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pith:TD2PDXL3

pith:2026:TD2PDXL326HKCOWFNIUG25NXHK
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Quantifying the Reconstructability of Astrophysical Methods with Large Language Models and Information Theory: A Case Study in Spectral Reconstruction

Hsing Wen Lin, Zong-Fu Sie

Increasing text clarifies astrophysical method structure but leaves an entropy floor of implementation variance.

arxiv:2605.11154 v2 · 2026-05-11 · astro-ph.IM · cs.AI · cs.LG

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4 Citations open
5 Replications open
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Claims

C1strongest claim

while increasing text successfully clarifies the overall algorithmic structure, it fails to eliminate variance at the implementation level. This persistent variance establishes an 'entropy floor,' demonstrating that multiple divergent implementations remain consistent with explicit instructions.

C2weakest assumption

That LLM-generated distributions accurately sample the space of valid implementations and that remaining variance after detailed text is caused by missing tacit knowledge in the description rather than limitations of the models or prompting.

C3one line summary

LLMs prompted with increasing levels of text on TNO spectral reconstruction from photometry reveal an entropy floor where implementation variance persists, showing text alone cannot capture all tacit expert knowledge needed for exact replication.

Receipt and verification
First computed 2026-05-28T02:04:49.345891Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

98f4f1dd7bd78ea13ac56a286d75b73aa6534d69078b97915a40e7cf3c85f6d4

Aliases

arxiv: 2605.11154 · arxiv_version: 2605.11154v2 · doi: 10.48550/arxiv.2605.11154 · pith_short_12: TD2PDXL326HK · pith_short_16: TD2PDXL326HKCOWF · pith_short_8: TD2PDXL3
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/TD2PDXL326HKCOWFNIUG25NXHK \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 98f4f1dd7bd78ea13ac56a286d75b73aa6534d69078b97915a40e7cf3c85f6d4
Canonical record JSON
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      "cs.AI",
      "cs.LG"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "astro-ph.IM",
    "submitted_at": "2026-05-11T19:00:09Z",
    "title_canon_sha256": "9b48669a9bc0249554fc5aea0c5cd62c907658683247fee0966625212cd7cbe3"
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