autotrust/JEV-27B-VL: a decision model that learned to see without a single image of training

AutoTrust has released JEV-27B-VL, an Apache-2.0 open-weight multimodal extension of JEV-27B that combines rapid calibrated “System 1” decisions with slower step-by-step “System 2” reasoning over text and images. The authors report that although its decision head was not trained on images, the model achieved zero-shot video recommendation performance on the MicroLens dataset comparable to collaborative filtering trained on 59,045 users’ histories, reaching an AUC of 0.727 versus 0.728 and a higher top-five hit rate, while cover images substantially outperformed titles. They also report that JEV-27B-VL preserves JEV-27B’s text capabilities, including strong RewardBench judging results, hallucination detection through confidence-based selection, zero-shot news recommendation, and search reranking. The model can route confident questions to fast one-pass decisions and defer uncertain cases to more intensive reasoning, and the release includes serving instructions, a model card, experiment code, and an interactive demo, while noting that the image-recommendation result is based on a single dataset of 200 users.

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