{"total":169,"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":"2409.12917","title":"Training Language Models to Self-Correct via Reinforcement Learning","created":"2024-09-19","cited_by_count":426,"area":"lr"},{"id":"2412.14093","title":"Alignment faking in large language models","created":"2024-12-18","cited_by_count":360,"area":"alignment"},{"id":"2511.18397","title":"Natural Emergent Misalignment from Reward Hacking in Production RL","created":"2025-11-23","cited_by_count":136,"area":"alignment"},{"id":"2503.21614","title":"A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond","created":"2025-03-27","cited_by_count":132,"area":"lr"},{"id":"2506.17218","title":"Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens","created":"2025-06-20","cited_by_count":124,"area":"lr"},{"id":"2505.15778","title":"Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space","created":"2025-05-21","cited_by_count":117,"area":"lr"},{"id":"2410.13787","title":"Looking Inward: Language Models Can Learn About Themselves by Introspection","created":"2024-10-17","cited_by_count":113,"area":null},{"id":"2501.13958","title":"A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models","created":"2025-01-21","cited_by_count":88,"area":"rag"},{"id":"2404.17140","title":"Small Language Models Need Strong Verifiers to Self-Correct Reasoning","created":"2024-04-26","cited_by_count":86,"area":"lr"},{"id":"2507.06203","title":"A Survey on Latent Reasoning","created":"2025-07-08","cited_by_count":81,"area":"lr"},{"id":"2405.15071","title":"Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization","created":"2024-05-23","cited_by_count":79,"area":"lr"},{"id":"2405.16506","title":"GRAG: Graph Retrieval-Augmented Generation","created":"2024-05-26","cited_by_count":78,"area":"rag"},{"id":"2406.12775","title":"Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries","created":"2024-06-18","cited_by_count":78,"area":null},{"id":"2501.19201","title":"Efficient Reasoning with Hidden Thinking","created":"2025-01-31","cited_by_count":75,"area":"lr"},{"id":"2607.15495","title":"Verbalizable Representations Form a Global Workspace in Language Models","created":"2026-07-16","cited_by_count":75,"area":null},{"id":"2406.11050","title":"A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners","created":"2024-06-16","cited_by_count":73,"area":"lr"},{"id":"2501.08156","title":"Are DeepSeek R1 And Other Reasoning Models More Faithful?","created":"2025-01-14","cited_by_count":72,"area":"lr"},{"id":"2411.04986","title":"The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalities","created":"2024-11-07","cited_by_count":69,"area":null},{"id":"2504.07986","title":"SEAL: Steerable Reasoning Calibration of Large Language Models for Free","created":"2025-04-07","cited_by_count":68,"area":"lr"},{"id":"2510.18632","title":"Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views","created":"2025-10-21","cited_by_count":65,"area":"lr"},{"id":"2406.01563","title":"LoFiT: Localized Fine-tuning on LLM Representations","created":"2024-06-03","cited_by_count":57,"area":null},{"id":"2506.02867","title":"Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning","created":"2025-06-03","cited_by_count":51,"area":"lr"},{"id":"2407.14507","title":"Internal Consistency and Self-Feedback in Large Language Models: A Survey","created":"2024-07-19","cited_by_count":50,"area":"lr"},{"id":"2502.14829","title":"Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps","created":"2025-02-20","cited_by_count":47,"area":"lr"},{"id":"2412.02975","title":"Theoretical limitations of multi-layer Transformer","created":"2024-12-04","cited_by_count":45,"area":null},{"id":"2411.13504","title":"Disentangling Memory and Reasoning Ability in Large Language Models","created":"2024-11-20","cited_by_count":44,"area":"lr"},{"id":"2505.20674","title":"PonderLM: Pretraining Language Models to Ponder in Continuous Space","created":"2025-05-27","cited_by_count":44,"area":null},{"id":"2505.13237","title":"SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based on Speech and Audio Information","created":"2025-05-19","cited_by_count":42,"area":"lr"},{"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":"2505.15957","title":"Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey","created":"2025-05-21","cited_by_count":39,"area":null},{"id":"2502.06975","title":"Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents","created":"2025-02-10","cited_by_count":36,"area":"agents"},{"id":"2502.08524","title":"LLM Pretraining with Continuous Concepts","created":"2025-02-12","cited_by_count":36,"area":"lr"},{"id":"2503.02854","title":"(How) Do Language Models Track State?","created":"2025-03-04","cited_by_count":34,"area":null},{"id":"2505.18454","title":"Hybrid