Neos: Learning to Interleave Search and Visual Reasoning
Recent multimodal deep search agents have advanced tool use and visual content retrieval, yet they still struggle to convert intermediate visual evidence into actionable guidance for subsequent search, limiting their effectiveness in long-horizon settings. To address this gap, we present Neos, a multimodal deep search framework that learns both how to acquire evidence and how to use it to guide subsequent decisions. To make this coupling learnable, Neos constructs training tasks with intermediate visual dependencies while filtering out textual shortcuts, and introduces a lightweight, trainable Multimodal Evidence Interface (MEI) that transforms retrieved content into goal-conditioned obse...
| Authors | Ekko He, Jack Hong, Shilin Yan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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