lom_id,title,year,cited_by_in_graph,cited_by_a_read_paper
arxiv:1905.10985,"Ai-gas: Ai-generating algorithms, an alternate paradigm for producing general artificial intelligence",2019,23,1
arxiv:2301.04104,Mastering diverse domains through world models,2024,22,1
arxiv:2001.08361,Scaling Laws for Neural Language Models,2020,22,0
arxiv:1710.05941,Searching for Activation Functions,2017,22,0
arxiv:2501.04227,Agent laboratory: Using llm agents as research assistants,2025,21,1
arxiv:2504.08066,The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search,2025,21,0
arxiv:2308.12950,Code Llama: Open Foundation Models for Code,2024,21,0
arxiv:2310.06770,"Swe-bench: Can language models resolve real-world github issues?, 2024",2024,21,1
arxiv:2401.04088,"Mixtral of experts, 2024",2024,21,1
arxiv:2402.03300,DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models,2024,21,0
arxiv:2403.07974,Livecodebench: Holistic and contamination free evaluation of large language models for code,2024,21,1
arxiv:2403.08295,Gemma: open models based on gemini research and technology,2024,21,0
arxiv:2406.11931,Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence,2024,21,1
arxiv:2412.19437,Deepseek-v3 technical report,2024,21,1
arxiv:2205.11916,Large language models are zero-shot reasoners,2023,21,1
arxiv:2210.03629,React: Synergizing reasoning and acting in language models,2023,21,1
arxiv:2301.08727,Neural Architecture Search: Insights from 1000 Papers,2023,21,0
arxiv:2302.13971,LLaMA: open and efficient foundation language models,2023,21,0
arxiv:2303.10158,Data-centric artificial intelligence: A survey,2023,21,1
arxiv:2306.08647,Language to rewards for robotic skill synthesis,2023,21,1
arxiv:2306.12672,From word models to world models: Translating from natural language to the probabilistic language of thought,2023,21,1
arxiv:2310.12931,Eureka: Human-level reward design via coding large language models,2023,21,1
arxiv:2312.09390,"Weak-to-strong generalization: Eliciting strong capabilities with weak supervision, 2023",2023,21,1
arxiv:1606.04671,Progressive Neural Networks,2022,21,0
arxiv:2108.07258,On the Opportunities and Risks of Foundation Models,2022,21,0
arxiv:2201.02177,Grokking: Generalization beyond overfitting on small algorithmic datasets,2022,21,1
arxiv:2203.11171,Self-consistency improves chain of thought reasoning in language models,2022,21,1
arxiv:2209.11895,In-context learning and induction heads,2022,21,1
arxiv:2210.05359,Mind’s eye: Grounded language model reasoning through simulation,2022,21,1
arxiv:2211.09085,Galactica: A Large Language Model for Science,2022,21,0
arxiv:2212.08073,Constitutional AI: Harmlessness from AI Feedback,2022,21,0
arxiv:2107.03374,Evaluating large language models trained on code,2021,21,1
arxiv:2107.12808,Open-Ended Learning Leads to Generally Capable Agents,2021,21,0
arxiv:2110.14168,Training verifiers to solve math word problems,2021,21,0
arxiv:2112.04359,Ethical and social risks of harm from Language Models,2021,21,0
arxiv:1912.01588,Leveraging Procedural Generation to Benchmark Reinforcement Learning,2020,21,0
arxiv:2005.01643,"Offline reinforcement learning: tutorial, review, and perspectives on open problems",2020,21,0
arxiv:1903.00742,Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research,2019,21,0
arxiv:1903.03176,MinAtar: An atari-inspired testbed for thorough and reproducible reinforcement learning experiments,2019,21,0
arxiv:1910.04098,Improving generalization in meta reinforcement learning using learned objectives,2019,21,1
arxiv:1708.07747,Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,2017,21,0
arxiv:1711.09846,Population based training of neural networks,2017,21,0
arxiv:1511.07289,Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs),2016,21,0
arxiv:2412.16720,Openai o1 system card,2024,20,1
arxiv:2201.11903,Chain-of-thought prompting elicits reasoning in large language models,2023,20,1
arxiv:2601.03267,Openai gpt-5 system card,2025,18,0
arxiv:2003.05325,Meta-learning curiosity algorithms,2020,18,1
arxiv:2211.09760,Velo: Training versatile learned optimizers by scaling up,2022,17,1
arxiv:2506.02153,Small Language Models are the Future of Agentic AI,2025,14,0
arxiv:2404.07738,"Researchagent: Iterative research idea generation over scientific literature with large language models, 2024",2024,14,1
arxiv:2506.13131,Alphaevolve: A coding agent for scientific and algorithmic discovery,2025,12,1
arxiv:2402.18679,Data interpreter: An llm agent for data science,2024,12,1
