Agentic Compilation: Reducing LLM Rerun Costs
The paper discusses a new architecture called Compile-and-Execute aimed at reducing the costs associated with LLM-driven web automation. This approach addresses the Rerun Crisis by decoupling model reasoning from browser execution, significantly lowering inference costs. Empirical evaluations show high success rates in various tasks, making this method a viable solution for economically scalable automation.
- ▪LLM-driven web agents face a scalability constraint known as the Rerun Crisis, leading to high inference costs.
- ▪The proposed Compile-and-Execute architecture reduces per-workflow inference costs to under 0.10 USD.
- ▪Empirical evaluations indicate zero-shot compilation success rates between 80-94% across different tasks.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2604.09718 |
| Publication time | Sat, 23 May 2026 21:39:58 +0000 |
| Retrieval time | 2026-05-23T21:52:27.757Z |
| Last seen | 2026-05-23T21:52:27.757Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| 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 | 8HSm29UdG6d9 |
| 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 |
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| 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 > Distributed, Parallel, and Cluster Computing arXiv:2604.09718 (cs) [Submitted on 8 Apr 2026 (v1), last revised 25 Apr 2026 (this version, v2)] Title:Agentic Compilation: Mitigating the LLM Rerun Crisis for Minimized-Inference-Cost Web Automation Authors:Jagadeesh Chundru View a PDF of the paper titled Agentic Compilation: Mitigating the LLM Rerun Crisis for Minimized-Inference-Cost Web Automation, by Jagadeesh Chundru View PDF HTML (experimental) Abstract:LLM-driven web agents operating through continuous inference loops -- repeatedly querying a model to evaluate browser state and select actions -- exhibit a fundamental scalability constraint for repetitive tasks.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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