// Reusable Context Engineering // Context bloat quietly kills long-horizon runs, but you can fix it from the outside without fine-tuning th…

// Reusable Context Engineering // Context bloat quietly kills long-horizon runs, but you can fix it from the outside without fine-tuning the underlying agent. (bookmark this) Context management is usually baked into an agent's own prompt or weights, which does not transfer and cannot wrap a closed model. New research introduces AdaCoM, which trains a separate LLM with end-to-end RL to prune or preserve the context of a frozen, possibly closed-source agent. That turns context engineering into a reusable, model-agnostic policy you train once and reuse, with substantial gains across diverse agents on web search and deep research. The authors also surface a capability-dependent fidelity-reliability trade-off, and find transfer works best between agents of similar capability. Paper: https://arxiv.org/abs/2605.30785 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-06-01