Sequential Beats Parallel: When Deep Research Agents Learn to Reflect
Opening — Why this matters now The last year has been crowded with so-called deep research agents. Everyone parallelizes. Everyone fans out queries. Everyone promises doctoral-level synthesis at web speed. And yet, the leaderboard keeps telling an inconvenient story: throwing more parallel agents at a problem does not reliably buy depth. The paper “Deep Researcher with Sequential Plan Reflection and Candidates Crossover” enters this debate with a pointed thesis: research is not a map-reduce problem. If you want insight, you need memory, reflection, and the ability to change your mind mid-flight. ...