Latent Reasoning via Reinforcement Learning","created":"2025-05-24","cited_by_count":32,"area":"lr"},{"id":"2410.01748","title":"Not All LLM Reasoners Are Created Equal","created":"2024-10-02","cited_by_count":30,"area":"lr"},{"id":"2406.11614","title":"Intrinsic Test of Unlearning Using Parametric Knowledge Traces","created":"2024-06-17","cited_by_count":29,"area":"safety"},{"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":"2405.18711","title":"Calibrating Reasoning in Language Models with Internal Consistency","created":"2024-05-29","cited_by_count":28,"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":"2502.04506","title":"When One LLM Drools, Multi-LLM Collaboration Rules","created":"2025-02-06","cited_by_count":28,"area":null},{"id":"2507.02199","title":"Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer","created":"2025-07-02","cited_by_count":27,"area":"lr"},{"id":"2510.13554","title":"Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization","created":"2025-10-15","cited_by_count":25,"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":"2502.10835","title":"Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models","created":"2025-02-15","cited_by_count":23,"area":"lr"},{"id":"2503.23084","title":"The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Direction","created":"2025-03-29","cited_by_count":21,"area":"lr"},{"id":"2505.13141","title":"Language-Specific Latent Process Hinders Cross-Lingual Performance","created":"2025-05-19","cited_by_count":21,"area":null},{"id":"2502.13913","title":"How Do LLMs Perform Two-Hop Reasoning in Context?","created":"2025-02-19","cited_by_count":20,"area":"lr"},{"id":"2505.13143","title":"Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models","created":"2025-05-19","cited_by_count":20,"area":"lr"},{"id":"2405.16674","title":"Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory","created":"2024-05-26","cited_by_count":19,"area":null},{"id":"2412.04703","title":"Transformers Struggle to Learn to Search","created":"2024-12-06","cited_by_count":19,"area":null},{"id":"2506.05140","title":"AudioLens: A Closer Look at Auditory Attribute Perception of Large Audio-Language Models","created":"2025-06-05","cited_by_count":18,"area":null},{"id":"2406.10218","title":"Semantic Membership Inference Attack against Large Language Models","created":"2024-06-14","cited_by_count":17,"area":"safety"},{"id":"2502.08301","title":"Compromising Honesty and Harmlessness in Language Models via Deception Attacks","created":"2025-02-12","cited_by_count":17,"area":null},{"id":"2502.14634","title":"CER: Confidence Enhanced Reasoning in LLMs","created":"2025-02-20","cited_by_count":17,"area":"lr"},{"id":"2406.14928","title":"Autonomous Agents for Collaborative Task under Information Asymmetry","created":"2024-06-21","cited_by_count":16,"area":"agents"},{"id":"2408.03247","title":"Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons","created":"2024-08-06","cited_by_count":16,"area":null},{"id":"2411.15862","title":"Do LLMs Really Think Step-by-step In Implicit Reasoning?","created":"2024-11-24","cited_by_count":16,"area":"lr"},{"id":"2601.08808","title":"Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge","created":"2026-01-13","cited_by_count":16,"area":"lr"},{"id":"2405.15302","title":"Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism","created":"2024-05-24","cited_by_count":15,"area":"lr"},{"id":"2410.08414","title":"Understanding the Interplay between Parametric and Contextual Knowledge for Large Language Models","created":"2024-10-10","cited_by_count":15,"area":null},{"id":"2510.24941","title":"Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought","created":"2025-10-28","cited_by_count":15,"area":"lr"},{"id":"2412.04614","title":"Extractive Structures Learned in Pretraining Enable Generalization on Finetuned Facts","created":"2024-12-05","cited_by_count":14,"area":null},{"id":"2503.07604","title":"Implicit Reasoning in Transformers is Reasoning through Shortcuts","created":"2025-03-10","cited_by_count":14,"area":"lr"},{"id":"2601.12626","title":"Linear Mechanisms for Spatiotemporal Reasoning in Vision Language Models","created":"2026-01-18","cited_by_count":14,"area":"lr"},{"id":"2404.01288","title":"Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided","created":"2024-04-01","cited_by_count":13,"area":null},{"id":"2504.01928","title":"Is the Reversal Curse a Binding Problem? Uncovering Limitations of Transformers from a Basic Generalization Failure","created":"2025-04-02","cited_by_count":13,"area":null},{"id":"2505.22651","title":"Sherlock: Self-Correcting Reasoning in Vision-Language