OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video

Key points

It encodes the links from on-screen visual anchors to real-world entities, source-supported web facts and answer-producing operations (comparison, arithmetic, counting) in one graph, VGEG, used for data construction, reward and evaluation.
A data engine that narrows 2.5M web videos to 70k produced about 110k training trajectories (36k single-image, 37k multi-image, 35k video) and about 10k RL tasks.
Beyond answer correctness and query quality, the RL reward adds EVGR, which separately scores whether tool observations actually support the required facts (evidence traceability) and whether the right objects and frames were found and used (visual grounding).
OneSearch-VL-8B beat the same-size Qwen3-VL-8B agent by 20.2 points on OneSearch-MI-Bench, 17.6 on OneSearch-Video-Bench and 27.0 on VideoDR, and averaged 58.3 on the image benchmarks, 1.7 points above OpenSearch-VL-8B.
Joint SFT on all three input types reached a benchmark average of 55.8 versus 51.7–55.1 for single-type training, and the EVGR reward raised the average from 57.3 to 61.1.
The authors note that external search tools and changing webpages limit exact reproducibility, and that automated VGEG construction and model-based rewards may introduce annotation errors or judging biases.

External research summaries. These are not HDATF publications or measured product results.