WeAgent-MMSearch: Native Text-Vision Interaction for Multimodal Search Agents
Quick summary
arXiv:2608.28062v2 Announce Type: replace Abstract: Multimodal search agents extend parametric knowledge with newly emerging and long-tail evidence from the open web. Yet many existing agentic search environments often expose retrieved evidence only as text and omit tool-returned images from subsequent context, reducing visually grounded trajectories to text-only reasoning. Long-horizon interaction also compounds tool-call, response-length, timeout, and budget failures, which can discard salvageable trajectories, waste rollout computation, and disturb policy updates. To address these issues, w
Key takeaways
- arXiv:2608.28062v2 Announce Type: replace Abstract: Multimodal search agents extend parametric knowledge with newly emerging and long-tail evidence from the open web.
- Yet many existing agentic search environments often expose retrieved evidence only as text and omit tool-returned images from subsequent context, reducing visually grounded trajectories to text-only reasoning.
- Long-horizon interaction also compounds tool-call, response-length, timeout, and budget failures, which can discard salvageable trajectories, waste rollout computation, and disturb policy updates.
Why it matters
“WeAgent-MMSearch: Native Text-Vision Interaction for Multimodal Search Agents” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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