pith:XYYQ35M6
Neural Decision-Propagation for Answer Set Programming
Decision-propagation computes stable models by alternating falsity decisions and truth propagations, and its neural version learns to do so efficiently.
arxiv:2605.01797 v2 · 2026-05-03 · cs.AI
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\pithnumber{XYYQ35M6QISLFGVCFOVWULA3HT}
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Record completeness
Claims
Successful DProp computations are shown to capture the stable model semantics. NDProp can learn to efficiently compute stable models, and it improves accuracy and scalability on neuro-symbolic benchmarks.
That neural decisions combined with fuzzy propagations can reliably approximate exact stable-model computation while preserving correctness and generalizing beyond the training distribution used in the benchmarks.
NDProp learns decision heuristics via neural networks and fuzzy propagation to compute stable models in ASP, improving accuracy and scalability over prior neuro-symbolic methods.
Receipt and verification
| First computed | 2026-06-02T01:03:47.956346Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
be310df59e8224b29aa22bab6a2c1b3ccc7d6729a873b3945ace2acc94952e61
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/XYYQ35M6QISLFGVCFOVWULA3HT \
| 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: be310df59e8224b29aa22bab6a2c1b3ccc7d6729a873b3945ace2acc94952e61
Canonical record JSON
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