Verified claim · AI-ML · 100% confidence
SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 4244c11611a72550
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Structured fields
- Subject
- SGLang
- Predicate
introduced_in- Object
- Zheng et al. 2024 — efficient LLM serving with structured outputs
- Confidence
- 100%
- Tags
- sglang · uc-berkeley · inference · structured-outputs · open-source · 2024 · introduced_in
Sources (2)
[1] preprint · arXiv (Zheng, Yin, Xie, Huang, Yu, Liu, Lin, Cuenca, Zhao, Stoica / UC Berkeley) · 2023-12-12
SGLang: Efficient Execution of Structured Language Model Programs“We introduce SGLang, a system for efficient execution of complex language model programs.”
[2] github release · SGLang Project / UC Berkeley · 2024-01-01
SGLang — official GitHub repository
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SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs. — SourceScore Claim 4244c11611a72550 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/4244c11611a72550.jsonEmbed this claim
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Frequently asked questions
Is the claim "SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs."?
Evidence comes from 2 primary sources: arXiv (Zheng, Yin, Xie, Huang, Yu, Liu, Lin, Cuenca, Zhao, Stoica / UC Berkeley), SGLang Project / UC Berkeley. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/4244c11611a72550.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/4244c11611a72550.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
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cURL
curl https://sourcescore.org/api/v1/claims/4244c11611a72550.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/4244c11611a72550.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/4244c11611a72550.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "SGLang introduced in: Zheng et al. 2024 — efficient LLM serving with structured outputs."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_sglang_fact() -> dict:
"""Fetch the verified SourceScore claim for SGLang."""
r = httpx.get("https://sourcescore.org/api/v1/claims/4244c11611a72550.json")
return r.json()