Thinking of ACE? We Can Do It with Fewer Tokens

TL;DR - An IBM Research post on the Hugging Face blog describing an approach in their ALTK ("evolve"/SLDD) tooling that aims to match ACE-style agentic context engineering while consuming far fewer tokens. Note: only the title/URL were available — the article body could not be retrieved, so the points below are inferences from the title and URL slug, not verified results.

• Framed as a response to ACE (agentic context engineering, where agents iteratively grow and refine a context "playbook"), with the claimed advantage being comparable behavior at lower token cost.
• Positioned as part of IBM Research's ALTK (agent lifecycle toolkit) family, under an "evolve" component with an "SLDD" variant — i.e., a productized/open-source library rather than a standalone paper.
• Core theme is context efficiency for agents: compressing or condensing accumulated context/instructions so prompt length (and thus latency and inference cost) stays bounded as the agent learns.
• Treat any specific benchmark numbers, token-reduction ratios, or accuracy claims as unverified here; the actual post should be read for evidence.