{"total":62,"rows":[{"id":"2307.05782","title":"Large Language Models","created":"2023-07-11","cited_by_count":780,"area":null},{"id":"2307.15771","title":"The Hydra Effect: Emergent Self-repair in Language Model Computations","created":"2023-07-28","cited_by_count":156,"area":null},{"id":"2311.00208","title":"What Formal Languages Can Transformers Express? A Survey","created":"2023-11-01","cited_by_count":124,"area":null},{"id":"2306.01129","title":"White-Box Transformers via Sparse Rate Reduction","created":"2023-06-01","cited_by_count":80,"area":null},{"id":"2306.00946","title":"Exposing Attention Glitches with Flip-Flop Language Modeling","created":"2023-06-01","cited_by_count":75,"area":null},{"id":"2401.07102","title":"Evolving Code with A Large Language Model","created":"2024-01-13","cited_by_count":43,"area":null},{"id":"2512.22382","title":"Completed Hyperparameter Transfer across Modules, Width, Depth, Batch and Duration","created":"2025-12-26","cited_by_count":27,"area":null},{"id":"2403.06949","title":"Materials science in the era of large language models: a perspective","created":"2024-03-11","cited_by_count":24,"area":null},{"id":"2312.06937","title":"Can a Transformer Represent a Kalman Filter?","created":"2023-12-12","cited_by_count":20,"area":null},{"id":"2502.01637","title":"Scaling Embedding Layers in Language Models","created":"2025-02-03","cited_by_count":20,"area":null},{"id":"2305.14699","title":"Can Transformers Learn to Solve Problems Recursively?","created":"2023-05-24","cited_by_count":17,"area":null},{"id":"2410.01537","title":"Attention layers provably solve single-location regression","created":"2024-10-02","cited_by_count":17,"area":null},{"id":"2306.05808","title":"RankFormer: Listwise Learning-to-Rank Using Listwide Labels","created":"2023-06-09","cited_by_count":9,"area":null},{"id":"2306.12584","title":"Hierarchical Neural Simulation-Based Inference Over Event Ensembles","created":"2023-06-21","cited_by_count":9,"area":null},{"id":"2308.12890","title":"Large Language Models Vote: Prompting for Rare Disease Identification","created":"2023-08-24","cited_by_count":9,"area":null},{"id":"2406.19997","title":"Wavelets Are All You Need for Autoregressive Image Generation","created":"2024-06-28","cited_by_count":9,"area":null},{"id":"2402.05424","title":"Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures","created":"2024-02-08","cited_by_count":8,"area":null},{"id":"2305.16192","title":"Explainability Techniques for Chemical Language Models","created":"2023-05-25","cited_by_count":6,"area":null},{"id":"2409.06142","title":"Variational Search Distributions","created":"2024-09-10","cited_by_count":6,"area":null},{"id":"2303.15190","title":"Evaluating self-attention interpretability through human-grounded experimental protocol","created":"2023-03-27","cited_by_count":5,"area":null},{"id":"2603.01514","title":"Training Dynamics of Softmax Self-Attention: Fast Global Convergence via Preconditioning","created":"2026-03-02","cited_by_count":5,"area":null},{"id":"2408.07802","title":"Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference","created":"2024-08-14","cited_by_count":4,"area":null},{"id":"2506.15881","title":"T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders","created":"2025-06-18","cited_by_count":4,"area":null},{"id":"2504.18048","title":"Modes of Sequence Models and Learning Coefficients","created":"2025-04-25","cited_by_count":3,"area":null},{"id":"2505.13112","title":"Attention-based clustering","created":"2025-05-19","cited_by_count":3,"area":null},{"id":"2510.25240","title":"Generative Bayesian Optimization: Generative Models as Acquisition Functions","created":"2025-10-29","cited_by_count":3,"area":null},{"id":"2603.09571","title":"An Optimal Control Approach To Transformer Training","created":"2026-03-10","cited_by_count":3,"area":null},{"id":"2307.16005","title":"Enhancing Object Detection in Ancient Documents with Synthetic Data Generation and Transformer-Based Models","created":"2023-07-29","cited_by_count":2,"area":null},{"id":"2311.08533","title":"Natural Language Processing for Financial Regulation","created":"2023-11-14","cited_by_count":2,"area":null},{"id":"2408.10818","title":"Learning Randomized Algorithms with Transformers","created":"2024-08-20","cited_by_count":2,"area":null},{"id":"2410.05353","title":"Towards a Categorical Foundation of Deep Learning: A Survey","created":"2024-10-07","cited_by_count":2,"area":null},{"id":"2211.04979","title":"Perceived personality state estimation in dyadic and small group