Agent in a Bottle: Can LLM Agents Turn Their Capabilities into Cheap Artifacts?
Computer Science > Artificial Intelligence arXiv:2610.08775 (cs) [Submitted on 6 Oct 2026] Title:Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts? Can LLM agents autonomously create cheaper solutions for such workloads? We call this ability "bottling": the ability to turn general capabilities into task-specific solutions that balance answer quality and amortised cost.
- ▪Computer Science > Artificial Intelligence arXiv:2610.08775 (cs) [Submitted on 6 Oct 2026] Title:Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?
- ▪Can LLM agents autonomously create cheaper solutions for such workloads?
- ▪We call this ability "bottling": the ability to turn general capabilities into task-specific solutions that balance answer quality and amortised cost.
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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2610.08775 |
| Publication time | Thu, 08 Oct 2026 01:18:19 +0000 |
| Retrieval time | 2026-10-08T01:23:22.569Z |
| Last seen | 2026-10-08T01:23:22.569Z |
| 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 | E287rD4advXD · 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
Computer Science > Artificial Intelligence arXiv:2610.08775 (cs) [Submitted on 6 Oct 2026] Title:Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts? Authors:Ankit Sonthalia, Haritz Puerto, Alexander Rubinstein, Martin Gubri, Seong Joon Oh View a PDF of the paper titled Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?, by Ankit Sonthalia and 4 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) can solve many narrow tasks, but querying them separately for millions of related instances can be prohibitively expensive.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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