Open, calibrated decision models that see, act and learn.

Typed decisions · computer use · real-time control · self-learning, open weights on Gemma 4.

Omni · 31B · 12B · E4B · Screen · Web · Control · Support ❤

♥ If AUBIN impresses you, press Like at the top of this page. It is the single biggest help for an independent open model.

_{🎬 AUBIN in 80 seconds (sound on) · Türkçe izle · vertical reel: EN / TR
Music: “Aphelion” by Scott Buckley, released under CC BY 4.0 · scottbuckley.com.au}

AUBIN-12B-v3d: AUBIN typed-decision model (Kev suites member)

This model makes typed, calibrated decisions (choice, score or yes/no) in one forward pass. It was trained on Kev's public training sources plus leak-free augmentation, including the wide 77-option banking77 questions.

Part of the AUBIN by Norovox family: AUBIN-12B · AUBIN-31B · full results: reports/AUBIN_RESULTS_2026-10-03.md

Single-model scores (kev_test / kev_transfer_test): 84.8 / 85.2

As part of the AUBIN ensemble:

• The ensemble combines E4B-v3, 12B-v3b, 12B-v3d and 31B, with members and weights selected on the calibration split.
• With AUBIN-Learn skills it reaches 87.08 on kev_test with NLL 0.431. Every choice was made on kev_dev and cal only.

model	kev_test
AUBIN ensemble + AUBIN-Learn skills	87.08
Kev-9B	87.4
Kev-27B	87.0
Kev-4B	86.5
Kev-0.8B	83.8

AUBIN does not yet beat Kev-9B on Kev's own training sources. The protocol and every variant tried are listed in the results report.

Use

from aubin import Aubin   # pip install "git+https://huggingface.co/emrevrg/AUBIN-12B#subdirectory=code"
m = Aubin("emrevrg/AUBIN-12B-v3d")
m.decide(state, questions)

AUBIN-Learn and the latest family results

AUBIN-Learn — instant self-learning (experimental, Norovox core)

AUBIN can learn from feedback without retraining. Verified cases go into an external decision memory (a write takes well under a millisecond with the hashing embedder), and a self-calibrator (Hedge / multiplicative weights) shifts trust per source between the model, the memory and their fusion — so where memory is not useful yet, AUBIN keeps trusting itself. Measured on Kev's public suites; full tables, protocol and code: reports/AUBIN_LEARN.md, code/aubin/learn.py.

• Feedback stream, kev_test: all 6 AUBIN variants improve, +1.0 to +1.3 points (best 84.3 → 85.6). Separate protocol — the label is revealed after each answer — so it is not comparable to static scores (Kev-9B 87.4 static).
• Never-seen sources (transfer, memory starts empty): −0.4 to +0.1 points — it does not hurt; a few hundred feedbacks per source are not enough to help yet.
• Fast skills, static locked test: per-source classifiers learned from memory in seconds (switched on only where dev proves them) lift kev_test for all 4 measured AUBIN variants, +0.3 to +0.6 points (ensemble 84.3 → 84.8; banking77 59.5 → 65.5).
• Raw memory (kNN) on the static test: no reliable gain (−1.0 to +0.7) — AUBIN already learned these sources.

Results, 3 October 2026 (full report: reports/AUBIN_RESULTS_2026-10-03.md)

benchmark	AUBIN	reference
Typed decisions (2,000 decisions)	77.55 (AUBIN-Learn, weights fixed before test)	Laya 76.65 · meraGPT 76.8 · Jev 72.7
Kev suites, Kev's training sources (kev_test)	85.7 (AUBIN ensemble, selected on cal split)	Kev-0.8B 83.8 · Kev-4B 86.5 · Kev-9B 87.4
Kev suites, transfer test	86.5 ensemble · 89.0 AUBIN-31B	–
Mind2Web cross-domain step SR (200 steps)	48.5 AUBIN-31B, no web training	MindAct-XL 39.6 · GPT-4 26.4
Grid control, 100 unseen episodes	92% success, 0 lava deaths (12B-Control + shield)	greedy rule + same shield 89%

On Kev's own training sources AUBIN is still 1.7 points behind Kev-9B; this is stated, not hidden.

