arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?
By Anirudh Ajith, Amanpreet Singh, Jay DeYoung, Nadav Kunievsky, Austin C. Kozlowski, Oyvind Tafjord, James Evans, Daniel S. Weld, Tom Hope, Doug Downey
arXiv:2606. 15497v1 Announce Type: new Abstract: The automation of science is a long-standing ambition in the field of AI.
By Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Chris Lu, Shengran Hu, Jakob Foerster, David Ha, Jeff Clune
arXiv:2605. 16902v2 Announce Type: replace Abstract: Scientific artifacts such as models and datasets are foundations for research.
By Haofei Yu, Jiaxuan You, Peter Clark, Bodhisattwa Prasad Majumder, Kyle Richardson
The paper proposes a new method for credit scoring research articles that distinguishes between a paper’s original contribution and the prior work it builds upon. It introduces a hierarchical ‘contribution tree’ framework that conserves importance across a document’s structure and separates original from citation-derived credit. Large language models are employed as noisy comparative estimators to scale the analysis, and the approach is extended to collections of articles via weighted citation graphs to produce corpus-level contributions and normalized influence scores.
By Sana Ebrahimi, Suraj Shetiya, Abolfazl Asudeh
The paper presents a SciBERT-based method for automatically classifying scientific papers into four telescope-related categories—science, instrumentation, mention, and not telescope—within strict 512-token limits. Despite truncation challenges, the approach achieved a macro F1 score of 0.89, topping the WASP-2025 leaderboard. The authors analyze truncation effects, compare chunking and long-context models, and offer insights into efficient scientific text curation.
By Madhusudhana Naidu
Modelpedia is an automated, LLM-assisted framework that extracts and organizes findings about AI models from published papers into a searchable public catalog. It links each finding to the relevant model, dataset, method, and concept, and has already extracted over a thousand findings from ICLR 2024 and 2025 papers. The authors invite the community to explore, contribute to, and build on this open catalog, positioning model findings as a shared foundation for the meta‑science of AI.
By Franciszek Bernat (Centre for Credible AI, Warsaw University of Technology), Dawid P{\l}udowski (Centre for Credible AI, Warsaw University of Technology), Micha{\l} Jan W{\l}odarczyk (Centre for Credible AI, Warsaw University of Technology), Luca Longo (University College Cork), Jianlong Zhou (University of Technology Sydney), Andreas Holzinger (Human-Centered AI Lab), Riccardo Guidotti (University of Pisa, ISTI-CNR), Wojciech Samek (Technical University of Berlin, Berlin Institute for the Foundations of Learning and Data), Przemys{\l}aw Biecek (Centre for Credible AI, University of Warsaw)