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AlphaTensor discovers faster matrix multiplication algorithms, beating Strassen's 1969 record for 4×4 mod 2

★★★★scienceDeepMindconfidence: high

DeepMind's AlphaTensor (Nature, Oct 2022) framed matrix multiplication as a tensor-decomposition game. It found a 4×4 algorithm over GF(2) with 47 multiplications (Strassen-based: 49) and improved 5×5 to 96. Human researchers cut 5×5 further to 95 within days.

Key facts

Science result

Field
computer-science / algebraic complexity
Problem
Minimum number of multiplications for small matrix products (tensor rank) (open since 1969)
Result
New lower-rank decompositions: 4×4 over GF(2) with 47 multiplications; improvements for several other sizes.
AI system
AlphaTensor
Human role
Autonomous search within a human-designed RL game
Verification
Peer-reviewed in Nature; algorithms checkable by direct computation
Status
confirmed
Why surprising
First improvement in over 50 years to a Strassen-era record for a small matrix size.

What happened

AlphaTensor, an AlphaZero descendant, searched the space of tensor decompositions and found matrix multiplication schemes using fewer scalar multiplications than any known for several sizes.

Why it matters

It was the first AI-found improvement to a famous algorithmic record. It prompted rapid human counter-improvements and led to AlphaEvolve's 48-multiplication complex 4×4 result in 2025.

Changelog

  • 2026-09-29: created

Related events

  1. AlphaEvolve: Gemini-powered agent discovers new algorithms ★★★★
  2. AlphaDev discovers faster small-sort routines, merged into LLVM's C++ standard library ★★★
  3. AlphaEvolve helps lower the matrix multiplication exponent ω to below 2.371177 ★★★

Sources (3)

id: 2022-10-05-alphatensor-matrix-multiplication · updated 2026-09-29 · open in the interactive timeline