We present ThousandOfWind’s hybrid system for GroundLM 2026 LitTraceQA, combining traditional retrieval-augmented generation (RAG) with code-agent research. Agents design question-specific search scripts, revise them as evidence emerges, and reconcile their findings with RAG candidates. The required-test configuration starts with independent research and accepts subsequent revisions through field-level confidence gates and table-structure checks. Using GPT-5.6 Terra in GitHub Copilot CLI, the system raises the 71-question test composite from 0.3567 to 0.7374 and multiple-choice accuracy from 18/50 to 50/50. Macro table cell accuracy reaches 0.2698, leaving structured assembly as the main bottleneck. Our analysis explains the limits of row-key-preserving repair and motivates source-supported structural editing.