Designing memory and compression to retain breadth with fidelity under context limits

Develop system-wide memory architectures and hierarchical compression strategies for deep research agentic systems that enable large-scale retrieval and planning while retaining coverage breadth and maintaining evidence fidelity within limited context windows.

Background

The evaluation reveals that as retrieval scope and system complexity grow, agents frequently exceed context limits, leading to loss of critical coverage or shallow synthesis. Multi-agent pipelines can amplify this by expanding planning steps and retrieved material into thousands of items, stressing memory and compression.

The authors explicitly frame the challenge as a system-wide design problem beyond the final synthesis component: agents need long-horizon memory with updates, hierarchical compression without information loss, and mechanisms to track processed content and preserve key evidence under tight context constraints.