{"total":44,"rows":[{"id":"2305.15408","title":"Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective","created":"2023-05-24","cited_by_count":294,"area":"lr"},{"id":"2402.12875","title":"Chain of Thought Empowers Transformers to Solve Inherently Serial Problems","created":"2024-02-20","cited_by_count":260,"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":"2404.15758","title":"Let's Think Dot by Dot: Hidden Computation in Transformer Language Models","created":"2024-04-24","cited_by_count":191,"area":"lr"},{"id":"2506.21734","title":"Hierarchical Reasoning Model","created":"2025-06-26","cited_by_count":177,"area":"lr"},{"id":"2505.12514","title":"Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought","created":"2025-05-18","cited_by_count":77,"area":"lr"},{"id":"2210.02671","title":"A Logic for Expressing Log-Precision Transformers","created":"2022-10-06","cited_by_count":67,"area":"lr"},{"id":"2405.18512","title":"Understanding Transformer Reasoning Capabilities via Graph Algorithms","created":"2024-05-28","cited_by_count":67,"area":"lr"},{"id":"2505.08243","title":"Training Strategies for Efficient Embodied Reasoning","created":"2025-05-13","cited_by_count":55,"area":"lr"},{"id":"2310.07923","title":"The Expressive Power of Transformers with Chain of Thought","created":"2023-10-11","cited_by_count":50,"area":"lr"},{"id":"2410.02338","title":"How Much Can RAG Help the Reasoning of LLM?","created":"2024-10-03","cited_by_count":31,"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":"2405.17950","title":"Self-Guiding Exploration for Combinatorial Problems","created":"2024-05-28","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":"2505.18948","title":"Exact Expressive Power of Transformers with Padding","created":"2025-05-25","cited_by_count":19,"area":"lr"},{"id":"2410.07432","title":"Can Transformers Reason Logically? A Study in SAT Solving","created":"2024-10-09","cited_by_count":16,"area":"lr"},{"id":"2509.25239","title":"A Formal Comparison Between Chain of Thought and Latent Thought","created":"2025-09-25","cited_by_count":15,"area":"lr"},{"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":"2505.19245","title":"To CoT or To Loop? A Formal Comparison Between Chain-of-Thought and Looped Transformers","created":"2025-05-25","cited_by_count":11,"area":"lr"},{"id":"2411.11496","title":"Safe + Safe = Unsafe? Exploring How Safe Images Can Be Exploited to Jailbreak Large Vision-Language Models","created":"2024-11-18","cited_by_count":10,"area":"lr"},{"id":"2411.02344","title":"Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning","created":"2024-11-04","cited_by_count":9,"area":"lr"},{"id":"2505.08140","title":"Lost in Transmission: When and Why LLMs Fail to Reason Globally","created":"2025-05-13","cited_by_count":9,"area":"lr"},{"id":"2504.04702","title":"Provable Failure of Language Models in Learning Majority Boolean Logic via Gradient Descent","created":"2025-04-07","cited_by_count":8,"area":"lr"},{"id":"2501.12997","title":"Ehrenfeucht-Haussler Rank and Chain of Thought","created":"2025-01-22","cited_by_count":6,"area":"lr"},{"id":"2503.01544","title":"Compositional Reasoning with Transformers, RNNs, and Chain of Thought","created":"2025-03-03","cited_by_count":6,"area":"lr"},{"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":"2501.00712","title":"Rethinking Addressing in Language Models via Contexualized Equivariant Positional Encoding","created":"2025-01-01","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":"2312.17248","title":"Rethinking Model-based, Policy-based, and Value-based Reinforcement Learning via the Lens of Representation Complexity","created":"2023-12-28","cited_by_count":2,"area":"lr"},{"id":"2506.10647","title":"Data Shifts Hurt CoT: A Theoretical Study","created":"2025-06-12","cited_by_count":2,"area":"lr"},{"id":"2602.02103","title":"How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning","created":"2026-02-02","cited_by_count":2,"area":"lr"},{"id":"2602.05120","title":"Certifiable Boolean Reasoning Is Universal","created":"2026-02-04","cited_by_count":2,"area":"lr"},{"id":"2605.30757","title":"Chain-of-Thought and Compressed Looped Transformers: A Memory-Budget Separation","created":"2026-05-29","cited_by_count":2,"area":"lr"},{"id":"2609.15309","title":"When Agents Slow Down: Understanding LLM Agents' Test-Time Strategies via Elo-per-token Analysis","created":"2026-09-14","cited_by_count":2,"area":"lr"},{"id":"2506.21797","title":"Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning","created":"2025-06-26","cited_by_count":1,"area":"lr"},{"id":"2508.02583","title":"CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge","created":"2025-08-04","cited_by_count":1,"area":"lr"},{"id":"2511.01190","title":"Analyzing the Power of Chain of Thought through Memorization Capabilities","created":"2025-11-03","cited_by_count":1,"area":"lr"},{"id":"2602.17993","title":"Turbo Connection: Reasoning as Information Flow from Higher to Lower Layers","created":"2026-02-20","cited_by_count":1,"area":"lr"},{"id":"2604.14501","title":"On the Expressive Power and Limitations of Multi-Layer SSMs","created":"2026-04-16","cited_by_count":1,"area":"lr"},{"id":"2604.25800","title":"Barriers to Universal Reasoning With Transformers (And How to Overcome Them)","created":"2026-04-28","cited_by_count":1,"area":"lr"},{"id":"2605.04330","title":"The Scaling Properties of Implicit Deductive Reasoning in Transformers","created":"2026-05-05","cited_by_count":1,"area":"lr"},{"id":"2605.18820","title":"Emergence of Frontier Superposition: M\\\"obius attractor and Cascade Supervision","created":"2026-05-12","cited_by_count":0,"area":"lr"},{"id":"2605.19944","title":"A Measure-Theoretic Analysis of Reasoning: Structural Generalization and Approximation Limits","created":"2026-05-19","cited_by_count":0,"area":"lr"},{"id":"2609.33134","title":"Ceiling of a Task: When Can a Transformer Succeed Without Its Chain of Thought?","created":"2026-09-27","cited_by_count":0,"area":"lr"}],"next_cursor":null}