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ARXIV:2603.15939 · NEURAL ARCHITECTURE SEARCH · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15939NEURAL ARCHITECTURE SEARCHSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A data-local framework for LLM-guided neural architecture search that automates model development for sensitive time-series data in healthcare.
Opportunity summary
Pain A data-local framework for LLM-guided neural architecture search that automates model development for sensitive time-series data in healthcare.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A data-local framework for LLM-guided neural architecture search that automates model development for sensitive time-series data in healthcare. This is a common problem in healthcare and other privacy-constrained domains (e.g., a hospital developing deep…
Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and architecture, yet training often has to run on-premise under strict data-local constraints. This is…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The results show that the modular baseline model is strong, and the LLM-guided NAS further improves it.
Neural Architecture Search moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A data-local framework for LLM-guided neural architecture search that automates model development for sensitive time-series data in healthcare.
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10.48550/arXiv.2603.15939A data-local framework for LLM-guided neural architecture search that automates model development for sensitive time-series data in healthcare.
Abstract
Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and architecture, yet training often has to run on-premise under strict data-local constraints. This is a common problem in healthcare and other privacy-constrained domains (e.g., a hospital developing deep learning models on patient EEG). This bottleneck is particularly challenging in multimodal fusion, where sensor modalities must be individually preprocessed and then combined. LLM-guided neural architecture search (NAS) can automate this exploration, but most existing workflows assume cloud execution or access to data-derived artifacts that cannot be exposed. We present a novel data-local, LLM-guided search framework that handles candidate pipelines remotely while executing all training and evaluation locally under a fixed protocol. The controller observes only trial-level summaries, such as pipeline descriptors, metrics, learning-curve statistics, and failure logs, without ever accessing raw samples or intermediate feature representations. Our framework targets multiclass, multimodal learning via one-vs-rest binary experts per class and modality, a lightweight fusion MLP, and joint search over expert architectures and modality-specific preprocessing. We evaluate our method on two regimes: UEA30 (public multivariate time-series classification dataset) and SleepEDFx sleep staging (heterogeneous clinical modalities such as EEG, EOG, and EMG). The results show that the modular baseline model is strong, and the LLM-guided NAS further improves it. Notably, our method finds models that perform within published ranges across most benchmark datasets. Across both settings, our method reduces manual intervention by enabling unattended architecture search while keeping sensitive data on-premise.
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unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 7.0
PROBLEM
A data-local framework for LLM-guided neural architecture search that automates model development for sensitive time-series data in healthcare. This is a common problem in healthcare and other privacy-constrained domains (e.g., a hospital developing deep learning models on patie...
METHOD
Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and architecture, yet training often has to run on-premise under strict data-local constraints. This is a common problem in healthca...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The results show that the modular baseline model is strong, and the LLM-guided NAS further improves it.
WHY NOW
Neural Architecture Search moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A data-local framework for LLM-guided neural architecture search that automates model development for sensitive time-series data in healthcare. This is a common problem in healthcare and other privacy-constrained domains (e.g., a hospital developing deep learning models on patient EEG).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Applying machine learning to sensitive time-series data is often bottlenecked by the iteration loop: Performance depends strongly on preprocessing and architecture, yet training often has to run on-premise under strict data-local constraints. This is a common problem in healthcare and other privacy-constrained domains (e.g., a hospital developing deep learning models on patient EEG).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The results show that the modular baseline model is strong, and the LLM-guided NAS further improves it.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Neural Architecture Search moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A data-local framework for LLM-guided neural architecture search that automates model development for sensitive time-series data in healthcare.
Segment
Neural Architecture Search
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Commercial read
7.0/10 public viability
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