{"total":17,"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":"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":"2502.02393","title":"Lower Bounds for Chain-of-Thought Reasoning in Hard-Attention Transformers","created":"2025-02-04","cited_by_count":28,"area":"lr"},{"id":"2405.04669","title":"Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics","created":"2024-05-07","cited_by_count":27,"area":"lr"},{"id":"2411.07107","title":"Training Neural Networks as Recognizers of Formal Languages","created":"2024-11-11","cited_by_count":24,"area":"lr"},{"id":"2203.12186","title":"AbductionRules: Training Transformers to Explain Unexpected Inputs","created":"2022-03-23","cited_by_count":17,"area":"lr"},{"id":"2506.05295","title":"Sample Complexity and Representation Ability of Test-time Scaling Paradigms","created":"2025-06-05","cited_by_count":12,"area":"lr"},{"id":"2511.20038","title":"Softmax Transformers are Turing-Complete","created":"2025-11-25","cited_by_count":9,"area":"lr"},{"id":"2504.20213","title":"Can Large Language Models Learn Formal Logic? A Data-Driven Training and Evaluation Framework","created":"2025-04-28","cited_by_count":5,"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":"2512.00003","title":"Efficient Turing Machine Simulation with Transformers","created":"2025-09-28","cited_by_count":4,"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":"2606.00376","title":"The Deterministic Horizon: When Extended Reasoning Fails and Tool Delegation Becomes Necessary","created":"2026-05-29","cited_by_count":1,"area":"lr"},{"id":"2406.02864","title":"NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models","created":"2024-06-05","cited_by_count":0,"area":"lr"},{"id":"2503.03128","title":"Towards Understanding Multi-Round Large Language Model Reasoning: Approximability, Learnability and Generalizability","created":"2025-03-05","cited_by_count":0,"area":"lr"},{"id":"2604.02709","title":"Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy","created":"2026-04-03","cited_by_count":0,"area":"lr"},{"id":"2609.37312","title":"Hidden Reasoning Must Leak, but Need Not Be Readable: Fundamental Opportunities and Limits for Chain-of-Thought Monitoring","created":"2026-09-29","cited_by_count":0,"area":"lr"}],"next_cursor":null}