Verified claim · AI-ML · 100% confidence
Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 9d7676f71d1ee4f3
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Structured fields
- Subject
- Tree of Thoughts
- Predicate
introduced_in- Object
- Yao et al. 2023 — deliberate problem solving with LLMs
- Confidence
- 100%
- Tags
- tree-of-thoughts · tot · princeton · deepmind · reasoning · prompting · 2023 · introduced_in
Sources (2)
[1] preprint · arXiv (Yao, Yu, Zhao, Shafran, Griffiths, Cao, Narasimhan / Princeton + Google DeepMind) · 2023-05-17
Tree of Thoughts: Deliberate Problem Solving with Large Language Models“We introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving.”
[2] github release · Princeton NLP · 2023-05-17
Tree of Thoughts — official Princeton NLP repository
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs. — SourceScore Claim 9d7676f71d1ee4f3 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/9d7676f71d1ee4f3.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/9d7676f71d1ee4f3/" width="100%" height="360" frameborder="0" loading="lazy" title="Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
SWE-bench introduced in: Jimenez et al. 2024 — software engineering benchmark from GitHub issues.
b16b5f5297e5f621 · 100% confidence · shares 3 tags (princeton, introduced_in, 2023)
ReAct prompting pattern introduced in: Yao et al. 2022 — synergizing reasoning and acting in language models.
95193a0b79c777e8 · 100% confidence · shares 3 tags (princeton, prompting, introduced_in)
Chain-of-Thought (CoT) introduced in: Wei et al. 2022 — Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.
a8503ad535423b54 · 100% confidence · shares 3 tags (prompting, reasoning, introduced_in)
Flamingo introduced in: Alayrac et al. 2022 — DeepMind few-shot vision-language model.
72ea74efc723bd06 · 100% confidence · shares 2 tags (deepmind, introduced_in)
Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use.
cd4387e16e2c3e3d · 100% confidence · shares 2 tags (2023, introduced_in)
Frequently asked questions
Is the claim "Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs." 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 "Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs."?
Evidence comes from 2 primary sources: arXiv (Yao, Yu, Zhao, Shafran, Griffiths, Cao, Narasimhan / Princeton + Google DeepMind), Princeton NLP. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/9d7676f71d1ee4f3.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/9d7676f71d1ee4f3.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.
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/9d7676f71d1ee4f3.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/9d7676f71d1ee4f3.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/9d7676f71d1ee4f3.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_tree_of_thoughts_fact() -> dict:
"""Fetch the verified SourceScore claim for Tree of Thoughts."""
r = httpx.get("https://sourcescore.org/api/v1/claims/9d7676f71d1ee4f3.json")
return r.json()