pith:TD2PDXL3
Quantifying the Reconstructability of Astrophysical Methods with Large Language Models and Information Theory: A Case Study in Spectral Reconstruction
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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\pithnumber{TD2PDXL326HKCOWFNIUG25NXHK}
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Record completeness
Claims
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.
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.
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
· · · · ·Agent API
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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