Models","created":"2025-05-28","cited_by_count":13,"area":"lr"},{"id":"2408.00137","title":"Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment","created":"2024-07-31","cited_by_count":12,"area":null},{"id":"2410.20210","title":"Looking Beyond The Top-1: Transformers Determine Top Tokens In Order","created":"2024-10-26","cited_by_count":12,"area":null},{"id":"2504.16787","title":"Credible Plan-Driven RAG Method for Multi-Hop Question Answering","created":"2025-04-23","cited_by_count":12,"area":"rag"},{"id":"2507.23581","title":"GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning","created":"2025-07-31","cited_by_count":12,"area":"lr"},{"id":"2606.07157","title":"Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models","created":"2026-06-05","cited_by_count":12,"area":"lr"},{"id":"2408.13184","title":"Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planning","created":"2024-08-23","cited_by_count":11,"area":"lr"},{"id":"2412.01113","title":"LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics","created":"2024-12-02","cited_by_count":11,"area":"lr"},{"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":"2505.22240","title":"BioHopR: A Benchmark for Multi-Hop, Multi-Answer Reasoning in Biomedical Domain","created":"2025-05-28","cited_by_count":10,"area":"lr"},{"id":"2506.10887","title":"Generalization or Hallucination? Understanding Out-of-Context Reasoning in Transformers","created":"2025-06-12","cited_by_count":10,"area":"lr"},{"id":"2503.16356","title":"CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners","created":"2025-03-20","cited_by_count":9,"area":null},{"id":"2506.00264","title":"MultiHoax: A Dataset of Multi-hop False-Premise Questions","created":"2025-05-30","cited_by_count":9,"area":null},{"id":"2409.12437","title":"Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data","created":"2024-09-19","cited_by_count":8,"area":"lr"},{"id":"2501.07845","title":"Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning","created":"2025-01-14","cited_by_count":8,"area":"lr"},{"id":"2502.18431","title":"TextGames: Learning to Self-Play Text-Based Puzzle Games via Language Model Reasoning","created":"2025-02-25","cited_by_count":8,"area":"lr"},{"id":"2503.01714","title":"Word Form Matters: LLMs' Semantic Reconstruction under Typoglycemia","created":"2025-03-03","cited_by_count":8,"area":null},{"id":"2510.01685","title":"How Do Language Models Compose Functions?","created":"2025-10-02","cited_by_count":8,"area":null},{"id":"2406.12084","title":"When Reasoning Meets Information Aggregation: A Case Study with Sports Narratives","created":"2024-06-17","cited_by_count":7,"area":"lr"},{"id":"2506.15732","title":"Can LLMs Reconcile Knowledge Conflicts in Counterfactual Reasoning","created":"2025-06-15","cited_by_count":7,"area":"lr"},{"id":"2507.07207","title":"Scaling can lead to compositional generalization","created":"2025-07-09","cited_by_count":7,"area":null},{"id":"2512.03276","title":"Too Late to Recall: Explaining the Two-Hop Problem in Multimodal Knowledge Retrieval","created":"2025-12-02","cited_by_count":7,"area":"mllm"},{"id":"2604.06374","title":"The Illusion of Superposition? A Principled Analysis of Latent Thinking in Language Models","created":"2026-04-07","cited_by_count":7,"area":"lr"},{"id":"2604.08299","title":"SeLaR: Selective Latent Reasoning in Large Language Models","created":"2026-04-09","cited_by_count":7,"area":"lr"},{"id":"2607.15178","title":"T^2MLR: Transformer with Temporal Middle-Layer Recurrence","created":"2026-07-16","cited_by_count":7,"area":null},{"id":"2412.10654","title":"Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data","created":"2024-12-14","cited_by_count":6,"area":"lr"},{"id":"2508.19221","title":"Evaluating the Evaluators: Are readability metrics good measures of readability?","created":"2025-08-26","cited_by_count":6,"area":null},{"id":"2509.05739","title":"Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated","created":"2025-09-06","cited_by_count":6,"area":"lr"},{"id":"2409.10840","title":"Implicit Reasoning in Deep Time Series Forecasting","created":"2024-09-17","cited_by_count":5,"area":"lr"},{"id":"2502.10626","title":"K-Edit: Language Model Editing with Contextual Knowledge Awareness","created":"2025-02-15","cited_by_count":5,"area":null},{"id":"2502.13361","title":"RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering","created":"2025-02-19","cited_by_count":5,"area":"rag"},{"id":"2506.13253","title":"Distinct Computations Emerge From Compositional Curricula in In-Context Learning","created":"2025-06-16","cited_by_count":5,"area":null},{"id":"2509.09650","title":"All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens","created":"2025-09-11","cited_by_count":5,"area":null}],"next_cursor":"100"}