Enjoyed being interviewed by @jeffreybrainard.bsky.social @science.org on AI for scientific novelty assessment—a problem that is still unsolved by SOTA LLMs in my experience. Stay tuned for more on this! www.science.org/content/arti...
Who's Who of AI
Tom Hope
Why they matter
Researcher with public evidence across AI research, NLP & language.
- AI signals
- 2 past 30d
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- 2 distinct domains
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- 0 past 30d
- Latest signal
- 4d ago
Assistant professor, research scientist | boosting scientific discovery with AI, NLP, IR, KG, HCI | @ai2.bsky.social
What they're sharing
science.org
MUSE: A Full-Text Cross-Domain Knowledge Base of Scientific Problems, Solutions, and Rationales arxiv.org
Articles & links
Led by my student Tsofia Cohen. Annotated dataset, trained extraction and problem-solving models (e.g., Qwen-32B), and the MUSE knowledge base are openly available (see links in paper). arxiv.org/abs/2608.10974
Their own posts
Recent commentary
MUSE, a knowledge base for AI scientists (and humans, too) to discover fine-grained technical research problems, solutions and their *rationales* , extracted from full-text papers across all domains on arXiv. 36,960 Problem–Solution–Rationale triplets across scientific domains.
Their network
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