AI Weekly Malaysia

IBM Research's ALTK-Evolve framework lets agents self-distill reusable guidelines from past trajectories and inject them at inference time with no weight updates. Testing across eight models reveals agentic memory isn't a switch but a dose: strong models like DeepSeek-V3.2 (671B MoE) gain +9.5pp with the full guideline set, weaker models like gpt-oss-120b gain +16.1pp with selective retrieval at ~50% fewer tokens, and already-saturated models show no measurable gain.

Why: If you're building agents with memory injection, don't assume more context is always better — calibrate the dose to your model tier. For smaller or cheaper models, a curated core plus per-task retrieval is both more accurate and cheaper than dumping all guidelines in. Prompt caching makes even the full-set approach viable in production for frontier models with headroom.