openjev — Qwen3.5 trained as jev model

openjev is Qwen3.5 turned into a jev model: a single cross-encoder that reads a premise and a hypothesis and answers with entailment, contradiction or neutral. That one primitive is enough to rerank answers, grade them against a reference, guard content, and play games in real time: hand it the game state and a few statements about it, and the argmax entailment is the move. Nothing is trained per task.

openjev-4B v2: text, images and agents

The new 4B checkpoint (qwen3.5-4b-nli-v2/) reads images as well as text and was trained on a much larger and harder mixture. It is strictly zero-shot on everything shown here.

• Doom straight from the pixels (first video): 10.4 kills per episode, twice the v1 model (5.2); random play gets 1.
• Crafts an iron pickaxe from nothing in real Minecraft (second video): 11 milestones in ~22 decisions, driven by a backward-chaining scaffold where the jev model only checks statements about the inventory and the world.
• Much stronger on adversarial NLI (ANLI r3 0.42 → 0.63, WANLI 0.63 → 0.77) and on image claims (0.52 → 0.84), better reranking (ARC-Challenge 0.59 → 0.72, MMLU 0.47 → 0.53), same MNLI (0.91).

Doom from the text state (v2, 11 kills per episode; a perfect-information bot gets 18.8):

Bigger jev: Qwen3.5-35B-A3B (MoE) as the backbone (qwen3.5-35b-a3b-nli/). Zero-shot, and with the backbone frozen plus a small MLP head on the last-token latent (mlp_heads_35b/, one head per task, loadable with LatentMLPHead.load):

What's inside

• qwen3.5-4b-nli-v2/ — recommended: the 4B v2 jev checkpoint, text + images.
• qwen3.5-4b-nli/ — the original 4B jev checkpoint (text).
• qwen3.5-35b-a3b-nli/ — the 35B-A3B MoE jev checkpoint (load with modeling_qwen35_moe_seqcls.py).
• All checkpoints: Qwen3_5ForSequenceClassification, 3 labels contradiction, entailment, neutral, last-token pooling, trained with plain cross-entropy over the three classes.
• modeling_openjev.py — OpenJevCrossEncoder: predict, rerank, grade, latents; LatentMLPHead for the per-task heads.
• modeling_qwen35_moe_seqcls.py — Qwen3_5MoeForSequenceClassification for the 35B-A3B backbone.
• mlp_heads_35b/<task>/ — head.pt + norm.npz + meta.json, the 35B latent + MLP heads behind the second radar.
• code/ — everything used here: the trainer and data mixture builder, the evaluation harness, Flappy Bird, Doom (text and pixels), the Minecraft scaffold and bot, the radar.
• videos/ — Flappy Bird, Doom and Minecraft replays; results/ — raw JSON for every run and the full report.

Use it

from modeling_openjev import OpenJevCrossEncoder
jev = OpenJevCrossEncoder("AlexWortega/openjev", subfolder="qwen3.5-4b-nli-v2")

jev.predict([("The bird is 0.05 below the centre of the gap.", "The bird is below the centre of the gap.")])
# -> [[contradiction, entailment, neutral]] probabilities

jev.rerank("Which gas do plants absorb during photosynthesis?", ["oxygen", "carbon dioxide", "nitrogen"])
# -> index of the option with the highest entailment

Or with plain transformers:

from transformers import AutoModelForSequenceClassification, AutoTokenizer
tok = AutoTokenizer.from_pretrained("AlexWortega/openjev", subfolder="qwen3.5-4b-nli-v2")
model = AutoModelForSequenceClassification.from_pretrained("AlexWortega/openjev", subfolder="qwen3.5-4b-nli-v2")
text = model.config.nli_template.format(premise="...", hypothesis="...")

Images go inside the premise as <|vision_start|><|image_pad|>…<|vision_end|> with pixel_values / image_grid_thw from the Qwen3.5 image processor; see code/doom_vision.py and code/eval_image_nli.py.

Reference point: dleemiller's NLI cross-encoders. Licence MIT.