arxiv:2407.16741,Openhands: An open platform for ai software developers as generalist agents,2024,12,1
arxiv:2305.14259,"Scimon: Scientific inspiration machines optimized for novelty, 2024b",2023,12,1
arxiv:1812.08775,Deep paper gestalt,2018,12,1
arxiv:2310.03302,Mlagentbench: Evaluating language agents on machine learning experimentation,2024,11,1
arxiv:2410.07095,Mle-bench: Evaluating machine learning agents on machine learning engineering,2025,10,1
arxiv:2512.02556,DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models,2025,9,0
arxiv:2401.04259,"Marg: Multi-agent review generation for scientific papers, 2024",2024,9,1
arxiv:2312.02139,"Diffit: Diffusion vision transformers for image generation, 2024",2024,8,1
arxiv:2402.17453,Ds-agent: Automated data science by empowering large language models with case-based reasoning,2024,8,1
arxiv:2502.13138,Aide: Ai-driven exploration in the space of code,2025,7,1
arxiv:2402.00854,"Symbolicai: A framework for logic-based approaches combining generative models and solvers, 2024",2024,7,1
doi:10.1162/neco.1997.9.8.1735,Long Short-Term Memory,1997,7,0
arxiv:2410.02958,Automl-agent: A multi-agent llm framework for full-pipeline automl,2025,6,1
arxiv:2401.03065,"Cruxeval: A benchmark for code reasoning, understanding and execution",2024,6,1
arxiv:2402.09664,Codemind: Evaluating large language models for code reasoning,2024,6,1
arxiv:2402.18381,Large language models as evolution strategies,2024,6,1
arxiv:2412.04604,ARC Prize 2024: Technical Report,2025,5,0
arxiv:2502.14499,Mlgym: A new framework and benchmark for advancing ai research agents,2025,5,1
arxiv:2309.02726,"Large language models for automated open-domain scientific hypotheses discovery, 2024",2024,5,1
arxiv:2406.10252,"Autosurvey: Large language models can automatically write surveys, 2024c",2024,5,1
arxiv:2206.08896,"Evolution through large models, 2022",2022,5,1
doi:10.4230/oasics.dx.2024.16,Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper),2017,5,0
arxiv:2504.09737,Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025,2025,4,0
arxiv:2405.15568,"Omni-epic: Open-endedness via models of human notions of interestingness with environments programmed in code, 2024",2024,4,1
arxiv:2411.10478,Large language models for constructing and optimizing machine learning workflows: A survey,2024,4,1
doi:10.18653/v1/n19-1423,BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,2019,4,1
doi:10.4230/lipics.cp.2025.31,"HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case",2019,4,1
arxiv:1703.03400,Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks,2017,4,0
doi:10.1038/nature16961,Mastering the game of Go with deep neural networks and tree search,2016,4,0
arxiv:2411.01679,Autoformulation of mathematical optimization models using llms,2025,3,1
arxiv:2502.07316,Codei/o: Condensing reasoning patterns via code input-output prediction,2025,3,1
arxiv:2506.01372,Ai scientists fail without strong implementation capability,2025,3,1
arxiv:2507.01903,Ai4research: A survey of artificial intelligence for scientific research,2025,3,1
arxiv:2510.02387,Cwm: An open-weights llm for research on code generation with world models,2025,3,1
arxiv:2510.16872,Deepanalyze: Agentic large language models for autonomous data science,2025,3,1
arxiv:2512.09117,A categorical analysis of large language models and why llms circumvent the symbol grounding problem,2025,3,1
arxiv:2404.17605,"Autonomous llm-driven research from data to human-verifiable research papers, 2024",2024,3,1
arxiv:2406.08414,Discovering preference optimization algorithms with and for large language models,2024,3,1
arxiv:2407.01725,"Discoverybench: Towards data-driven discovery with large language models, 2024",2024,3,1
arxiv:2408.14033,Mlr-copilot: Autonomous machine learning research based on large language models agents,2024,3,1
arxiv:2410.17238,Sela: Tree-search enhanced llm agents for automated machine learning,2024,3,1
arxiv:1904.10509,Generating Long Sequences with Sparse Transformers,2019,3,0
openalex:W2990704537,SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems,2019,3,0
arxiv:1802.01561,IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures,2018,3,0
arxiv:1804.03720,Gotta Learn Fast: A New Benchmark for Generalization in RL,2018,3,0
doi:10.1038/nature24270,Mastering the game of Go without human knowledge,2017,3,0
doi:10.1007/s11263-015-0816-y,ImageNet Large Scale Visual Recognition Challenge,2015,3,0
doi:10.1038/nature14236,Human-level control through deep reinforcement learning,2015,3,0