Can Broad Biomedical Knowledge be Contextualized into Scenario-Grounded Propositions?
The article discusses a new framework called SCENE that aims to contextualize broad biomedical knowledge into specific, scenario-grounded propositions. This framework operates on two levels, converting general knowledge into actionable search directions and optimizing them to identify concrete propositions. The results demonstrate that SCENE effectively bridges the gap between broad knowledge and specific evidence, enhancing the discovery of patient subgroups and biological responses.
- ▪SCENE is a bi-level multi-agent framework designed for knowledge contextualization in biomedical research.
- ▪The upper level of SCENE converts broad knowledge into search directions, while the lower level optimizes these directions to find concrete propositions.
- ▪In clinical trials, SCENE outperforms existing methods by discovering specific patient subgroups with heterogeneous treatment benefits.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27082 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ELzfm13lB5LF |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.27082 (cs) [Submitted on 26 May 2026] Title:Can Broad Biomedical Knowledge be Contextualized into Scenario-Grounded Propositions? Authors:Qingyuan Zeng, Ziyang Chen, Pengxiang Cai, Zixin Guan, Anglin Liu, Lang Qin, Xinyao Lai, Jintai Chen View a PDF of the paper titled Can Broad Biomedical Knowledge be Contextualized into Scenario-Grounded Propositions?, by Qingyuan Zeng and 7 other authors View PDF HTML (experimental) Abstract:Biomedical discovery often requires connecting broad biomedical knowledge with specific experimental or clinical data.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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