Pith Number
pith:APIXJFSO
pith:2026:APIXJFSO2ZHOPMTTMXPRL3USN7
not attested
not anchored
not stored
refs pending
When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?
arxiv:2606.18531 v1 · 2026-06-16 · stat.ML · cs.LG
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{APIXJFSO2ZHOPMTTMXPRL3USN7}
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 | 2026-06-19T16:11:40.218431Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
03d174964ed64ee7b27365df15ee926fca4e64bb15efe0febdf22399f131c0fc
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/APIXJFSO2ZHOPMTTMXPRL3USN7 \
| 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: 03d174964ed64ee7b27365df15ee926fca4e64bb15efe0febdf22399f131c0fc
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "992acb844f7852103d68a02a7cb2497aa81171f3bb399a2db6640c73ac361f20",
"cross_cats_sorted": [
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"primary_cat": "stat.ML",
"submitted_at": "2026-06-16T22:55:45Z",
"title_canon_sha256": "12bc0919d720f8f0d32b4984806e1ffa90717be3e15e26f080782522f76e909f"
},
"schema_version": "1.0",
"source": {
"id": "2606.18531",
"kind": "arxiv",
"version": 1
}
}