# Reflective Prompt Evolution

Clinical EEG interpretation must cover technical quality, background and sleep states, interictal and ictal abnormalities across a whole recording, then synthesize a report, so event detection alone is not enough. ERIA has a locally deployed 27B language-model controller call deterministic signal analyses, learned clinical models and local knowledge retrieval, while controller weights, tools and safety constraints stay fixed and only the natural-language analysis policy is rewritten by GEPA using feedback from expert reports. On 200 paired clinical EEGs, the physician-aligned report score on the protected test set rose from 0.5052 to 0.6024, and the policy still performed on CerebraGloss-Bench and BrainBench Foundational Analysis.

Clinical EEG interpretation must cover technical quality, background and sleep states, interictal and ictal abnormalities across a whole recording, then synthesize a report, so event detection alone is not enough. ERIA has a locally deployed 27B language-model controller call deterministic signal analyses, learned clinical models and local knowledge retrieval, while controller weights, tools and safety constraints stay fixed and only the natural-language analysis policy is rewritten by GEPA using feedback from expert reports. On 200 paired clinical EEGs, the physician-aligned report score on the protected test set rose from 0.5052 to 0.6024, and the policy still performed on CerebraGloss-Bench and BrainBench Foundational Analysis.

Reflective prompt evolution rewrote the two system prompts of a multi-agent deep-research agent, and under a split with no topic shared across train, validation, and test, held-out citation reward rose from 0.702 to 0.781, a gain of 0.079 that replicated across three seeds. Nearly half of the winning prompt by word count is training answers copied into the prompt, yet a controlled ablation shows this content earns no reward even on the 24 exact training questions it encodes, so the validation-minus-held-out gap fits small-sample selection under a noisy judge rather than memorization. Decomposed per-dimension feedback is what makes the gain transfer, and collapsing the reward to a single scalar drives held-out transfer negative.

Reflective prompt evolution rewrote the two system prompts of a multi-agent deep-research agent, and under a split with no topic shared across train, validation, and test, held-out citation reward rose from 0.702 to 0.781, a gain of 0.079 that replicated across three seeds. Nearly half of the winning prompt by word count is training answers copied into the prompt, yet a controlled ablation shows this content earns no reward even on the 24 exact training questions it encodes, so the validation-minus-held-out gap fits small-sample selection under a noisy judge rather than memorization. Decomposed per-dimension feedback is what makes the gain transfer, and collapsing the reward to a single scalar drives held-out transfer negative.