Framework-agnostic integration with structurally different context artifacts

Validate integration of the Combee parallel prompt learning framework with prompt learning methods that maintain structurally different context artifacts, such as program libraries or retrieval-augmented skill stores, to ascertain whether Combee remains framework-agnostic beyond ACE and GEPA.

Background

Combee is proposed as a framework-agnostic approach to scaling prompt learning, and the paper prototypes it on ACE and GEPA, which evolve text-based playbooks and system prompts within a generate–reflect–update loop. The authors note that other methods maintain different kinds of context artifacts (e.g., program libraries or retrieval-augmented skill stores) that may pose distinct integration challenges.

The open item concerns validating that Combee’s Map–Shuffle–Reduce design, parallel scan aggregation, and augmented shuffling can integrate with these structurally different artifact types while maintaining quality and efficiency under high parallelism.

References

While Combee demonstrates consistent improvements across our evaluation settings, several aspects remain open for future work. First, our experiments focus on two base prompt learning frameworks (ACE and GEPA). Although both follow the generate-reflect-update paradigm and Combee's design is intended to be framework-agnostic, we plan to further validate integration with methods that maintain structurally different context artifacts (e.g., program libraries or retrieval-augmented skill stores).

— Combee: Scaling Prompt Learning for Self-Improving Language Model Agents  (2604.04247 - Li et al., 5 Apr 2026) in Section: Limitations and Future Work