count":63,"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":"2501.15089","title":"LongReason: A Synthetic Long-Context Reasoning Benchmark via Context Expansion","created":"2025-01-25","cited_by_count":50,"area":"lr"},{"id":"2501.02448","title":"Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap","created":"2025-01-05","cited_by_count":43,"area":"lr"},{"id":"2509.02350","title":"Implicit Reasoning in Large Language Models: A Comprehensive Survey","created":"2025-09-02","cited_by_count":43,"area":"lr"},{"id":"2404.05567","title":"Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models","created":"2024-04-08","cited_by_count":42,"area":"lr"},{"id":"2508.15096","title":"Nemotron-CC-Math: A 133 Billion-Token-Scale High Quality Math Pretraining Dataset","created":"2025-08-20","cited_by_count":38,"area":"lr"},{"id":"2604.07822","title":"Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers","created":"2026-04-09","cited_by_count":37,"area":"lr"},{"id":"2408.06621","title":"Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs","created":"2024-08-13","cited_by_count":36,"area":"lr"},{"id":"2405.06001","title":"LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit","created":"2024-05-09","cited_by_count":33,"area":"lr"},{"id":"2402.17887","title":"JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability","created":"2024-02-27","cited_by_count":32,"area":"lr"},{"id":"2504.13941","title":"Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning","created":"2025-04-15","cited_by_count":31,"area":"lr"},{"id":"2505.10475","title":"Parallel Scaling Law for Language Models","created":"2025-05-15","cited_by_count":30,"area":"lr"},{"id":"2310.10845","title":"CoTFormer: A Chain-of-Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference","created":"2023-10-16","cited_by_count":27,"area":"lr"},{"id":"2402.07754","title":"Diffusion of Thoughts: Chain-of-Thought Reasoning in Diffusion Language Models","created":"2024-02-12","cited_by_count":27,"area":"lr"},{"id":"2301.11596","title":"ThoughtSource: A central hub for large language model reasoning data","created":"2023-01-27","cited_by_count":23,"area":"lr"},{"id":"2502.00963","title":"PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs","created":"2025-02-03","cited_by_count":23,"area":"lr"},{"id":"2503.07932","title":"A Theory of Learning with Autoregressive Chain of Thought","created":"2025-03-11","cited_by_count":23,"area":"lr"},{"id":"2305.05191","title":"COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective","created":"2023-05-09","cited_by_count":22,"area":"lr"},{"id":"2502.07827","title":"Implicit Language Models are RNNs: Balancing Parallelization and Expressivity","created":"2025-02-10","cited_by_count":22,"area":"lr"},{"id":"2602.06176","title":"Large Language Model Reasoning Failures","created":"2026-02-05","cited_by_count":21,"area":"lr"},{"id":"2212.08286","title":"ALERT: Adapting Language Models to Reasoning Tasks","created":"2022-12-16","cited_by_count":17,"area":"lr"},{"id":"2511.10645","title":"ParoQuant: Pairwise Rotation Quantization for Efficient Reasoning LLM Inference","created":"2025-11-13","cited_by_count":17,"area":"lr"},{"id":"2202.00436","title":"ROCK: Causal Inference Principles for Reasoning about Commonsense Causality","created":"2022-01-31","cited_by_count":16,"area":"lr"},{"id":"2407.05399","title":"IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning","created":"2024-07-07","cited_by_count":16,"area":"lr"},{"id":"2509.23184","title":"PonderLM-2: Pretraining LLM with Latent Thoughts in Continuous Space","created":"2025-09-27","cited_by_count":16,"area":"lr"},{"id":"2306.17778","title":"Look, Remember and Reason: Grounded reasoning in videos with language models","created":"2023-06-30","cited_by_count":14,"area":"lr"},{"id":"2410.12881","title":"MIND: Math Informed syNthetic Dialogues for Pretraining LLMs","created":"2024-10-15","cited_by_count":14,"area":"lr"},{"id":"2310.13332","title":"Democratizing Reasoning Ability: Tailored Learning from Large Language Model","created":"2023-10-20","cited_by_count":13,"area":"lr"},{"id":"2402.16602","title":"Rethinking Negative Instances for Generative Named Entity Recognition","created":"2024-02-26","cited_by_count":13,"area":"lr"},{"id":"2601.05679","title":"Do Sparse Autoencoders Identify Reasoning Features in Language Models?","created":"2026-01-09","cited_by_count":12,"area":"lr"},{"id":"2305.09802","title":"Sasha: Creative Goal-Oriented Reasoning in Smart Homes with Large Language Models","created":"2023-05-16","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":"2311.18812","title":"What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations","created":"2023-11-30","cited_by_count":10,"area":"lr"},{"id":"2411.02528","title":"What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length","created":"2024-11-04","cited_by_count":10,"area":"lr"},{"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":"2606.00206","title":"Quantized Reasoning Models Think They Need to Think Longer, but They Do Not","created":"2026-05-29","cited_by_count":10,"area":"lr"},{"id":"2403.03029","title":"Socratic Reasoning Improves Positive Text Rewriting","created":"2024-03-05","cited_by_count":9,"area":"lr"},{"id":"2510.00404","title":"AbsTopK: