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
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 identify that a key weakness of such models is their inability to perform content-based reasoning, and make several improvements. … Mamba enjoys fast inference (5× higher throughput than Transformers).”
[2] github release · state-spaces (Gu, Dao) · 2023-12-01
Mamba reference implementation
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const r = await fetch("https://sourcescore.org/api/v1/claims/3518f8aa40cb0d36.json");
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// "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()
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# "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()