aiqbench analysis
The paper builds an agent around a locally run 27B language model that calls specialized EEG analysis tools, and it improves the agent by optimizing only its natural-language instructions with GEPA, leaving model weights and tools fixed. Its claims are measured against expert-written reports and two external benchmarks, where it is described as "competitive" rather than state-of-the-art. For readers, the useful takeaways are the local, traceable tool-use design and the prompt-level optimization approach, though clinical deployment would still need validation beyond these benchmarks.
ERIA: An Instruction-Optimized Agent for Clinical EEG Reporting
arXiv:2611.01394v1 Announce Type: new Abstract: Clinical EEG interpretation requires integrating technical quality, background and behavioral states, interictal and ictal abnormalities, and overall clinical context across long recordings. We introduce ERIA (EEG Reporting and Instruction-Optimized Agent), a locally deployable framework in which a single 27B language-model controller analyzes whole-recording EEGs by orchestrating specialized parametric and signal-processing tools. ERIA combines learned tools for background abnormality, sleep-wake state, seizure-like activity, and interictal epileptiform discharges with deterministic spectral, amplitude, symmetry, montage, and signal-quality analyses. Rather than fine-tuning the controller or using multi-agent orchestration, ERIA keeps the controller weights, specialist tools, and safety constraints fixed and optimizes only its natural-language analysis policy with GEPA (Genetic-Pareto), using feedback from expert-written EEG reports. GEPA improves physician-aligned whole-recording report quality over the raw policy, and the optimized policy transfers to external EEG benchmarks. On CerebraGloss-Bench and BrainBench Foundational Analysis, ERIA remains competitive with the models evaluated in the original benchmarks. These results show that specialist tools and language-level policy optimization can extend the clinical and general EEG capabilities of a single locally executable model while preserving local inference and traceable tool use.
Abstract:Clinical EEG interpretation requires integrating technical quality, background and behavioral states, interictal and ictal abnormalities, and overall clinical context across long recordings. We introduce ERIA (EEG Reporting and Instruction-Optimized Agent), a locally deployable framework in which a single 27B language-model controller analyzes whole-recording EEGs by orchestrating specialized parametric and signal-processing tools. ERIA combines learned tools for background abnormality, sleep-wake state, seizure-like activity, and interictal epileptiform discharges with deterministic spectral, amplitude, symmetry, montage, and signal-quality analyses. Rather than fine-tuning the controller or using multi-agent orchestration, ERIA keeps the controller weights, specialist tools, and safety constraints fixed and optimizes only its natural-language analysis policy with GEPA (Genetic-Pareto), using feedback from expert-written EEG reports. GEPA improves physician-aligned whole-recording report quality over the raw policy, and the optimized policy transfers to external EEG benchmarks. On CerebraGloss-Bench and BrainBench Foundational Analysis, ERIA remains competitive with the models evaluated in the original benchmarks. These results show that specialist tools and language-level policy optimization can extend the clinical and general EEG capabilities of a single locally executable model while preserving local inference and traceable tool use.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2611.01394 [cs.LG]
(or arXiv:2611.01394v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.01394 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 276 words