Pith Number
pith:FWF47FHD
pith:2026:FWF47FHDAR2PJRBHUBKPBPLA5G
not attested
not anchored
not stored
refs pending
Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning
arxiv:2608.04663 v1 · 2026-08-05 · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{FWF47FHDAR2PJRBHUBKPBPLA5G}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
· sign in to
claim
4
Citations
5
Replications
✓
Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-08-06T01:46:07.510344Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2d8bcf94e30474f4c427a054f0bd60e9b271905fb8a9ca61a1213d9ff81ba11a
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FWF47FHDAR2PJRBHUBKPBPLA5G \
| 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: 2d8bcf94e30474f4c427a054f0bd60e9b271905fb8a9ca61a1213d9ff81ba11a
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "261022188b3d9623980ee4c5852629c33049ab8a20539b41066c1ded1cb061d9",
"cross_cats_sorted": [],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.AI",
"submitted_at": "2026-08-05T10:21:11Z",
"title_canon_sha256": "84a12853ef8826d2e2ec16f4edb58c35e0913266ac24e754b9eb21160503874a"
},
"schema_version": "1.0",
"source": {
"id": "2608.04663",
"kind": "arxiv",
"version": 1
}
}