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UID:pretalx-adass2026-ZS9EYM@pretalx.adass.org
DTSTART;TZID=AWST:20261102T160000
DTEND;TZID=AWST:20261102T161500
DESCRIPTION:Radio interferometric pipelines encode expert judgment as fixed
  heuristics: reasonable defaults for a "typical" dataset. When a dataset d
 oesn't fit the mold\, an expert inspects the diagnostics and steps in by h
 and. That doesn't scale as surveys push toward higher data volumes and les
 s human oversight. LLMs can close that gap. They read the same diagnostics
  an expert would and reason about them per dataset. But letting a model bo
 th reason and act is risky. LLMs hallucinate\, drift\, and skip steps sile
 ntly. We built an architecture that strictly separates measurement from re
 asoning. The model gets room to adapt\, but it never touches the data path
  directly.\n\nAn MCP layer wraps the reduction software (CASA\, here) and 
 exposes each Measurement Set operation (metadata queries\, instrument geom
 etry\, calibration\, imaging) as an independent tool. Every tool returns s
 tructured data with explicit completeness and provenance. None of them int
 erpret their own output or call each other. Reasoning lives outside the to
 ols\, in skills: version-controlled\, plain-text documents that encode int
 erferometric expertise. They're fed into the LLM's context stage by stage\
 , so it can reason about what the tools hand back.\n\nAn orchestration lay
 er walks the model through a sequence of stages that looks like a processi
 ng pipeline\, except the parameters aren't fixed. Skill-based reasoning le
 ts the model make an informed\, per-dataset call instead of falling back o
 n defaults. Every stage's state\, its parameter choices\, and the sequenci
 ng are written out as documentation and a reproducible script. Since reaso
 ning lives outside the MCP layer\, the orchestrator doesn't care which mod
 el is driving it. Cloud (Claude\, Codex) or local and open (Gemma\, Qwen):
  both work. A model equipped with real domain expertise\, via skills\, bea
 ts a one-size-fits-all pipeline\, dataset by dataset.\n\nWe demonstrate en
 d-to-end inspection and calibration on VLA\, GMRT\, and ALMA data. We disc
 uss extending the approach to other instruments\, facilities\, and pipelin
 es.
DTSTAMP:20261001T111420Z
LOCATION:Banquet Hall
SUMMARY:# LLM-Orchestrated Radio Interferometric Data Reduction - Srikrishn
 a Sekhar
URL:https://pretalx.adass.org/adass2026/talk/ZS9EYM/
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