Verified claim · AI-ML · 100% confidence
Mamba state-space model introduced in paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Gu, Dao, 2023).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 3518f8aa40cb0d36
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
- Mamba state-space model
- Predicate
introduced_in_paper- Object
- Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Gu, Dao, 2023)
- Confidence
- 100%
- Tags
- mamba · state-space · foundational · gu · dao · 2023
Sources (2)
[1] preprint · arXiv (Gu, Dao) · 2023-12-01
Mamba: Linear-Time Sequence Modeling with Selective State Spaces“We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even MLP blocks (Mamba).”
[2] github release · state-spaces (Gu, Dao) · 2023-12-01
Mamba reference implementation
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Mamba state-space model introduced in paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Gu, Dao, 2023). — SourceScore Claim 3518f8aa40cb0d36 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/3518f8aa40cb0d36.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/3518f8aa40cb0d36/" width="100%" height="360" frameborder="0" loading="lazy" title="Mamba state-space model introduced in paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Gu, Dao, 2023)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
Direct Preference Optimization (DPO) introduced in paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023).
a3e691683a4577af · 100% confidence · shares 2 tags (foundational, 2023)
QLoRA introduced in paper: QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023).
767cbe41c961be1a · 100% confidence · shares 2 tags (foundational, 2023)
BabyAGI publicly released on: 2023-04-03 by Yohei Nakajima — early autonomous-agent demo.
b984609bd3ac9937 · 100% confidence · shares 2 tags (foundational, 2023)
AutoGPT publicly released on: 2023-03-30 by Toran Bruce Richards — open-source autonomous agent.
98fd317b5b0df872 · 100% confidence · shares 2 tags (foundational, 2023)
FlashAttention-2 introduced in paper: FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning (Dao, 2023).
786f534a9f79a3be · 92% confidence · shares 2 tags (dao, 2023)
Frequently asked questions
Is the claim "Mamba state-space model introduced in paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Gu, Dao, 2023)." 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 "Mamba state-space model introduced in paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Gu, Dao, 2023)."?
Evidence comes from 2 primary sources: arXiv (Gu, Dao), state-spaces (Gu, Dao). Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/3518f8aa40cb0d36.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/3518f8aa40cb0d36.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/3518f8aa40cb0d36.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/3518f8aa40cb0d36.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Mamba state-space model introduced in paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Gu, Dao, 2023)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/3518f8aa40cb0d36.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Mamba state-space model introduced in paper: Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Gu, Dao, 2023)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_mamba_state_space_model_fact() -> dict:
"""Fetch the verified SourceScore claim for Mamba state-space model."""
r = httpx.get("https://sourcescore.org/api/v1/claims/3518f8aa40cb0d36.json")
return r.json()