2 October 2026

New method splits AI search agents into separate planning and synthesis roles

• IterSynth separates two functions that typically run together: one component plans next steps, another generates responses based on summaries rather than full conversation history.
• The approach reduces context accumulation, meaning agents don't have to remember every previous interaction to stay focused on current tasks.
• The method addresses limitations in ReAct-style agents, the current standard approach where planning and response generation happen in a single loop.

How it was covered

Deep Learning WeeklyEditorial team

A paper proposed IterSynth, which separates planning from synthesis using a Planner and Synthesizer with summary-based state, addressing limitations of ReAct-style agents through role decoupling and reduced context accumulation.