Rethinking Sparse Autoencoders For Bidirectional Features","created":"2025-10-01","cited_by_count":9,"area":"lr"},{"id":"2402.10691","title":"Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts","created":"2024-02-16","cited_by_count":8,"area":"lr"},{"id":"2505.09614","title":"Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?","created":"2025-05-14","cited_by_count":7,"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":"2311.04348","title":"Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning","created":"2023-11-07","cited_by_count":6,"area":"lr"},{"id":"2408.16345","title":"The Unreasonable Ineffectiveness of Nucleus Sampling on Mitigating Text Memorization","created":"2024-08-29","cited_by_count":6,"area":"lr"},{"id":"2410.01044","title":"RATIONALYST: Mining Implicit Rationales for Process Supervision of Reasoning","created":"2024-10-01","cited_by_count":6,"area":"lr"},{"id":"2505.10182","title":"Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning","created":"2025-05-15","cited_by_count":6,"area":"lr"},{"id":"2505.18087","title":"CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays","created":"2025-05-23","cited_by_count":6,"area":"lr"},{"id":"2508.06533","title":"The Art of Breaking Words: Rethinking Multilingual Tokenizer Design","created":"2025-08-03","cited_by_count":6,"area":"lr"},{"id":"2508.12726","title":"DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning","created":"2025-08-18","cited_by_count":6,"area":"lr"},{"id":"2204.05673","title":"What do Toothbrushes do in the Kitchen? How Transformers Think our World is Structured","created":"2022-04-12","cited_by_count":5,"area":"lr"},{"id":"2305.11461","title":"Hint of Pseudo Code (HoPC): Zero-Shot Step by Step Pseudo Code Reasoning Prompting","created":"2023-05-19","cited_by_count":5,"area":"lr"},{"id":"2405.01660","title":"Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts","created":"2024-05-02","cited_by_count":5,"area":"lr"},{"id":"2412.06845","title":"7B Fully Open Source Moxin-LLM/VLM -- From Pretraining to GRPO-based Reinforcement Learning Enhancement","created":"2024-12-08","cited_by_count":5,"area":"lr"},{"id":"2211.15593","title":"GPT-Neo for commonsense reasoning -- a theoretical and practical lens","created":"2022-11-28","cited_by_count":4,"area":"lr"},{"id":"2504.09100","title":"A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions","created":"2025-04-12","cited_by_count":4,"area":"lr"},{"id":"2506.07463","title":"CCI4.0: A Bilingual Pretraining Dataset for Enhancing Reasoning in Large Language Models","created":"2025-06-09","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":"2510.25804","title":"Beyond Length: Quantifying Long-Range Information for Long-Context LLM Pretraining Data","created":"2025-10-29","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":"2602.19548","title":"Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining","created":"2026-02-23","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":"2604.16909","title":"PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations","created":"2026-04-18","cited_by_count":4,"area":"lr"},{"id":"2211.07727","title":"Logical Tasks for Measuring Extrapolation and Rule Comprehension","created":"2022-11-14","cited_by_count":3,"area":"lr"},{"id":"2401.10712","title":"Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World Knowledge","created":"2024-01-19","cited_by_count":3,"area":"lr"},{"id":"2504.14175","title":"Hypothetical Documents or Knowledge Leakage? Rethinking LLM-based Query Expansion","created":"2025-04-19","cited_by_count":3,"area":"lr"},{"id":"2509.25531","title":"MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources","created":"2025-09-29","cited_by_count":3,"area":"lr"},{"id":"2512.06201","title":"K2-V2: A 360-Open, Reasoning-Enhanced LLM","created":"2025-12-05","cited_by_count":3,"area":"lr"},{"id":"2512.24503","title":"Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice","created":"2025-12-30","cited_by_count":3,"area":"lr"},{"id":"2605.05227","title":"Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods","created":"2026-04-19","cited_by_count":3,"area":"lr"},{"id":"2605.15257","title":"Training on Documents About Monitoring Leads to CoT Obfuscation","created":"2026-05-14","cited_by_count":3,"area":"lr"},{"id":"2503.23779","title":"WinoWhat: A Parallel Corpus of Paraphrased WinoGrande Sentences with Common Sense Categorization","created":"2025-03-31","cited_by_count":2,"area":"lr"},{"id":"2505.04075","title":"Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progress","created":"2025-05-07","cited_by_count":2,"area":"lr"},{"id":"2505.21598","title":"Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives","created":"2025-05-27","cited_by_count":2,"area":"lr"},{"id":"2507.03327","title":"Read Quietly, Think Aloud: Decoupling Comprehension and Reasoning in LLMs","created":"2025-07-04","cited_by_count":2,"area":"lr"},{"id":"2603.23539","title":"PLDR-LLMs Reason At Self-Organized Criticality","created":"2026-03-12","cited_by_count":2,"area":"lr"},{"id":"2604.07963","title":"Rethinking Data Mixing from the Perspective of Large Language Models","created":"2026-04-09","cited_by_count":2,"area":"lr"}],"next_cursor":"100"}