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

Single-image, multi-image, and video deep research require different visual operations but share a workflow of visual grounding, external retrieval, and fact composition.

By Li, Zhang, Feng +11arXivOct 8, 2026

Score█████░░░░░4.5

Caveats

Preprint; not yet peer reviewed.

VerdictCompetent work. Briefs at most.

Read the original PDF Code

Abstract

Single-image, multi-image, and video deep research require different visual operations but share a workflow of visual grounding, external retrieval, and fact composition. A key challenge is to preserve the dependencies linking localized visual anchors, entity relations, source-supported facts, and answer-producing operations. We introduce OneSearch-VL, a unified agent centered on the Visually Grounded Evidence Graph (VGEG), which encodes these dependencies as a shared task-level reference for data construction, process supervision, and operation-level evaluation. Our VGEG-based data engine constructs and verifies multi-image and video questions and filters expert trajectories. Using these data, we assemble OneSearch-VL-SFT-110K and OneSearch-VL-RL-10K for SFT and RL, respectively. We further derive the Evidence-aware Visual-Grounded Rubric reward (EVGR) from VGEG annotations to supervise evidence traceability and visual grounding during RL. For fine-grained evaluation, we construct OneSearch-MI-Bench and OneSearch-Video-Bench, organizing questions by the research operations encoded in their VGEGs. Experiments show that OneSearch-VL-8B improves over Qwen3-VL-8B with tool access by 20.2 and 17.6 percentage points on the two new benchmarks, respectively, while also achieving substantial gains across 7 image benchmarks and VideoDR. Project repository: https://github.com/appletea233/OneSearch-VL

Hongyu Li, Manyuan Zhang, Kaituo Feng, Shu Chen, Dian Zheng, Hao Li, Hao Yu, Zhangquan Chen, Zoey Guo, Ray Zhang, Shaofei Huang, Tianrui Hui, Linjiang Huang, Si Liu

The editor's rubric

Heuristic review

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	██░░░ 2	18%	Solid incremental gain on a meaningful problem.
Evidence	███░░ 3	14%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	██░░░ 2	18%	Some room to improve with obvious engineering.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); breadth (many tasks); verification (multiple benchmarks, code released); stakes (general AI).

How the score was computed

rank-2026-10-07

Score█████░░░░░4.5

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Merit: 4.8 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.3 / 10; Shrunk toward the desk prior by editor confidence (38%).
Attention: 57%; Citations, upvotes, points, mentions.
Freshness: 32%; Half-life decay since publication.

Citations0 (reference 15, via semantic-scholar, Oct 11, 2026, 13:49 UTC)
Influential citations0 (reference 3, via semantic-scholar, Oct 11, 2026, 13:49 UTC)
Hugging Face upvotes24 (reference 25, via hf-daily, Oct 11, 2026, 13:49 UTC)
GitHub stars19 (reference 250, via hf-daily, Oct 11, 2026, 13:49 UTC)

The record

Reviewed by heuristic-v6 on Oct 11, 2026, 02:05 UTC. Paper type: method.
Categories: cs.CV
BRIEF, No.5 in the Artificial Intelligence edition of October 12, 2026.