SUMONE: A Simple and Efficient Summarizer for Plug-and-Play Agent Memory

In 30 Sekunden

Was passiert ist

SUMONE is a summarizer that compresses agent interaction histories using masked reconstruction, reducing inference costs by 75-78% without task-specific training.

Warum es zählt

Engineers building long-horizon language model agents need efficient context management to handle growing interaction histories without retraining for each task.

Achtung

The paper shows performance drops of 0.04 on benchmarks; real-world impact on agent quality across diverse downstream tasks remains unclear.