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UID:pretalx-adass2026-9ELWAF@pretalx.adass.org
DTSTART;TZID=AWST:20261103T094500
DTEND;TZID=AWST:20261103T100000
DESCRIPTION:Building and optimizing High-Performance Computing (HPC) pipeli
 nes for next-generation radio telescopes (e.g.\, SKA) presents immense sof
 tware engineering challenges. We present a novel approach using continuous
  "Loop Engineering" driven by specialized Large Language Model (LLM) agent
 s to refactor a radio interferometry imaging codebase (RICK). By deploying
  distinct AI personas—such as an HPC/MPI specialist\, a GPU-porting engi
 neer\, and a domain-specific radio astronomer—we automated complex archi
 tectural upgrades. Operating sequentially to prevent code conflicts\, thes
 e agents successfully aligned scientific outputs with industry standards (
 WSClean) within a strict 1% tolerance\, guaranteed MPI scale invariance\, 
 and optimized OpenMP GPU memory traffic. We discuss the efficacy of agent-
 driven automated development\, the technique of prompt cross-validation ac
 ross different LLM engines to debug complex FITS WCS metadata\, and the pa
 radigm shift from manual coding to managing autonomous AI engineering loop
 s in astronomical software development.
DTSTAMP:20261001T101611Z
LOCATION:Banquet Hall
SUMMARY:Multi-Agent Loop Engineering for Radio Astronomy Pipelines: Autonom
 ous Refactoring and Scientific Validation - Giovanni Lacopo
URL:https://pretalx.adass.org/adass2026/talk/9ELWAF/
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