Evening update (3 Oct, all measured, details in the results report):

benchmark	AUBIN	reference
ScreenSpot click accuracy (visual computer use)	69.3 AUBIN-E4B-Screen (zero-shot 48.5; 67.7 after round 1)	SeeClick 53.4 · CogAgent 47.4 · UGround-7B 73.3 · UI-TARS-7B 89.5
Mind2Web step success, cross-task / website / domain	47.0 / 37.0 / 43.5 (12B) · 47.0 / 34.5 / 42.0 (E4B, ≈2× faster)	MindAct-XL 52.0 / 38.9 / 39.6
ViZDoom FPS, kills per episode (30 episodes)	17.3 with in-game self-learning (AUBIN-Learn) · 15.6 without (AUBIN-12B, 0.6 s/move)	random 1.3 · scripted rule 18.8
Kev training sources + learned skills (selected on kev_dev + cal only)	87.08, NLL 0.43 (ensemble 85.69, NLL 0.79)	Kev-9B 87.4 (not yet beaten) · Kev-27B 87.0 · Kev-4B 86.5

New in code/: AubinLearning.acquire_skill (learns a skill, self-tests on held-out data, enables only on proven gain), learn_skill2/3.py, fuse_multi.py, screenspot_train/eval.py, fps_vizdoom.py.

The AUBIN family: every ability, one interface (AubinEngine routes between them)

ability	model	measured
typed decisions (choice / score / yes-no), calibrated	AUBIN-31B · AUBIN-12B · 12B-v3b · 12B-v3d · E4B-v3	typed-decisions 77.55 (#1) · Jev's set 8/8 · Kev unseen sources 89.8 · Kev training sources 87.08
web agent	AUBIN-12B-Web · AUBIN-E4B-Web (fast) · 31B web training running	Mind2Web cross-domain 43.5 (MindAct-XL 39.6)
visual computer use (click on screenshots)	AUBIN-E4B-Screen · 12B screen training running	ScreenSpot 69.3
real-time control	AUBIN-12B-Control · AUBIN-E4B-Control	92% success, 0 lava deaths
FPS play, self-learning in game	AUBIN-12B + AUBIN-Learn	ViZDoom 17.3 kills/episode vs 15.6 without learning (30 episodes)
self-learning, self-acquired skills	AubinLearning (learn, acquire_skill) in every repo	skills switch on only after a held-out self-test proves a gain

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_{🎬 AUBIN filmi, Türkçe (80 sn, sesi aç) · English · Müzik: “Aphelion”, Scott Buckley, CC BY 4.0}

♥ Help AUBIN get seen

On Hugging Face, likes decide what people discover. Big labs have marketing teams; AUBIN has one 17-year-old student and measured results. If AUBIN is useful, interesting or just impressive to you, press ♥ Like at the top of this page and on AUBIN-Omni, AUBIN-31B and AUBIN-12B, then share it with one person who builds with AI. Every like helps an independent, open, honestly measured model get discovered.

_{🇹🇷 Beğenin, AUBIN'in görünür olmasını sağlar: sayfanın üstündeki ♥ Like'a basarak destek ol ve bir arkadaşına gönder.}

Support Norovox

Norovox is built by one 17-year-old high school student in Türkiye: no company, no sponsor, no budget. Every model on this page was trained on free GPU quotas that run out within days. When they run out, training stops halfway and the next, stronger version has to wait, sometimes for a week.

Your support goes directly into GPU time. Even a small contribution lets a training run finish instead of stopping, and helps the Nythos, N1, AUBIN and Norovox Embed models get better faster. Supporters are credited in the model cards and get early access to new releases.

Contact: zgremre@gmail.com · emrevrgdev@gmail.com

_{🇹🇷 Norovox'u Türkiye'de 17 yaşında bir lise öğrencisi tek başına geliştiriyor: şirket yok, sponsor yok, bütçe yok. Buradaki modellerin hepsi birkaç günde biten ücretsiz GPU kotalarıyla eğitildi; kota bitince eğitim yarıda kalıyor ve daha güçlü sürüm bekliyor. Desteğin doğrudan GPU süresine gider; küçük bir katkı bile bir eğitimin yarıda kalmadan tamamlanmasını sağlar. Destekçiler model kartlarında anılır ve yeni sürümlere erken erişir. İletişim: zgremre@gmail.com · emrevrgdev@gmail.com}

_{Built with Claude Opus 5.5 and GPT-5.6 Sol; because of OpenAI usage limits, the final stretch was completed with Claude Opus 5.5. License: Apache-2.0 (adapters and code). Base models: Google Gemma 4 (Apache-2.0).}