Thinking of ACE? We Can Do It with Fewer Tokens

This post introduces a comparative analysis of two agent memory systems, ACE and ALTK-Evolve, focusing on their strategies for consolidating and delivering learned lessons. ALTK-Evolve's contribution is a calibrated delivery mechanism that selects a subset of guidelines based on model capability (e.g., full set for strong models, curated retrieval for weaker ones), contrasting with ACE's approach of always injecting a comprehensive playbook. This difference in delivery strategy leads to significant cost savings (tokens/task) with comparable or improved accuracy, particularly on harder tasks. The post quantifies these differences on specific models (DeepSeek-V3.2 and gpt-oss-120b) and discusses the trade-offs between consolidation methods (embedding-based de-duplication vs. support-conserving clustering) and delivery strategies (fixed comprehensive playbook vs. calibrated retrieval).