Show HN: Stanford's ACE paper was just open sourced
7 days ago
- #AI
- #Machine Learning
- #Self-Improving Models
- ACE (Agentic Context Engineering) is a framework for self-improving large language models using evolving playbooks.
- It employs a three-role architecture: Generator, Reflector, and Curator for continuous context improvement.
- Key features include incremental delta updates, self-supervised learning, high efficiency, and cost-effectiveness.
- ACE achieves significant performance gains: +10.6% on agent tasks and +8.6% on domain-specific benchmarks.
- The framework prevents context collapse by preserving detailed knowledge through structured, localized edits.
- ACE is highly efficient, reducing adaptation latency by 86.9% compared to existing methods.
- It includes tools for offline and online adaptation, with detailed evaluation and logging capabilities.
- The framework is extensible to new tasks and domains with minimal customization required.
- ACE is open-source, with comprehensive documentation and community support for contributions and feedback.