pith:QLHBVG2K
Answer Only as Precisely as Justified: Calibrated Claim-Level Specificity Control for Agentic Systems
Compositional selective specificity calibrates each claim to the most specific level supported by evidence.
arxiv:2604.17487 v2 · 2026-04-19 · cs.CL
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\pithnumber{QLHBVG2KR6XPVMHMJDOZVZQIRD}
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Record completeness
Claims
Across a full LongFact run and HotpotQA pilots, calibrated CSS improves the risk-utility trade-off of fixed drafts. On the full LongFact run, it raises overcommitment-aware utility from 0.846 to 0.913 relative to the no-CSS output while achieving 0.938 specificity retention.
That automated claim decomposition and backoff proposal can reliably identify which parts of a response exceed the evidence without introducing new inaccuracies or losing critical context.
Compositional selective specificity (CSS) improves overcommitment-aware utility from 0.846 to 0.913 on LongFact while retaining 0.938 specificity by calibrating claim-level backoffs in agentic AI responses.
Receipt and verification
| First computed | 2026-05-20T00:04:31.974207Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
82ce1a9b4a8faefab0ec48dd9ae60888e92bd215fe4cb112975947bec3d002c6
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/QLHBVG2KR6XPVMHMJDOZVZQIRD \
| 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: 82ce1a9b4a8faefab0ec48dd9ae60888e92bd215fe4cb112975947bec3d002c6
Canonical record JSON
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