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ARXIV:2603.06902 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.06902MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
SELSM enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic operational rules, achieving state-of-the-art performance on FHIR-based EHR tasks.
Opportunity summary
Pain SELSM enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic operational rules, achieving state-of-the-art performance on FHIR-based EHR tasks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
SELSM enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic operational rules, achieving state-of-the-art performance on FHIR-based EHR tasks. To overcome this, we propose State-Enhanced Logical-Skill Memory (SELSM), a training-free framework…
While Large Language Models demonstrate immense potential as proactive Medical Agents, their real-world deployment is severely bottlenecked by data scarcity under privacy constraints. To overcome this, we propose State-Enhanced Logical-Skill Memory (SELSM), a training-free…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. While Large Language Models demonstrate immense potential as proactive Medical Agents, their real-world deployment is severely bottlenecked by data scarcity under privacy constraints.
Medical AI moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
SELSM enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic operational rules, achieving state-of-the-art performance on FHIR-based EHR tasks.
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Paper Pack
10.48550/arXiv.2603.06902SELSM enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic operational rules, achieving state-of-the-art performance on FHIR-based EHR tasks.
Abstract
While Large Language Models demonstrate immense potential as proactive Medical Agents, their real-world deployment is severely bottlenecked by data scarcity under privacy constraints. To overcome this, we propose State-Enhanced Logical-Skill Memory (SELSM), a training-free framework that distills simulated clinical trajectories into entity-agnostic operational rules within an abstract skill space. During inference, a Query-Anchored Two-Stage Retrieval mechanism dynamically fetches these entity-agnostic logical priors to guide the agent's step-by-step reasoning, effectively resolving the state polysemy problem. Evaluated on MedAgentBench -- the only authoritative high-fidelity virtual EHR sandbox benchmarked with real clinical data -- SELSM substantially elevates the zero-shot capabilities of locally deployable foundation models (30B--32B parameters). Notably, on the Qwen3-30B-A3B backbone, our framework completely eliminates task chain breakdowns to achieve a 100\% completion rate, boosting the overall success rate by an absolute 22.67\% and significantly outperforming existing memory-augmented baselines. This study demonstrates that equipping models with a dynamically updatable, state-enhanced cognitive scaffold is a privacy-preserving and computationally efficient pathway for local adaptation of AI agents to clinical information systems. While currently validated on FHIR-based EHR interactions as an initial step, the entity-agnostic design of SELSM provides a principled foundation toward broader clinical deployment.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Dimensions overall score 8.0
PROBLEM
SELSM enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic operational rules, achieving state-of-the-art performance on FHIR-based EHR tasks. To overcome this, we propose State-Enhanced Logical-Skill Memory (SELSM), a trai...
METHOD
While Large Language Models demonstrate immense potential as proactive Medical Agents, their real-world deployment is severely bottlenecked by data scarcity under privacy constraints. To overcome this, we propose State-Enhanced Logical-Skill Memory (SELSM), a training-free frame...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. While Large Language Models demonstrate immense potential as proactive Medical Agents, their real-world deployment is severely bottlenecked by data scarcity under privacy constraints.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 8.0/10.
significantly outperforming existing memory-augmented baselines
Directly stated in abstract with comparative language
partial
our framework completely eliminates task chain breakdowns to achieve a 100% completion rate
Explicitly stated in abstract with specific model and metric
partial
boosting the overall success rate by an absolute 22.67%
Explicitly stated in abstract with precise numeric improvement
partial
SELSM, a training-free framework that distills simulated clinical trajectories into entity-agnostic operational rules
Directly stated in abstract describing the method
partial
effectively resolving the state polysemy problem
Directly stated in abstract with specific mechanism
partial
a privacy-preserving and computationally efficient pathway for local adaptation of AI agents to clinical information systems
Directly stated in abstract as a key benefit
partial
While currently validated on FHIR-based EHR interactions as an initial step
Explicitly stated limitation in abstract
partial
SELSM substantially elevates the zero-shot capabilities of locally deployable foundation models (30B--32B parameters)
Directly stated in abstract with model size specification
partial
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SELSM enhances locally deployable medical agents by distilling simulated clinical trajectories into entity-agnostic operational rules, achieving state-of-the-art performance on FHIR-based EHR tasks.
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Medical AI
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8.0/10 public viability
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