Yang, Chengyu, Zheng, Yunxiang, Li, Zongzheng, Li, Jiamei, Lan, Boxun, Liu, Xiaoyu, Zhang, Xiaoyu, Chen, Xiaohong, Hu, Ming · Oct 29, 2026
Compile Away Uncertainty: Self-Evolving Agent Skills via Programmatic Compilation
Markdown · JSON · Original source
The authors of “Compile Away Uncertainty” argue that agents can reduce repeated reliance on reasoning by compiling well-specified operations in reusable skills into validated programs. Their SkillCompiler framework keeps LLM reasoning for context-dependent operations, while turning suitable operations into programs; it also reuses existing program assets and synthesizes and verifies new ones when needed. The authors report that experiments across five and six model families show improved task performance, with gains of up to 32.33%. The proposed approach matters because it aims to make parts of an agent’s learned procedures executable and independently verifiable, rather than repeatedly interpreted and regenerated. The abstract does not establish how well the reported results transfer beyond those experiments. Which operations can be compiled reliably in settings beyond the reported benchmarks?
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Oct 29, 2026 (2026-10-29).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T20:01:22.357Z. First observed: 2026-10-30T06:42:32.755Z. Retrieved: 2026-10-30T10:32:45.784Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The abstract reports results on five and six model families but does not identify them or establish how well the results transfer to other settings. The abstract does not specify which real-world operations are sufficiently well-specified to compile reliably.
Open question
Which operations can be compiled reliably in settings beyond the reported ?
Source-stated
The authors argue that textual skills executed through LLM reasoning require repeated interpretation and regeneration, introducing uncertainty into each execution.
even previously learned procedures must be repeatedly interpreted and regenerated, introducing avoidable uncertainty into every execution.
Source-stated
SkillCompiler separates operations that can be compiled into validated programs from context-dependent operations that retain LLM reasoning.
LLM reasoning for context-dependent operations while compiling well-specified ones into validated programs.
Source-stated
The framework reuses existing program assets where applicable and synthesizes and verifies new programs when necessary.
retrieves existing implementations whenever applicable, and synthesizes and verifies new programs only when necessary.
Source-stated
The authors report improved task performance across five benchmarks and six model families, with a maximum improvement of 32.33%.
Extensive experiments on five benchmarks and six model families demonstrate that SkillCompiler consistently outperforms state-of-the-art methods, achieving up to 32.33\% improvement in task performance.
Source and contributor context
Yang, Chengyu, Zheng, Yunxiang, Li, Zongzheng, Li, Jiamei, Lan, Boxun, Liu, Xiaoyu, Zhang, Xiaoyu, Chen, Xiaohong, Hu, Ming · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 2f1d2fa1-f8a4-47b8-87d4-674e6c07c4d4.
Prepared by server-excerpt-repair/stream-excerpt-repair-v1 (original draft: gpt-6-luna); grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T20:01:22.357Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 373a09cf5f0f49972f4258dd38de91380e5592692c0fa02dd58fd5b9266a5149. Source text hash: 46a9e9abf3c103c9d4dbabdf1dad819ccfd007e35c875c29b9076f07c86b2e34.
Revision and source-check history
Version 1 · 2026-11-10T20:01:22.357Z
Compile Away Uncertainty: Self-Evolving Agent Skills via Programmatic Compilation
The authors of “Compile Away Uncertainty” argue that agents can reduce repeated reliance on reasoning by compiling well-specified operations in reusable skills into validated programs. Their SkillCompiler framework keeps LLM reasoning for context-dependent operations, while turning suitable operations into programs; it also reuses existing program assets and synthesizes and verifies new ones when needed. The authors report that experiments across five and six model families show improved task performance, with gains of up to 32.33%. The proposed approach matters because it aims to make parts of an agent’s learned procedures executable and independently verifiable, rather than repeatedly interpreted and regenerated. The abstract does not establish how well the reported results transfer beyond those experiments. Which operations can be compiled reliably in settings beyond the reported benchmarks?