interaction with deep learning methods","created":"2022-11-09","cited_by_count":1,"area":null},{"id":"2304.10557","title":"An Introduction to Transformers","created":"2023-04-20","cited_by_count":1,"area":null},{"id":"2408.10277","title":"Increasing transformer token length with a Maximum Entropy Principle Method","created":"2024-08-17","cited_by_count":1,"area":null},{"id":"2409.17502","title":"Broadcast Product: Redefining Shape-aligned Element-wise Multiplication and Beyond","created":"2024-09-26","cited_by_count":1,"area":null},{"id":"2410.19370","title":"Notes on the Mathematical Structure of GPT LLM Architectures","created":"2024-10-25","cited_by_count":1,"area":null},{"id":"2504.11830","title":"Emergence of Computational Structure in a Neural Network Physics Simulator","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2510.21052","title":"Amortized Active Generation of Pareto Sets","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2603.16840","title":"What DINO saw: ALiBi positional encoding reduces positional bias in Vision Transformers","created":"2026-03-17","cited_by_count":1,"area":null},{"id":"2605.08679","title":"Attention-based graph neural networks: a survey","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2211.09812","title":"GAMMT: Generative Ambiguity Modeling Using Multiple Transformers","created":"2022-11-16","cited_by_count":0,"area":null},{"id":"2302.01834","title":"Coinductive guide to inductive transformer heads","created":"2023-02-03","cited_by_count":0,"area":null},{"id":"2302.09327","title":"Transformadores: Fundamentos teoricos y Aplicaciones","created":"2023-02-18","cited_by_count":0,"area":null},{"id":"2306.03408","title":"Agents Explore the Environment Beyond Good Actions to Improve Their Model for Better Decisions","created":"2023-06-06","cited_by_count":0,"area":null},{"id":"2309.09934","title":"Hierarchical Attention and Graph Neural Networks: Toward Drift-Free Pose Estimation","created":"2023-09-18","cited_by_count":0,"area":null},{"id":"2402.08830","title":"Sequence graphs realizations and ambiguity in language models","created":"2024-02-13","cited_by_count":0,"area":null},{"id":"2403.09451","title":"M&M: Multimodal-Multitask Model Integrating Audiovisual Cues in Cognitive Load Assessment","created":"2024-03-14","cited_by_count":0,"area":null},{"id":"2405.09061","title":"Improving Transformers using Faithful Positional Encoding","created":"2024-05-15","cited_by_count":0,"area":null},{"id":"2406.08686","title":"Opportunities in deep learning methods development for computational biology","created":"2024-06-12","cited_by_count":0,"area":null},{"id":"2410.16302","title":"Computational design of target-specific linear peptide binders with TransformerBeta","created":"2024-10-07","cited_by_count":0,"area":null},{"id":"2501.13188","title":"Topological constraints on self-organisation in locally interacting systems","created":"2025-01-22","cited_by_count":0,"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":"2508.20211","title":"What can we learn from signals and systems in a transformer? Insights for probabilistic modeling and inference architecture","created":"2025-08-27","cited_by_count":0,"area":null},{"id":"2508.21307","title":"MultiFluxAI Enhancing Platform Engineering with Advanced Agent-Orchestrated Retrieval Systems","created":"2025-08-29","cited_by_count":0,"area":null},{"id":"2511.11754","title":"Batch Transformer Architecture: Case of Synthetic Image Generation for Emotion Expression Facial Recognition","created":"2025-11-13","cited_by_count":0,"area":null},{"id":"2602.02521","title":"Scaled Dot-Product Attention implements projection of inputs onto a common surface","created":"2026-01-25","cited_by_count":0,"area":null},{"id":"2602.07562","title":"Gaussian Match-and-Copy: A Minimalist Benchmark for Studying Transformer Induction","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2604.07242","title":"Weaves, Wires, and Morphisms: Formalizing and Implementing the Algebra of Deep Learning","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2605.06394","title":"Lecture Notes on Statistical Physics and Neural Networks","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.15608","title":"Transformer-like Inference from Optimal Control","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2610.03947","title":"Synthesizing Physics Formulae with Transformers","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.09760","title":"Leaner Transformers Can Easily Learn to Cluster","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}