{"total":25,"rows":[{"id":"2501.16265","title":"Training Dynamics of In-Context Learning in Linear Attention","created":"2025-01-27","cited_by_count":46,"area":null},{"id":"2410.02724","title":"Large Language Models as Markov Chains","created":"2024-10-03","cited_by_count":35,"area":null},{"id":"2403.08081","title":"Mechanics of Next Token Prediction with Self-Attention","created":"2024-03-12","cited_by_count":34,"area":null},{"id":"2411.12892","title":"Selective Attention: Enhancing Transformer through Principled Context Control","created":"2024-11-19","cited_by_count":16,"area":null},{"id":"2406.18400","title":"Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers","created":"2024-06-26","cited_by_count":15,"area":null},{"id":"2504.04308","title":"Gating is Weighting: Understanding Gated Linear Attention through In-context Learning","created":"2025-04-06","cited_by_count":12,"area":null},{"id":"2405.13718","title":"Next-token prediction capacity: general upper bounds and a lower bound for transformers","created":"2024-05-22","cited_by_count":11,"area":null},{"id":"2507.17657","title":"Attention (as Discrete-Time Markov) Chains","created":"2025-07-23","cited_by_count":11,"area":null},{"id":"2510.06954","title":"From Condensation to Rank Collapse: A Two-Stage Analysis of Transformer Training Dynamics","created":"2025-10-08","cited_by_count":10,"area":null},{"id":"2410.14581","title":"Optimizing Attention with Mirror Descent: Generalized Max-Margin Token Selection","created":"2024-10-18","cited_by_count":9,"area":null},{"id":"2509.08184","title":"Selective Induction Heads: How Transformers Select Causal Structures In Context","created":"2025-09-09","cited_by_count":8,"area":null},{"id":"2506.13688","title":"What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers","created":"2025-06-16","cited_by_count":7,"area":null},{"id":"2407.05591","title":"On the Power of Convolution Augmented Transformer","created":"2024-07-08","cited_by_count":6,"area":null},{"id":"2506.05249","title":"On the Convergence of Gradient Descent on Learning Transformers with Residual Connections","created":"2025-06-05","cited_by_count":6,"area":null},{"id":"2408.10491","title":"Achieving the Tightest Relaxation of Sigmoids for Formal Verification","created":"2024-08-20","cited_by_count":2,"area":null},{"id":"2409.13421","title":"State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?","created":"2024-09-20","cited_by_count":2,"area":null},{"id":"2510.23912","title":"Key and Value Weights Are Probably All You Need: On the Necessity of the Query, Key, Value weight Triplet in Self-Attention Transformers","created":"2025-10-27","cited_by_count":2,"area":null},{"id":"2511.23239","title":"Towards Understanding Transformers in Learning Random Walks","created":"2025-11-28","cited_by_count":2,"area":null},{"id":"2603.16068","title":"Resource Consumption Threats in Large Language Models","created":"2026-03-17","cited_by_count":2,"area":null},{"id":"2505.17282","title":"Attention with Trained Embeddings Provably Selects Important Tokens","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2604.10848","title":"Transformers Learn Latent Mixture Models In-Context via Mirror Descent","created":"2026-04-12","cited_by_count":1,"area":null},{"id":"2505.00818","title":"Dual Filter: A Transformer-like Inference Architecture for Hidden Markov Models","created":"2025-05-01","cited_by_count":0,"area":null},{"id":"2507.09378","title":"Context-Aware Regularization with Markovian Integration for Attention-Based Nucleotide Analysis","created":"2025-07-12","cited_by_count":0,"area":null},{"id":"2605.04308","title":"Memory as a Markov Matrix: Sample Efficient Knowledge Expansion via Token-to-Dictionary Mapping","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2607.22951","title":"Modeling Memory-Dependent Reliability of LLMs: A Hidden Markov Model","created":"2026-07-24","cited_by_count":0,"area":null}],"next_cursor":null}