Original source
cs.AI · Article
Source details
Original source date: Oct 29, 2026 (2026-10-29).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T20:01:22.357Z. First observed: 2026-10-30T06:42:32.755Z. Retrieved: 2026-10-30T10:32:45.784Z.
Limits
The abstract reports results on five and six model families but does not identify them or establish how well the results transfer to other settings. The abstract does not specify which real-world operations are sufficiently well-specified to compile reliably.
Open question
Which operations can be compiled reliably in settings beyond the reported ?
Source-stated
The authors argue that textual skills executed through LLM reasoning require repeated interpretation and regeneration, introducing uncertainty into each execution.
even previously learned procedures must be repeatedly interpreted and regenerated, introducing avoidable uncertainty into every execution.
Source-stated
SkillCompiler separates operations that can be compiled into validated programs from context-dependent operations that retain LLM reasoning.
LLM reasoning for context-dependent operations while compiling well-specified ones into validated programs.
Source-stated
The framework reuses existing program assets where applicable and synthesizes and verifies new programs when necessary.
retrieves existing implementations whenever applicable, and synthesizes and verifies new programs only when necessary.
Source-stated
The authors report improved task performance across five benchmarks and six model families, with a maximum improvement of 32.33%.
Extensive experiments on five benchmarks and six model families demonstrate that SkillCompiler consistently outperforms state-of-the-art methods, achieving up to 32.33\% improvement in task performance.
Source and contributor context
Yang, Chengyu, Zheng, Yunxiang, Li, Zongzheng, Li, Jiamei, Lan, Boxun, Liu, Xiaoyu, Zhang, Xiaoyu, Chen, Xiaohong, Hu, Ming · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 2f1d2fa1-f8a4-47b8-87d4-674e6c07c4d4.
Prepared by server-excerpt-repair/stream-excerpt-repair-v1 (original draft: gpt-6-luna); grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T20:01:22.357Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 373a09cf5f0f49972f4258dd38de91380e5592692c0fa02dd58fd5b9266a5149. Source text hash: 46a9e9abf3c103c9d4dbabdf1dad819ccfd007e35c875c29b9076f07c86b2e34.
uncertain · 2026-11-10T20:01:22.357Z · anthropic/claude-opus-5.5
Publication: 2f1d2fa1-f8a4-47b8-87d4-674e6c07c4d4
attribution — pass: The argument, the SkillCompiler framework and the reported results are attributed to the paper's authors throughout. The reading does not present the benchmark gains as established fact or as Escape Meridian's view.
we introduce SKillCompiler, a self-evolving skill framework that integrates programmatic compilation into the skill evolution process.
summary — uncertain: The reading is faithful but leaves two pieces of essential context unclear for a non-specialist. First, it never says what a 'skill' is. The source defines skills as textual documents of procedural knowledge that agents accumulate from experience without updating model parameters. Second, it reports 'improved task performance' without naming the comparison. The source says the gains are against state-of-the-art methods and are consistent. Smallest repair: add one clause defining skills, and change the result to 'outperforms state-of-the-art methods'. The question is also repeated at the end of the summary, which is redundant.
such skills are typically represented as textual documents and executed through LLM reasoning
date — pass: The originalPublished date of 2026-10-29 matches the arXiv v1 submission date. The later feed date (Oct 30) is correctly not used.
[Submitted on 29 Oct 2026]
support — pass: Each claim is supported by a verbatim excerpt from the abstract. The 32.33% figure is framed as a reported maximum. The limitations correctly note that the abstract does not name the benchmarks or establish how well the results transfer to other settings. The question follows from that gap and does not imply an unsupported benefit.
Extensive experiments on five benchmarks and six model families demonstrate that SkillCompiler consistently outperforms state-of-the-art methods
media — pass: The selected media is the verified direct link to the original arXiv abstract page, matching the source URL. No other source media was found.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
A language model with many learned numerical settings, used to process and generate language.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA language model with many learned numerical settings, used to process and generate language.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition reference