CA3 lets your open model keep learning after deployment, by your rules. What it learns is kept alongside the original, which never changes.
on six new European regions in six days, for NASA and IBM's crop model.
See the case → Keeps100%of a robot arm's ten everyday tasks while it learned two new ones. Ordinary fine-tuning: 0%.
See the case → Blocks0of 24planted falsehoods kept. A RAG over the same news repeated 8.
See the case → Forgets108sto erase on request, from all its memory.
See the case →Retrain it, and it can break what already worked. Add RAG, and it pays to reread the same context on every question. Either way, nobody sees what changed.
Learns every day.
Keeps what it knew.
Blocks bad data.
Forgets on request.
You decide.
Language models, a brain implant, a robot arm, a satellite model, a home robot, a personal app, image generation, model edits, speech, vision, sound and recommendation.
More right than RAG, at a seventh of the compute. Not one planted falsehood kept.
Alibaba Qwen · Meta Llama · SmolLM2 · NVIDIA Nemotron · 0.5B to 30BSee the case →
Seven new days learned; the earlier days kept at 0.939, fine-tuning 0.712.
SPINT decoder · FALCON public recordingsSee the case →All ten everyday tasks kept; ordinary fine-tuning lost them all.
Ai2 MolmoAct2 · simulatedSee the case →
+43.2 points in six regions; the US held at 60.
NASA and IBM Prithvi-100MSee the case →Every lesson learned by the next morning; five bad ones refused, two forgotten on request.
Alibaba Qwen3-1.7B · Roborock S7 · simulated nightsSee the case →
Better advice than the same model, eight times faster.
Alibaba Qwen3-4B · Multiverse LittleLambSee the case →
One new character a day; none forgotten, none mixed up.
Krea 2 · research demonstrationSee the case →
4 of 7 edits written by an AI held back, each with what it breaks; 3 let through.
Alibaba Qwen3.5 · Google Gemma 3 · edits written by an AISee the case →
Names 8% → 97%, and its English kept.
IBM Granite Speech 4.1See the case →
41% → 87% on it, the old camera kept, a planted trigger refused.
Meta DINOv2See the case →
39.8 MB per client, on one shared model.
Sony MMAudio · research demonstrationSee the case →
Every fake account's ratings held back.
Matrix factorisation · MovieLens-1MSee the case →Four to eight weeks on your own data, under NDA, with the same report.
Contact →The routine it settles on its own. The doubtful cases come to you, with the evidence side by side, and every decision stays on record.
229 of 3,136 checks, on three language models · every day a version: 1 of 29 undone on its own · See the case
From the language test: Alibaba Qwen3 14B, round 20d; moving to a new base model, Qwen2.5 7B to Qwen3 8B Base, round 13 · See the case
Four to eight weeks on your own data, under NDA. You get the same report on your data: accuracy and cost against your RAG, what it learned, what it refused and what it forgot.
CA3