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.
Related verified claims
More verified claims related to this one — keep exploring.
SWE-bench introduced in: Jimenez et al. 2024 — software engineering benchmark from GitHub issues.
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.
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.
100% confidence · shares 3 tags (prompting, reasoning, introduced_in)
Flamingo introduced in: Alayrac et al. 2022 — DeepMind few-shot vision-language model.
100% confidence · shares 2 tags (deepmind, introduced_in)
Toolformer introduced in: Schick et al. 2023 — self-supervised LLM tool-use.
100% confidence · shares 2 tags (2023, introduced_in)
vLLM introduced in: Kwon et al. 2023 — high-throughput LLM serving via PagedAttention.
100% confidence · shares 2 tags (2023, introduced_in)
Chatbot Arena introduced in: Zheng et al. 2023 — LMSYS open platform for evaluating LLMs by human preference.
100% confidence · shares 2 tags (2023, introduced_in)
Self-RAG introduced in: Asai et al. 2023 — self-reflective retrieval-augmented generation.
100% confidence · shares 2 tags (2023, introduced_in)
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 claim record + top cited 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
Frequently asked questions
How has SourceScore reviewed the claim "Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs."?
SourceScore records this assertion with 100% legacy editorial confidence as of 2026-05-16, under methodology veritas-v0.1. That metadata is not a truth guarantee. Inspect the 2 cited source record(s), excerpts, and live evidence below.
What is the evidence for "Tree of Thoughts introduced in: Yao et al. 2023 — deliberate problem solving with LLMs."?
The record lists 2 cited source(s): arXiv (Yao, Yu, Zhao, Shafran, Griffiths, Cao, Narasimhan / Princeton + Google DeepMind), Princeton NLP. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/9d7676f71d1ee4f3.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
When was this claim record last reviewed by SourceScore?
Last reviewed 2026-05-16 under methodology version veritas-v0.1. The dated JSON record includes SourceScore-issued HMAC metadata, which is not publicly recomputable. Refetch the record and inspect current evidence before relying on it.
How can I cite this SourceScore claim in my code or article?
Fetch the JSON record from https://sourcescore.org/api/v1/claims/9d7676f71d1ee4f3.json, including the verbatim claim, cited evidence, confidence, methodology version, and last-verified date. Refetch the canonical HTTPS record and inspect cited evidence; the HMAC tag is not publicly independently verifiable. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this record from your application. The response includes verbatim excerpts, primary-source URLs, and SourceScore-issued HMAC integrity metadata. Refetch the canonical HTTPS record and inspect cited evidence; public users cannot independently verify the HMAC tag.
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()