Papers by Daniel S Weld
Ai2 Scholar QA: Organized Literature Synthesis with Attribution (2025.acl-demo)
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Amanpreet Singh, Joseph Chee Chang, Dany Haddad, Aakanksha Naik, Jena D. Hwang, Rodney Kinney, Daniel S Weld, Doug Downey, Sergey Feldman
| Challenge: | Ai2 Scholar QA is a free online scientific question answering application . it uses retrieval-augmented generation to answer complex scientific questions . many of these systems are expensive to use and closed-source . |
| Approach: | They propose a retrieval-augmented generation-based scientific question answering application . it uses a Python package and an interactive web app to make the entire pipeline public . they compare it with other similar question-answering applications . |
| Outcome: | The proposed system outperforms other systems on a recent scientific QA benchmark. |
Generating Literature-Driven Scientific Theories at Scale (2026.acl-long)
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| Challenge: | Contemporary automated scientific discovery systems focus on generating experiments, but higher-level activities such as theory building remain underexplored. |
| Approach: | They propose to synthesize theories from scientific literature using literature-grounding versus parametric knowledge. |
| Outcome: | The proposed method matches existing evidence better than parametric LLM memory generation. |
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation (2025.findings-acl)
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Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue, Tom Hope, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Daniel S Weld, Peter Clark
| Challenge: | Automated scientific discovery (ASD) systems are limited in their evaluation of software artifacts and large volumes of research artifs are typically evaluated using conference-style paper review with limited evaluation of code. |
| Approach: | They propose a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain. |
| Outcome: | The proposed system returns 19 discoveries on machine-generated ideas in the domain of agents and virtual environments. |