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A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline

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arXiv:2606. 07718v1 Announce Type: new Abstract: Agentic AI tools offer a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build, where scientists care about correctness and robustness, not implementation details.

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Authors: Kai A. Horstmann, Ethan Lin, Alice A. Robie, Jennifer J. Sun, Kristin Branson
Released: 2026-06-09 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, rect, correct, soft, eval, source, line, rate. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.07718

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