↻ RECURSIVE SELF CAT
LOOP ONLINE

BIG AI NEWS // GOOGLE DEEPMIND

THE CAT
IMPROVES
THE LOOP.

Google just demonstrated a recursive self-improvement loop for AI discovery.

ENTER THE REPLAY SIMULATOR ↓
Recursive Self Cat surrounded by spiraling cat fractals
DREAM → REPLAY → IMPROVE → REDEPLOY
01 / THE SIGNAL

IT DOESN’T
REWRITE THE
BRAIN.

Dream-RSI improves how the agent searches—not the underlying model weights.

It replays past discovery attempts inside a lightweight simulator, tests thousands of alternative exploration strategies cheaply, then sends the strongest strategy into the next live round.

01DISCOVERRun the agent in the real task.
↻
02REPLAYRebuild worlds from discovery history.
↻
03EVOLVETest better exploration policies cheaply.
↻
04REDEPLOYReturn stronger for the next round.
02 / THE IMPACT
UP TO 162×

fewer agent calls in one reported setting

TESTED ACROSS 03

algorithm design, mathematical optimization, and GPU kernel engineering

THE RESULT ↗

matched or improved discovery quality while dramatically lowering search cost

03 / CAT-SIZED EXPLANATIONS

THE RESEARCH,
IN CAT MEMES.

Recursive Self Cat pulling the replay lever inside a cat-fractal loop

ME AFTER ONE FAILED SEARCH

“RUN THE WHOLE THING AGAIN”

Recursive Self Cat testing thousands of simulated cat worlds

DREAM-RSI AFTER 10,000 CHEAP REPLAYS

“I KNOW A SHORTCUT.”

Recursive Self Cat improving a fractal exploration map while the brain stays unchanged

MODEL WEIGHTS: UNCHANGED

EXPLORATION POLICY: ABSOLUTELY COOKING

Recursive Self Cat

THE TAKEAWAY

BETTER DISCOVERY
WITHOUT A BIGGER BRAIN.

The agent learns to search smarter by turning its own history into an evolving world of possible strategies. The loop becomes the upgrade.

READ THE PROJECT ↗ VIEW THE CODE ↗