New paper: We’re building retrieval models for AI scientists (and human ones, too)—retrieval trained to directly support inspiration💡: 🫠Problem-solution latent links, ⏫Zooming out (abstraction), ⏬Zooming in (specification). Cognitive theories of ideation suggests these are fundamental ways humans traverse idea spaces and reframe problems, while brainstorming and connecting different areas. Our new work, RATIO, with my student @maayansharon10, brings these operations into scientific retrieval. Given for example a query on “reward hacking in RL-trained LLMs,” the goal is to retrieve a direct solution (e.g., reward-model ensembles), broaden the problem (e.g., to Goodhart’s law), or specify that principle in a new setting (e.g., recommender systems). Our focus here is on defining the task and creating a new automatic way for data collection from literature. The overarching goal: retrieve not just according to standard notions of topical relevance, but according to more latent, abstract links that help scientists take the next ideation step. I've been exploring this line of work in multiple papers over the years, excited to take a first step toward training models for this, and for more upcoming work in this space. This is also related to the recent notion of oblique retrieval (@lateinteraction): retrieving latent and implicit connections rather than relying only on surface similarity.

If you're attending #ACL2026 later today, check out these talks from my students and colleagues (along with all the other cool work from @allen_ai )! @NoySternlicht @NirMMazor Lior Forer Hiba Arnaout @IGurevych @UKPLab @aclmeeting See details in the image.

💡As #ACL2026 kicks off, we have four papers presented by my students and collaborators on using AI and scientific literature for discovery. See details in the image ⬇️ and links in the thread 🧵 @NoySternlicht @NirMMazor Lior Forer Hiba Arnaout @IGurevych @allen_ai @nlphuji @aclmeeting @UKPLab

Happy to share three papers accepted to the #ACL2026 @aclmeeting conference: on AI ideation, multimodal medical AI, and literature understanding. If you want to read about them, check out the following posts from @NoySternlicht , @NirMMazor and @UKPLab . More updates on each to come separately. In the hypothesis generation / ideation space : cross-domain inspirations. Led by @NoySternlicht . xitter.cf/NoySternlicht/status/1… In the multimodal medical AI domain: medical image diagnosis with literature retrieval. Led by @NirMMazor . xitter.cf/NirMMazor/status/20157… In literature understanding space: summarizing thousands of citations to understand a paper's impact. Led by Hiba Arnaout from @IGurevych's lab, with major contributions from @NoySternlicht . lnkd.in/de6DCS8J