lom_id,title,year,claim_domains,domains
arxiv:2309.02726,"Large language models for automated open-domain scientific hypotheses discovery, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2310.06770,"Swe-bench: Can language models resolve real-world github issues?, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2312.02139,"Diffit: Diffusion vision transformers for image generation, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2401.04088,"Mixtral of experts, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2401.04259,"Marg: Multi-agent review generation for scientific papers, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2402.00854,"Symbolicai: A framework for logic-based approaches combining generative models and solvers, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2402.18381,Large language models as evolution strategies,2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2404.07738,"Researchagent: Iterative research idea generation over scientific literature with large language models, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2404.15794,Large language models as in-context ai generators for quality-diversity,2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2404.17605,"Autonomous llm-driven research from data to human-verifiable research papers, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2405.03547,"Position paper: Leveraging foundational models for black-box optimization: Benefits, challenges, and future directions",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2405.15143,"Intelligent go-explore: Standing on the shoulders of giant foundation models, 2024b",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2405.15568,"Omni-epic: Open-endedness via models of human notions of interestingness with environments programmed in code, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2406.08414,Discovering preference optimization algorithms with and for large language models,2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2406.10252,"Autosurvey: Large language models can automatically write surveys, 2024c",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2406.11931,Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence,2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2407.01725,"Discoverybench: Towards data-driven discovery with large language models, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2407.21783,"The llama 3 herd of models, 2024",2024,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2305.14259,"Scimon: Scientific inspiration machines optimized for novelty, 2024b",2023,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2306.08647,Language to rewards for robotic skill synthesis,2023,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2310.12931,Eureka: Human-level reward design via coding large language models,2023,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2312.09390,"Weak-to-strong generalization: Eliciting strong capabilities with weak supervision, 2023",2023,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2201.02177,Grokking: Generalization beyond overfitting on small algorithmic datasets,2022,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2203.11171,Self-consistency improves chain of thought reasoning in language models,2022,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2206.08896,"Evolution through large models, 2022",2022,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2209.11895,In-context learning and induction heads,2022,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2211.09760,Velo: Training versatile learned optimizers by scaling up,2022,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2107.03374,Evaluating large language models trained on code,2021,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:2003.05325,Meta-learning curiosity algorithms,2020,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:1905.10985,"Ai-gas: Ai-generating algorithms, an alternate paradigm for producing general artificial intelligence",2019,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:1910.04098,Improving generalization in meta reinforcement learning using learned objectives,2019,2,"CS / ML / automated science,metascience / TeamScience ops"
doi:10.18653/v1/n19-1421,CommonsenseQA: A question answering challenge targeting commonsense knowledge,2019,2,"CS / ML / automated science,metascience / TeamScience ops"
arxiv:1812.08775,Deep paper gestalt,2018,2,"CS / ML / automated science,metascience / TeamScience ops"
openalex:W3017344694,(untitled in OpenAlex),,2,"climate / NLP / claim verification,CS / NLP / metascience"