Latency in agent performance significantly impacts report quality by influencing the iterative processes involved in generating research reports. As described in the Test-Time Diffusion Deep Researcher (TTD-DR) framework, adding more search and revision steps correlates with increased performance while maintaining similar latency compared to competing agents, as observed in the Pareto frontier analysis. This indicates that longer processing times can yield higher quality outputs but must be balanced against efficiency to avoid diminishing returns.
TTD-DR's approach integrates continuous feedback loops, allowing for timely refinements to reports as new information is gathered. This method ensures better quality integration of retrieved data, ultimately enhancing the coherence and helpfulness of the final report, demonstrating the need for optimal latency management in agent systems[1].
Get more accurate answers with Super Search, upload files, personalized discovery feed, save searches and contribute to the PandiPedia.
Let's look at alternatives:
- Modify the query.
- Start a new thread.
- Remove sources (if manually added).
- Request a manual search from our human research team.