PANDO: Efficient Multimodal AI Agents via Online Skill Distillation
The paper introduces PANDO, a framework designed to enhance the efficiency of multimodal AI agents through online skill distillation. It addresses inefficiencies in existing systems by analyzing common issues and proposing solutions that reduce token usage while improving success rates. PANDO demonstrates a significant performance improvement over previous models, achieving a 58.3% success rate on a comprehensive set of tasks.
- ▪PANDO achieves a 58.3% success rate on 910 VisualWebArena tasks, outperforming previous models.
- ▪The framework uses 58% fewer tokens than SGV and 61% fewer than WALT, indicating improved efficiency.
- ▪Three trajectory-level efficiency metrics are introduced to assess performance beyond just success rates.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24785 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
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| Cluster | lCTRb5-90SWE |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.24785 (cs) [Submitted on 24 May 2026] Title:PANDO: Efficient Multimodal AI Agents via Online Skill Distillation Authors:Yubo Li, Yidi Miao, Haotian Shen, Yuxin Liu View a PDF of the paper titled PANDO: Efficient Multimodal AI Agents via Online Skill Distillation, by Yubo Li and 3 other authors View PDF HTML (experimental) Abstract:Recent advances in multimodal web agents often rely on increased inference-time computation, including rollout search, verifier passes, offline skill discovery, and specialist model stacks.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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