{"total":36,"rows":[{"id":"2503.03961","title":"A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers","created":"2025-03-05","cited_by_count":53,"area":null},{"id":"2406.10727","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","created":"2024-06-15","cited_by_count":44,"area":null},{"id":"2406.06467","title":"How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad","created":"2024-06-10","cited_by_count":36,"area":null},{"id":"2505.19488","title":"Understanding Transformer from the Perspective of Associative Memory","created":"2025-05-26","cited_by_count":34,"area":null},{"id":"2504.15266","title":"Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction","created":"2025-04-21","cited_by_count":29,"area":null},{"id":"2412.04703","title":"Transformers Struggle to Learn to Search","created":"2024-12-06","cited_by_count":19,"area":null},{"id":"2407.01214","title":"Revisiting Random Walks for Learning on Graphs","created":"2024-07-01","cited_by_count":13,"area":null},{"id":"2507.12549","title":"The Serial Scaling Hypothesis","created":"2025-07-16","cited_by_count":13,"area":null},{"id":"2503.01805","title":"Depth-Width tradeoffs in Algorithmic Reasoning of Graph Tasks with Transformers","created":"2025-03-03","cited_by_count":11,"area":null},{"id":"2602.03655","title":"Sequential Group Composition: A Window into the Mechanics of Deep Learning","created":"2026-02-03","cited_by_count":10,"area":null},{"id":"2505.24067","title":"Primal-Dual Neural Algorithmic Reasoning","created":"2025-05-29","cited_by_count":9,"area":null},{"id":"2410.01985","title":"Lost-in-Distance: Impact of Contextual Proximity on LLM Performance in Graph Tasks","created":"2024-10-02","cited_by_count":8,"area":null},{"id":"2501.02393","title":"Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers","created":"2025-01-04","cited_by_count":7,"area":null},{"id":"2411.15671","title":"Best of Both Worlds: Advantages of Hybrid Graph Sequence Models","created":"2024-11-23","cited_by_count":6,"area":null},{"id":"2510.00368","title":"The Transformer Cookbook","created":"2025-10-01","cited_by_count":6,"area":null},{"id":"2505.14840","title":"Subquadratic Algorithms and Hardness for Attention with Any Temperature","created":"2025-05-20","cited_by_count":5,"area":null},{"id":"2507.07544","title":"Position: We Need An Algorithmic Understanding of Generative AI","created":"2025-07-10","cited_by_count":5,"area":null},{"id":"2509.22613","title":"Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspective","created":"2025-09-26","cited_by_count":4,"area":null},{"id":"2411.13028","title":"A Theory for Compressibility of Graph Transformers for Transductive Learning","created":"2024-11-20","cited_by_count":3,"area":null},{"id":"2503.10542","title":"Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More More","created":"2025-03-13","cited_by_count":3,"area":null},{"id":"2506.05393","title":"Are Large Language Models Good Temporal Graph Learners?","created":"2025-06-03","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":"2411.07467","title":"Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes","created":"2024-11-12","cited_by_count":2,"area":null},{"id":"2511.08028","title":"Generalizable Insights for Graph Transformers in Theory and Practice","created":"2025-11-11","cited_by_count":2,"area":null},{"id":"2605.22471","title":"Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers","created":"2026-05-21","cited_by_count":2,"area":null},{"id":"2503.06917","title":"Sample-Efficient Optimization over Generative Priors via Coarse Learnability","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2506.20575","title":"Exploring Graph-Transformer Out-of-Distribution Generalization Abilities","created":"2025-06-25","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":"2602.10386","title":"Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2605.06239","title":"When Graph Language Models Go Beyond Memorization","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2602.01553","title":"Plain Transformers are Surprisingly Powerful Link Predictors","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2602.13155","title":"Learning to Approximate Uniform Facility Location via Graph Neural Networks","created":"2026-02-13","cited_by_count":0,"area":null},{"id":"2606.06865","title":"Are Large Language Models Suitable for Graph Computation? Progress and Prospects","created":"2026-06-05","cited_by_count":0,"area":null},{"id":"2609.38446","title":"What Pretraining and Midtraining Make Learnable from Rewards?","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.14456","title":"Learning to Extrapolate in Depth with Looped Transformers","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.15185","title":"DeltaNet-PC: Test-Time Memory Without Backpropagation Through Time","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}