In-Depth Analysis of MiroThinker 1.7: Engineering Optimizations and Guardrails for Long-Horizon Reasoning Agents
MiroThinker 1.7 has achieved SOTA results in the field of long-horizon question reasoning. These outstanding results stem from the combination of a powerful Model and a solid Harness. This article documents key engineering optimizations in its Harness implementation. Prerequisite Background MiroThinker is a deep research Agent — given a complex question (“what are the titles of the cs papers on arxiv today”), it autonomously breaks down the task, searches, scrapes webpages, runs Python for verification, and finally outputs the \boxed{answer}. The foundation is classic ReAct: each turn involves LLM reasoning + tool calling, the results are written back to the history, looping 200~300 times until convergence. ...