Paper summary: Seed1.5-VL Technical Report
ByteDance's Seed1.5-VL pairs a 532M vision encoder with a 20B-active MoE LLM and reports state of the art on 38 of 60 public benchmarks, plus strong GUI-control and gameplay agent results.
Problem
Building a vision-language model that’s strong at perception, reasoning and acting as an agent, without making it enormous.
Method
Seed1.5-VL combines a 532M-parameter vision encoder with a Mixture-of-Experts LLM that has 20B active parameters. Most of the report covers the team’s experience in model design, data construction and multi-stage training.
Key results
From the abstract:
- state of the art on 38 of 60 public benchmarks
- in agent-centric tasks (GUI control and gameplay), it outperforms OpenAI CUA and Claude 3.7
- strong multimodal reasoning, such as visual puzzles
It’s accessible through ByteDance’s Volcano Engine.
Limitations
There’s no dedicated limitations section in the extracted text. The abstract points to access through Volcano Engine, and part of the evaluation uses internal suites that outsiders can’t rerun.
Sources
- Seed1.5-VL Technical Report (arXiv:2505.07062) — arXiv
- Hugging Face Daily Papers, 2025-05-13 — Hugging Face
This is a summary written from the linked public sources. Figures and numbers are as reported there.