Long-running agents face a fundamental constraint: after many turns, the conversation history exceeds the model's context window. This is especially acute for small language models (SLMs) and constrained inference systems, where the window is small by design. The new arXiv preprint ContextCompact 2.0 addresses this by proposing context compaction, a technique that replaces older turns with a more compact form.
The abstract describes the core mechanism only briefly: context compaction replaces older turns with a… (the text cuts off). From the title and framing, the method is positioned as a way to preserve agent success and accuracy rather than simply truncating history. The target setting is explicitly SLMs and constrained context windows, suggesting the authors aim for a lightweight solution that does not require a large model's capacity.
As a single source, this digest can only report the abstract's claims. No experimental results, baselines, or comparisons are included in the available text. Still, the problem is well-defined: if agents are to run for many turns on small models, something must give way, and compaction is a plausible direction.