MDIA: A Multi-Agent Diagnostic Intelligence Pipeline on HealthBench Professional
The article discusses the MDIA, a Multi-Agent Diagnostic Intelligence Pipeline designed for clinical reasoning. It highlights the performance improvements achieved through architectural design rather than just prompt engineering. The findings suggest that the choice of grading model can significantly impact evaluation results.
- ▪MDIA is implemented as a 7-node specialty-routed clinical reasoning graph on the HealthBench Professional benchmark.
- ▪It achieved a score of 0.6272, outperforming OpenAI's ChatGPT for Clinicians by 3.72 percentage points.
- ▪The performance improvements are attributed to system architecture features such as specialty routing and multi-turn context preservation.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24699 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| 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 | CcETV5K71R0S |
| 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:2605.24699 (cs) [Submitted on 23 May 2026] Title:MDIA: A Multi-Agent Diagnostic Intelligence Pipeline on HealthBench Professional Authors:Roberto Cruz, David Rey-Blanco View a PDF of the paper titled MDIA: A Multi-Agent Diagnostic Intelligence Pipeline on HealthBench Professional, by Roberto Cruz and David Rey-Blanco View PDF HTML (experimental) Abstract:Most reported gains on agentic-LLM clinical benchmarks are often attributed to prompt engineering, yet our results suggest that larger improvements can come from architectural and engine-level design.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
