Pith Number

pith:LYBJAIC2

pith:2026:LYBJAIC2ZGXFR7XUUQTJIDUDC3

not attested not anchored not stored refs resolved

SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management

Lang Cao, Shengyue Guan, Yihao Liu

An SOP-free framework lets LLMs autonomously synthesize executable procedures for supply chain tool use and delivers the strongest consistent performance.

arxiv:2602.07342 v2 · 2026-02-07 · cs.AI

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{LYBJAIC2ZGXFR7XUUQTJIDUDC3}

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 open · sign in to claim

4 Citations open

5 Replications open

✓ 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.

Claims

C1strongest claim

SupChain-ReAct, an SOP-free framework that autonomously synthesizes executable procedures for tool use, achieves the strongest and most consistent tool-calling performance.

C2weakest assumption

That the tasks and standard operating procedures in SupChain-Bench accurately represent real-world supply chain workflows and that performance gains from SupChain-ReAct will generalize beyond the specific models and scenarios tested.

C3one line summary

SupChain-Bench reveals substantial gaps in LLM reliability for long-horizon supply chain orchestration, while the proposed SupChain-ReAct framework improves tool-calling by autonomously synthesizing procedures.

References

21 extracted · 21 resolved · 1 Pith anchors

[1] Smart routing for sustainable supply chain net- works: An ai and knowledge graph driven approach. Applied Sciences, 15(14):8001. Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, 2024

[2] Large Language Model Agent: A Survey on Methodology, Applications and Challenges 2025 · arXiv:2503.21460

[3] Identifier Extraction:The system first parses the user’s query to extract the primary trade_order_id

[4] An order may be split into multiple fulfillments

[5] Status Check:For each fulfillment_id iden- tified in the previous step, the system queries its real-time status

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:31.322406Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5e0290205ac9ae58fef4a426940e8316cf97059127a3d16179cc94be8ac488b5

Aliases

arxiv: 2602.07342 · arxiv_version: 2602.07342v2 · doi: 10.48550/arxiv.2602.07342 · pith_short_12: LYBJAIC2ZGXF · pith_short_16: LYBJAIC2ZGXFR7XU · pith_short_8: LYBJAIC2

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/LYBJAIC2ZGXFR7XUUQTJIDUDC3 \
  | 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: 5e0290205ac9ae58fef4a426940e8316cf97059127a3d16179cc94be8ac488b5

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "09ff1b7536411fbd75f17ff9bc5c672c9dfa972c99eb2bf34a0cdced8bd6547f",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-02-07T03:49:25Z",
    "title_canon_sha256": "48aa46042a6438d3916f11b3dbfb88c2a0f3820fab4d91a479b86bc9968306e7"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2602.07342",
    "kind": "arxiv",
    "version": 2
  }
}