Thinking of ACE? We Can Do It with Fewer Tokens
The difference is what they do with what they learn — and that decides the token bill. Give an LLM agent a realistic multi-step task — split a bill, find a song, reconcile an order across nine simulated apps — and when it fails, it usually isn't for lack of knowledge. It mis-paginates an API, resolves the wrong person, or returns a value when none was asked for.
- ▪The difference is what they do with what they learn — and that decides the token bill.
- ▪Give an LLM agent a realistic multi-step task — split a bill, find a song, reconcile an order across nine simulated apps — and when it fails, it usually isn't for lack of knowledge.
- ▪It mis-paginates an API, resolves the wrong person, or returns a value when none was asked for.
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Record
| Original publisher | Hugging Face - Blog |
| Canonical URL | https://huggingface.co/blog/ibm-research/altk-evolve-sldd |
| Publication time | Tue, 11 Aug 2026 13:37:10 GMT |
| Retrieval time | 2026-08-11T13:38:11.371Z |
| Last seen | 2026-08-11T13:38:11.371Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | BrQ2sRxWHrKM · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Back to Articles Thinking of ACE? We Can Do It with Fewer Tokens Enterprise Article Published August 11, 2026 Upvote 2 Vatche Isahagian Vatche Follow ibm-research Jayaram Radhakrishnan jayaramkr Follow ibm-research Vinod Muthusamy vinodmut Follow ibm-research Gaodan Fang gaodan-fang Follow ibm-research Punleuk Oum illeatmyhat Follow ibm-research G Thomas gsthomasx Follow ibm-research Ashwath Vaithinathan Aravindan ashwath-vaithina Follow ibm-research Evelyn Duesterwald evduester Follow ibm-research Merve Unuvar mrvnvr Follow ibm-research What we agree on Where we differ Why it matters Same lessons, different delivery Linked artifacts / references Method notes Reference tables ALTK-Evolve and ACE both let an agent learn from its own trajectories.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hugging Face - Blog.
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