{"list":"other","total":55,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"1707.06347","title":"Proximal Policy Optimization Algorithms","created":"2017-07-20","cited_by_count":15215},{"id":"2201.11903","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","created":"2022-01-28","cited_by_count":12459},{"id":"2402.03300","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","created":"2024-02-05","cited_by_count":9379},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"2111.00396","title":"Efficiently Modeling Long Sequences with Structured State Spaces","created":"2021-10-31","cited_by_count":2465},{"id":"1901.02860","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","created":"2019-01-09","cited_by_count":1850},{"id":"2312.08935","title":"Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations","created":"2023-12-14","cited_by_count":928},{"id":"2211.14275","title":"Solving math word problems with process- and outcome-based feedback","created":"2022-11-25","cited_by_count":901},{"id":"2308.09687","title":"Graph of Thoughts: Solving Elaborate Problems with Large Language Models","created":"2023-08-18","cited_by_count":886},{"id":"2412.06769","title":"Training Large Language Models to Reason in a Continuous Latent Space","created":"2024-12-09","cited_by_count":797},{"id":"1911.05507","title":"Compressive Transformers for Long-Range Sequence Modelling","created":"2019-11-13","cited_by_count":726},{"id":"2204.06745","title":"GPT-NeoX-20B: An Open-Source Autoregressive Language Model","created":"2022-04-14","cited_by_count":713},{"id":"1807.03819","title":"Universal Transformers","created":"2018-07-10","cited_by_count":647},{"id":"1410.5401","title":"Neural Turing Machines","created":"2014-10-20","cited_by_count":644},{"id":"1603.08983","title":"Adaptive Computation Time for Recurrent Neural Networks","created":"2016-03-29","cited_by_count":506},{"id":"2305.18654","title":"Faith and Fate: Limits of Transformers on Compositionality","created":"2023-05-29","cited_by_count":503},{"id":"1912.10077","title":"Are Transformers universal approximators of sequence-to-sequence functions?","created":"2019-12-20","cited_by_count":406},{"id":"1410.3916","title":"Memory Networks","created":"2014-10-15","cited_by_count":363},{"id":"2404.19737","title":"Better & Faster Large Language Models via Multi-token Prediction","created":"2024-04-30","cited_by_count":360},{"id":"2305.14788","title":"Adapting Language Models to Compress Contexts","created":"2023-05-24","cited_by_count":356},{"id":"2305.15408","title":"Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective","created":"2023-05-24","cited_by_count":294},{"id":"2207.02098","title":"Neural Networks and the Chomsky Hierarchy","created":"2022-07-05","cited_by_count":217},{"id":"2207.06881","title":"Recurrent Memory Transformer","created":"2022-07-14","cited_by_count":203},{"id":"2207.00729","title":"The Parallelism Tradeoff: Limitations of Log-Precision Transformers","created":"2022-07-02","cited_by_count":193},{"id":"2404.15758","title":"Let's Think Dot by Dot: Hidden Computation in Transformer Language Models","created":"2024-04-24","cited_by_count":191},{"id":"2404.08819","title":"The Illusion of State in State-Space Models","created":"2024-04-12","cited_by_count":187},{"id":"2403.04642","title":"Teaching Large Language Models to Reason with Reinforcement Learning","created":"2024-03-07","cited_by_count":160},{"id":"2311.12424","title":"Looped Transformers are Better at Learning Learning Algorithms","created":"2023-11-21","cited_by_count":149},{"id":"2503.14456","title":"RWKV-7 \"Goose\" with Expressive Dynamic State Evolution","created":"2025-03-18","cited_by_count":141},{"id":"2106.16213","title":"Saturated Transformers are Constant-Depth Threshold Circuits","created":"2021-06-30","cited_by_count":139},{"id":"2306.02896","title":"Representational Strengths and Limitations of Transformers","created":"2023-06-05","cited_by_count":133},{"id":"2311.00208","title":"What Formal Languages Can Transformers Express? A Survey","created":"2023-11-01","cited_by_count":124},{"id":"2409.13373","title":"LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench","created":"2024-09-20","cited_by_count":94},{"id":"2006.09286","title":"On the Computational Power of Transformers and its Implications in Sequence Modeling","created":"2020-06-16","cited_by_count":85},{"id":"2402.09268","title":"Transformers, parallel computation, and logarithmic depth","created":"2024-02-14","cited_by_count":79},{"id":"1809.02942","title":"Cellular automata as convolutional neural networks","created":"2018-09-09","cited_by_count":45},{"id":"2110.14237","title":"Learning Graph Cellular Automata","created":"2021-10-27","cited_by_count":33},{"id":"2211.01233","title":"Attention-based Neural Cellular Automata","created":"2022-11-02","cited_by_count":26},{"id":"2406.14197","title":"On the Representational Capacity of Neural Language Models with Chain-of-Thought Reasoning","created":"2024-06-20","cited_by_count":26},{"id":"2405.08644","title":"Thinking Tokens for Language Modeling","created":"2024-05-14","cited_by_count":24},{"id":"2502.08482","title":"Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning","created":"2025-02-12","cited_by_count":19},{"id":"2409.09239","title":"Autoregressive + Chain of Thought = Recurrent: Recurrence's Role in Language Models' Computability and a Revisit of Recurrent Transformer","created":"2024-09-14","cited_by_count":16},{"id":"2009.01398","title":"It's Hard for Neural Networks To Learn the Game of Life","created":"2020-09-03","cited_by_count":13},{"id":"2301.10497","title":"E(n)-equivariant Graph Neural Cellular Automata","created":"2023-01-25","cited_by_count":9},{"id":"2401.17169","title":"Conditional and Modal Reasoning in Large Language Models","created":"2024-01-30","cited_by_count":9},{"id":"2406.12546","title":"Liar, Liar, Logical Mire: A Benchmark for Suppositional Reasoning in Large Language Models","created":"2024-06-18","cited_by_count":9},{"id":"2310.14809","title":"Learning spatio-temporal patterns with Neural Cellular Automata","created":"2023-10-23","cited_by_count":8},{"id":"2409.12182","title":"LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata","created":"2024-09-03","cited_by_count":8},{"id":"2103.14886","title":"Generalization over different cellular automata rules learned by a deep feed-forward neural network","created":"2021-03-27","cited_by_count":6},{"id":"2012.02179","title":"Reconstructing cellular automata rules from observations at nonconsecutive times","created":"2020-12-03","cited_by_count":5},{"id":"2404.06460","title":"Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction","created":"2024-04-09","cited_by_count":5},{"id":"2404.05259","title":"Cellular automata, many-valued logic, and deep neural networks","created":"2024-04-08","cited_by_count":2},{"id":"2408.12778","title":"Data-Centric Approach to Constrained Machine Learning: A Case Study on Conway's Game of Life","created":"2024-08-23","cited_by_count":2}],"next_cursor":null}