Search it. See it. Keep reasoning with it. 👀 That last part is harder than it sounds. In many search-agent setups, images retrieved along the way are not preserved in the model’s later context. As a result, a seemingly multimodal search trajectory can gradually collapse back into text-only reasoning. WeAgent-MMSearch, developed by the Weixin AI team at Tencent, is designed to keep visual evidence in the loop. With a persistent multimodal state, retrieved images remain accessible throughout the search trajectory. The agent can revisit them, cross-check visual evidence across turns, perform reverse-image searches, and reason over the retrieved images directly in subsequent reasoning and tool calls — rather than reducing everything to text. With agentic SFT followed by Failure-Aware GSPO, WeAgent-MMSearch reaches an average score of 55.97 across 8 benchmarks, up 19.22 points from baseline. We also introduce VisTarget-Bench, a human-verified benchmark of 150 tasks designed to disentangle two failure modes that standard evaluation often conflates: failing to retrieve the right visual evidence, and failing to answer correctly even after that evidence has been retrieved. Each task is paired with a held-out target image, allowing us to separately measure whether the agent retrieves the intended evidence and whether it can answer correctly once that evidence has been found. For multimodal search, finding the evidence is only the beginning. More on WeAgent-MMSearch: