Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning
The paper presents a novel approach called Query-Conditioned Entity Alignment (QCEA) for improving cross-system medical reasoning. This method addresses the limitations of traditional static entity alignment by incorporating query context and recognizing the asymmetry between systems. Experimental results indicate that QCEA significantly enhances alignment accuracy and retrieval effectiveness in integrative medical settings.
- ▪QCEA reformulates entity alignment as a query-conditioned correspondence problem.
- ▪The framework integrates semantic encoding and graph-based representation learning.
- ▪Experimental evaluations show consistent improvements over existing alignment methods.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18570 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
| 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 | lmCg13_NS8aE |
| 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 |
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| 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:2605.18570 (cs) [Submitted on 18 May 2026] Title:Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning Authors:Yan Jiao, Jingran Xu, Pin-Han Ho, Limei Peng View a PDF of the paper titled Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning, by Yan Jiao and 3 other authors View PDF HTML (experimental) Abstract:Cross-domain knowledge alignment is essential for integrating heterogeneous medical systems, yet existing approaches typically treat entity alignment as a static matching problem, ignoring query context and cross-system asymmetry.
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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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