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+# JEV-27B-VL: a decision model that learned to see without a single image of training
+
+*AutoTrust · 30 September 2026*
+
+Today we are releasing **[autotrust/JEV-27B-VL](https://huggingface.co/autotrust/JEV-27B-VL)**, JEV-27B with vision. Show it
+images and text, ask a question, and it answers in one forward pass with a calibrated probability for every option. When a
+question needs more thought, the same model thinks it through step by step, and it can look at the images while it does.
+
+Its decision head never saw an image during training. It still makes image decisions well enough to compete with a
+recommender built from tens of thousands of users' behaviour.
+
+**TL;DR**
+
+* **Zero-shot image recommendation:** looking only at video covers, JEV-27B-VL ranks what a user will watch next as well as
+  collaborative filtering learned from **59,045 users' watch histories** (AUC 0.727 vs 0.728), with a higher top-5 hit rate
+  (59% vs 49%), and without any interaction data.
+* **Pictures beat words:** covers alone score far better than titles alone (AUC 0.727 vs 0.649).
+* **Everything JEV-27B does on text, it still does:** it gives the same text decisions. That includes a **RewardBench score of
+  89.9** (ahead of GPT-4o, Gemini 1.5 Pro and Claude 3.5 Sonnet used as judges), catching its own hallucinations (71% → 96%
+  accuracy on the questions it chooses to answer) and zero-shot news recommendation that beats rankers trained on the data.
+* **Open weights**, Apache-2.0, one command to serve.
+
+## One model, two ways to answer, now with eyes
+
+| | what you get | typical time |
+|---|---|---|
+| **System 1** | a yes/no, pick-one or 0-5 decision over text **and images**, as a calibrated probability for every option | about 0.1 s |
+| **System 2** | the full 27B model thinking step by step, with image input | seconds |
+
+Nothing to parse and no prompt gymnastics: you ask *"Is this scenario one where the user clicks this video?"* and get
+`{"true": 0.83, "false": 0.17}`.
+
+## It sees: zero-shot image recommendation
+
+We tested it on [MicroLens](https://github.com/westlake-repl/MicroLens), a public dataset of real users of a short-video app
+with the original cover images. For each of 200 users we hid the video they actually watched next among 19 videos other people
+were watching at the same time, which is what a feed would have shown. JEV-27B-VL looked at the covers of the last five videos
+the user watched and at each candidate cover, and ranked the 20 candidates.
+
+![Zero-shot image recommendation on MicroLens](https://huggingface.co/autotrust/JEV-27B-VL/resolve/main/blog/image_recommendation.png)
+
+| method | uses interaction data? | AUC | hit in top 5 |
+|---|---|---:|---:|
+| random order | no | 0.489 | 21% |
+| title similarity | no | 0.602 | 39% |
+| JEV-27B-VL reading titles only | no | 0.649 | 46% |
+| **JEV-27B-VL looking at covers only** | **no** | **0.727** | **59%** |
+| collaborative filtering | yes, 59,045 users | 0.728 | 49% |
+
+Three things stand out:
+
+* **It matches collaborative filtering with zero behaviour data.** Collaborative filtering needs to know who watched what. JEV
+  only needs the pictures.
+* **New videos are covered from the first second.** A video nobody has watched yet has no co-watch history, but it has a cover.
+* **Looking is better than reading.** The covers carry more of a user's taste than the titles do (+0.078 AUC, statistically
+  significant).
+
+A typical example: a user had been watching manga-commentary and dubbing clips, including part 1.6 of one manga-commentary
+series. Among 20 candidates, JEV-27B-VL put part 1.9 of the same series first, with a click probability of 1.00. That is the
+video the user watched next. It recognised the series from the cover art alone, and scored all 20 covers in 2.6 seconds.
+
+The same one-pass decisions work for any question about an image. A cover of a big plate of food gets *"shows food or
+cooking" = 1.00*; a game screenshot gets *"video game" = 1.00* out of four categories.
+
+## It still does everything JEV-27B does
+
+JEV-27B-VL gives the same text decisions as [JEV-27B](https://huggingface.co/autotrust/JEV-27B) (same top option on 24 of 24
+reference decisions). These results were measured with JEV-27B:
+
+**A judge that beats frontier models used as judges.** On RewardBench, 2,985 human-verified pairs of a better and a worse
+answer, System 1 scores **89.9** in one forward pass per ordering, ahead of the published results for Gemini 1.5 Pro (88.2),
+GPT-4o (86.7) and Claude 3.5 Sonnet (84.2). It is as accurate as the same model thinking before it judges (0.900 vs 0.902
+on a 400-pair sample), and about six times faster.
+
+![RewardBench](https://huggingface.co/autotrust/JEV-27B-VL/resolve/main/blog/judge_rewardbench.png)
+
+**It knows when an answer is made up.** System 2 answers 1,000 trivia questions and gets 71% right. System 1 then checks each
+answer. Answering only the half it trusts most gives **96% accuracy**; ranking by the model's own stated confidence gets only
+87%. When System 1 says 0.97 or more, the answer was right 99% of the time.
+
+![Hallucination guard](https://huggingface.co/autotrust/JEV-27B-VL/resolve/main/blog/hallucination_guard.png)
+
+**Zero-shot news recommendation.** On real MSN News users (MIND), reading the headlines a user clicked, JEV scores an AUC of
+**0.642** with no training, above every zero-shot baseline (best 0.606) and above LightGBM rankers trained on the dataset
+(0.590 and 0.616). Added to a trained ranker as one feature, it is worth as much as four times more training data.
+
+![News recommendation](https://huggingface.co/autotrust/JEV-27B-VL/resolve/main/blog/news_recommendation.png)
+
+**Search re-ranking** above a dedicated re-ranker: nDCG@10 **0.858** on TREC-COVID, against 0.793 for bge-reranker-v2-m3 and
+0.623 for BM25.
+
+**Thinks only when it needs to.** System 1 answers when it is confident and hands the rest to System 2. With a 0.70
+threshold, 70% of questions are answered in 0.11 s and accuracy rises from 0.792 to **0.892**, close to thinking on every
+question (0.917).
+
+![System 1 to System 2](https://huggingface.co/autotrust/JEV-27B-VL/resolve/main/blog/system1_to_system2.png)
+
+## Try it
+
+```bash
+hf download autotrust/JEV-27B-VL --local-dir JEV-27B-VL
+bash JEV-27B-VL/serve.sh        # vLLM, one GPU with 80 GB or more
+```
+
+The [model card](https://huggingface.co/autotrust/JEV-27B-VL) has copy-paste code for image decisions and image chat. All the
+experiments above, with code and an interactive web app (image recommendation, news recommendation, judge, hallucination
+guard and more), are in **[JEV-27B-DEMO](https://github.com/yuhai-china/JEV-27B-DEMO)**.
+
+Image decisions are zero-shot, and the recommendation result above comes from one dataset of 200 users. We would love to hear
+what JEV-27B-VL does on your images.