{"total":33,"rows":[{"id":"2311.00208","title":"What Formal Languages Can Transformers Express? A Survey","created":"2023-11-01","cited_by_count":124,"area":null},{"id":"2310.13897","title":"Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages","created":"2023-10-21","cited_by_count":49,"area":null},{"id":"2404.04393","title":"Counting Like Transformers: Compiling Temporal Counting Logic Into Softmax Transformers","created":"2024-04-05","cited_by_count":39,"area":null},{"id":"2412.09925","title":"Simulating Hard Attention Using Soft Attention","created":"2024-12-13","cited_by_count":15,"area":null},{"id":"2505.23623","title":"Characterizing the Expressivity of Fixed-Precision Transformer Language Models","created":"2025-05-29","cited_by_count":14,"area":null},{"id":"2402.05359","title":"An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models","created":"2024-02-08","cited_by_count":12,"area":null},{"id":"2506.16055","title":"Knee-Deep in C-RASP: A Transformer Depth Hierarchy","created":"2025-06-19","cited_by_count":12,"area":null},{"id":"2503.14615","title":"Unique Hard Attention: A Tale of Two Sides","created":"2025-03-18","cited_by_count":10,"area":null},{"id":"2505.11199","title":"The Counting Power of Transformers","created":"2025-05-16","cited_by_count":9,"area":null},{"id":"2404.02040","title":"Transformers as Transducers","created":"2024-04-02","cited_by_count":8,"area":null},{"id":"2510.00368","title":"The Transformer Cookbook","created":"2025-10-01","cited_by_count":6,"area":null},{"id":"2410.10101","title":"Learning Linear Attention in Polynomial Time","created":"2024-10-14","cited_by_count":5,"area":null},{"id":"2602.05896","title":"Parity, Sensitivity, and Transformers","created":"2026-02-05","cited_by_count":5,"area":null},{"id":"2510.19315","title":"Transformers are Inherently Succinct","created":"2025-10-22","cited_by_count":4,"area":null},{"id":"2601.01754","title":"Context-Free Recognition with Transformers","created":"2026-01-05","cited_by_count":4,"area":null},{"id":"2603.02238","title":"Length Generalization Bounds for Transformers","created":"2026-02-13","cited_by_count":4,"area":null},{"id":"2405.04515","title":"A Transformer with Stack Attention","created":"2024-05-07","cited_by_count":2,"area":null},{"id":"2505.11930","title":"The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics","created":"2025-05-17","cited_by_count":2,"area":null},{"id":"2510.27118","title":"Probability Distributions Computed by Autoregressive Transformers","created":"2025-10-31","cited_by_count":2,"area":null},{"id":"2601.19467","title":"On the Expressiveness of State Space Models via Temporal Logics","created":"2026-01-27","cited_by_count":2,"area":null},{"id":"2506.18739","title":"On the Existence of Universal Simulators of Attention","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2509.23912","title":"From Neural Networks to Logical Theories: The Correspondence between Fibring Modal Logics and Fibring Neural Networks","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2509.24024","title":"The Role of Logic and Automata in Understanding Transformers","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2606.17522","title":"An expressivity analysis of hierarchical modelling in deep transformers via bounded-depth grammars","created":"2026-06-16","cited_by_count":1,"area":null},{"id":"2506.03370","title":"Comparison of different Unique hard attention transformer models by the formal languages they can recognize","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2602.16473","title":"Synthesis and Verification of Transformer Programs (Technical Report)","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2605.04683","title":"Average Attention Transformers and Arithmetic Circuits","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.07705","title":"Encoder-Decoder Transformers: Logical Characterizations and Periodicity","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.10393","title":"The Polynomial Counting Capabilities of Message Passing Neural Networks","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.13687","title":"A Hierarchical Language Model with Predictable Scaling Laws and Provable Benefits of Reasoning","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.16640","title":"Provably Shorter Scratchpads in Hybrid DeltaNet-Attention Decoders","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2608.12671","title":"On the Expressive Power of Transformers","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2610.20337","title":"Training Dynamics of Transformers on Unbounded Inputs: Attention Learning Can Lag Output Layer Learning","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}