Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

  • 类型：arxiv
  • 标识：2607.13125
  • 链接：https://arxiv.org/abs/2607.13125
  • 主分类：multimodal
  • 形态：method
  • 被引：1
  • 被引来源：Semantic Scholar
  • S2被引：1
  • OpenAlex被引：0
  • 影响力被引：0
  • TLDR：It is demonstrated that strengthening the understanding capability of the Boogu-Image system, through a stronger multimodal encoder, agentic prompt rewriting, and related techniques, together with improvements in data quality, training pipelines, and agentic inference-time scaling, can substantially enhance generation and editing performance even under highly constrained compute budgets.
  • OpenAlex ID：W7169075743
  • OpenAlex DOI：10.48550/arxiv.2607.13125
  • DOI：10.48550/arxiv.2607.13125
  • DOI来源：OpenAlex
  • 开放获取：green
  • 开放获取链接：https://doi.org/10.48550/arxiv.2607.13125
  • OpenAlex更新：2026-07-19
  • 待LLM分类：否
  • 标题中文：Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation
  • TLDR中文：研究表明，通过更强的多模态编码器、Agentic prompt 改写及相关技术来增强 Boogu-Image 系统的理解能力，并结合数据质量、训练流程和 Agentic 推理时扩展的改进，即使在计算预算极为受限的条件下，也能显著提升生成与编辑性能。
  • 副分类：agent
  • 来源文件：
  • /inbox/tom/_candidates/2026-07-16-agent-rag-longcontext-candidates.json
  • [S2 enrich]
  • [OpenAlex backfill]