2026-07-28 04:00 UTCOriginal source2 min readUpdated: 2026-07-28 08:25 UTC

DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs

DeepLens Diagnosis Agent uses a five-stage pipeline (structured clinical extraction, disciplined retrieval, constrained candidate generation, explicit evidence triangulation, and auditable final decision) to boost a small medical reasoning model JSL Medical Small 7B v2 to 60.14% diagnostic accuracy, outperforming many frontier models at lower cost. The workflow design enables reliable diagnostic reasoning under uncertainty and produces traceable intermediate artifacts for high-stakes applications.

SourcearXiv AIAuthor: Mahmood Bayeshi, Veysel Kocaman, Muhammed Ali Naqvi, Yigit Gul, David Talby

Article intelligence

EngineersAdvanced

Key points

DeepLens Diagnosis Agent employs a five-stage pipeline (extraction, retrieval, candidate generation, evidence triangulation, decision) to significantly improve diagnostic accuracy of a small model.
On DiagnosisArena benchmark, the agent achieves 60.14% accuracy, surpassing Claude Sonnet 4.5 and Gemini 3.1 Pro while costing 35-45% less.
Workflow design boosts model performance from 23.99% to 60.14%, a +36 point gain, showing structured process matters more than knowledge recall.
The pipeline generates auditable intermediate artifacts, suitable for high-risk medical settings requiring traceability and reproducibility.

Why it matters

This matters because deepLens Diagnosis Agent employs a five-stage pipeline (extraction, retrieval, candidate generation, evidence triangulation, decision) to significantly improve diagnostic accuracy of a small model.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

This panel is AI-generated and reviewed for accuracy.

-->

[Submitted on 19 May 2026]

Title:DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs

View a PDF of the paper titled DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs, by Mahmood Bayeshi and 4 other authors

View PDF HTML (experimental)

Abstract:Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with explanations. Frontier LLMs are strong generalists, but single-shot prompting often yields brittle diagnostic reasoning. We present the DeepLens Diagnosis Agent, a five-stage harnessing pipeline (combining model capabilities with disciplined process constraints) centered on a small medical reasoning model (JSL Medical Small 7B v2) and retrieval-augmented generation (RAG). The pipeline enforces structured clinical extraction, disciplined retrieval, constrained candidate generation, explicit evidence triangulation, and an auditable final decision. On the 915-case DiagnosisArena benchmark, the agent achieved 60.14% top-1 diagnostic accuracy, the highest among small and medium-sized models. The same model without the agent workflow achieved 23.99%, a +36-point gain from workflow design alone, despite 88.2% on standard medical benchmarks, showing that diagnostic reasoning under uncertainty requires more than knowledge recall. The agent costs USD 0.0072 per case (24K tokens on A100) with 24-second latency, 35-45% cheaper than Claude Sonnet 4.5 (USD 0.0110) and Gemini 3.1 Pro (USD 0.0128) while outperforming them by +9.70pp and +9.17pp. Harnessing can also correct frontier model failures; workflow constraints can outweigh parameter count or API cost.

Beyond aggregate accuracy, the pipeline produces structured intermediate artifacts that make each stage inspectable and support error localization. These properties support high-stakes settings where traceability, reproducibility, and auditable evidence matter alongside benchmark performance.

Comments: 20 pages, 5 figures, 5 tables. Technical report, John Snow Labs

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.22555 [cs.AI]

(or arXiv:2607.22555v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.22555

arXiv-issued DOI via DataCite

Submission history

From: Yigit Gul [view email] [v1] Tue, 19 May 2026 12:55:39 UTC (1,073 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs, by Mahmood Bayeshi and 4 other authors

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.AI

new | recent | 2026-07

Change to browse by:

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)