{"total":45,"rows":[{"id":"2405.17394","title":"The Expressive Capacity of State Space Models: A Formal Language Perspective","created":"2024-05-27","cited_by_count":46,"area":null},{"id":"2412.02975","title":"Theoretical limitations of multi-layer Transformer","created":"2024-12-04","cited_by_count":45,"area":null},{"id":"2405.01745","title":"Large Language Models for UAVs: Current State and Pathways to the Future","created":"2024-05-02","cited_by_count":33,"area":null},{"id":"2408.14332","title":"One-layer transformers fail to solve the induction heads task","created":"2024-08-26","cited_by_count":32,"area":null},{"id":"2407.15160","title":"When Can Transformers Count to n?","created":"2024-07-21","cited_by_count":24,"area":null},{"id":"2504.14370","title":"Density Measures for Language Generation","created":"2025-04-19","cited_by_count":21,"area":null},{"id":"2412.02682","title":"The Asymptotic Behavior of Attention in Transformers","created":"2024-12-03","cited_by_count":16,"area":null},{"id":"2507.07996","title":"Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs","created":"2025-07-10","cited_by_count":16,"area":null},{"id":"2511.05295","title":"Language Generation and Identification From Partial Enumeration: Tight Density Bounds and Topological Characterizations","created":"2025-11-07","cited_by_count":13,"area":null},{"id":"2501.19215","title":"Strassen Attention, Split VC Dimension and Compositionality in Transformers","created":"2025-01-31","cited_by_count":12,"area":null},{"id":"2502.16792","title":"The Role of Sparsity for Length Generalization in Transformers","created":"2025-02-24","cited_by_count":8,"area":null},{"id":"2505.21024","title":"Pause Tokens Strictly Increase the Expressivity of Constant-Depth Transformers","created":"2025-05-27","cited_by_count":8,"area":null},{"id":"2406.15275","title":"How language models extrapolate outside the training data: A case study in Textualized Gridworld","created":"2024-06-21","cited_by_count":7,"area":null},{"id":"2506.05200","title":"Transformers Meet In-Context Learning: A Universal Approximation Theory","created":"2025-06-05","cited_by_count":6,"area":null},{"id":"2510.05364","title":"The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures","created":"2025-10-06","cited_by_count":4,"area":null},{"id":"2603.08859","title":"Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models","created":"2026-03-09","cited_by_count":4,"area":null},{"id":"2409.18028","title":"Compositional Hardness of Code in Large Language Models -- A Probabilistic Perspective","created":"2024-09-26","cited_by_count":3,"area":null},{"id":"2412.20195","title":"Lower bounds on transformers with infinite precision","created":"2024-12-28","cited_by_count":3,"area":null},{"id":"2501.02535","title":"A completely uniform transformer for parity","created":"2025-01-05","cited_by_count":3,"area":null},{"id":"2501.15857","title":"Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?","created":"2025-01-27","cited_by_count":3,"area":null},{"id":"2503.22076","title":"Concise One-Layer Transformers Can Do Function Evaluation (Sometimes)","created":"2025-03-28","cited_by_count":3,"area":null},{"id":"2509.09001","title":"Fast attention mechanisms: a tale of parallelism","created":"2025-09-10","cited_by_count":3,"area":null},{"id":"2510.24831","title":"The Narrative Continuity Test: A Conceptual Framework for Evaluating Identity Persistence in AI Systems","created":"2025-10-28","cited_by_count":3,"area":null},{"id":"2602.11175","title":"Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth","created":"2026-01-19","cited_by_count":3,"area":null},{"id":"2604.17935","title":"How Much Cache Does Reasoning Need? Depth-Cache Tradeoffs in KV-Compressed Transformers","created":"2026-04-20","cited_by_count":3,"area":null},{"id":"2502.13953","title":"Benchmarking graph construction by large language models for coherence-driven inference","created":"2025-02-19","cited_by_count":2,"area":null},{"id":"2607.22361","title":"Indexing: the Beginning and the End","created":"2026-07-24","cited_by_count":2,"area":null},{"id":"2409.20054","title":"Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis","created":"2024-09-30","cited_by_count":1,"area":null},{"id":"2502.06038","title":"Provably Overwhelming Transformer Models with Designed Inputs","created":"2025-02-09","cited_by_count":1,"area":null},{"id":"2503.04848","title":"Three tiers of computation in transformers and in brain architectures","created":"2025-03-05","cited_by_count":1,"area":null},{"id":"2601.14175","title":"A model of errors in transformers","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2602.01763","title":"A Provable Expressiveness Hierarchy in Hybrid Linear-Full Attention","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2602.02707","title":"Every Bit Counts: A Theoretical Study of Precision-Expressivity Tradeoffs in Quantized Transformers","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2605.31558","title":"Positional versus Symbolic Attention Heads: Learning Dynamics, RoPE Geometry, and Length Generalization","created":"2026-05-29","cited_by_count":1,"area":null},{"id":"2608.04243","title":"Attention-based representations for multi-task computation","created":"2026-08-04","cited_by_count":1,"area":null},{"id":"2411.08003","title":"Can adversarial attacks by large language models be attributed?","created":"2024-11-12","cited_by_count":0,"area":null},{"id":"2509.18520","title":"Coherence-driven inference for cybersecurity","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2511.01363","title":"Automatic Minds: Cognitive Parallels Between Hypnotic States and Large Language Model Processing","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2511.16191","title":"CausalMamba: Interpretable State Space Modeling for Temporal Rumor Causality","created":"2025-11-20","cited_by_count":0,"area":null},{"id":"2602.01451","title":"Understanding QA generation: Extracting Parametric and Contextual Knowledge with CQA for Low Resource Bangla Language","created":"2026-02-01","cited_by_count":0,"area":null},{"id":"2603.11133","title":"Beyond Pairwise Attention: Higher-Order Modular Attention for Efficient Sequence Learning","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2603.11332","title":"On the Computational Hardness of Transformers","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2604.22951","title":"The Power of Power Law: Asymmetry Enables Compositional Reasoning","created":"2026-04-24","cited_by_count":0,"area":null},{"id":"2608.11066","title":"Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2608.26145","title":"LLMs for Academic Workflows: An Evaluation of Literature Reviews Generated with Short and Long Context Windows of LLMs","created":"2026-06-28","cited_by_count":0,"area":null}],"next_cursor":null}