Cortex is a compact specialist game policy:
11M trainable parameters (10.98M exactly) over frozen
DINOv3 ViT-S+/16 features. Trained with pure behavior cloning on the ≈475-hour Quake subset of the public
Pixels2Play corpus on a single consumer GPU (RTX 5080, 16 GB), it reaches deeper engine-verified route progress on Quake E1M1 than the released
P2P-150M and
NitroGen (≈500M) generalist gaming agents evaluated under the same protocol.
The Video Preview on this page shows a rollout of this checkpoint on E1M1 from a fresh spawn; there is
another run on YouTube.
In one sentence: 4 frames × 41 frozen DINOv3 tokens → a 6-layer 384-d transformer → 36 held-state logits (sampled at temperature 1) plus continuous mouse dx/dy, at 10 Hz. The full architecture, action-schema contract, and design rationale live in the
cortex-actor repository; complete results, evaluation protocol, and negative results are in the
paper.
1from cortex_actor import from_hub
2
3model, ck = from_hub("mad-bot/cortex")
4
5# Per step: cls (B, T, 384) and patches (B, T, 5, 8, 384) from frozen
6# DINOv3 ViT-S+/16 at 640x400, T = 4 most recent frames at 10 Hz.
7out = model(cls, patches=patches)
8out["held_logits"] # (B, 36) — sample at temperature 1
9out["mouse_dx"], out["mouse_dy"] # tanh outputs; scale by 500 / 250 mickeys
The action-channel contract (key roster order, held-state layout, mouse scales) is defined in
cortex_actor/schema.py and is part of the architecture: the index of a key in
KEYS is its channel in the logits tensor.
On Quake E1M1 under an engine-verified evaluation protocol (N=20 stochastic episodes across 5 seeds, independently replicated), Cortex passes the opening door–button–gate sequence 20/20 and reaches route waypoint median 5–6 (max 9) with 28–32 kills per batch; the released
P2P-150M and
NitroGen baselines, run in the same environment with their official inference code at matched duration, stall at route waypoint median 1. No system, including ours, completes the level. Full numbers, controls, robustness batteries, and what did
not work:
the paper.
Dzmitry Malyshau. Development was heavily AI-assisted by Anthropic's Claude and OpenAI's Codex — see the paper's AI assistance disclosure.
MIT for the model weights and code. The DINOv3 encoder is distributed by Meta under its own license; the Pixels2Play corpus belongs to Elefant AI.