MIRA: A Multimodal Illustrated Deep Research Agent for Controllable Reports

Deep research agents are increasingly capable of searching the open web and synthesizing evidence into comprehensive answers, yet existing agents remain poorly suited to controllable report generation in multimodal deep research, a challenging task in which users provide interleaved textual and visual inputs, agents must gather multimodal evidence through multi-step web search, and users expect a comprehensive, illustrated report rather than a short answer. Two limitations are particularly prominent. During reasoning, existing search agents often underutilize visual information and lack effective mechanisms for interacting with images discovered throughout the search trajectory. During generation, they typically produce brief responses rather than comprehensive reports, while relying solely on base models provides limited user control over three report-level attributes: report granularity, multimodal composition, and evidence grounding. To address these challenges, we introduce MIRA, a Multimodal Illustrated Deep Research Agent for controllable reports. MIRA tightly integrates textual and visual evidence throughout the search process, enabling the agent to inspect, reason over, and reuse intermediate images while preserving strong web-search capabilities. At its core, we develop MIRA-Harness, which translates user requirements into generation contracts over report granularity, multimodal composition, and evidence grounding, and provides four complementary execution stages for improving report quality. Building on this harness, we develop MIRA-Data, a data construction pipeline for producing high-quality multi-hop multimodal research trajectories, and post-train foundation models to operate effectively within the harness. Together, MIRA achieves state-of-the-art performance among open agents on 7 representative, established multimodal-search and deep-research-agent benchmarks, while producing user-aligned illustrated reports.