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ARXIV:2603.12712 · CAD CODE GENERATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.12712CAD CODE GENERATIONSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
A novel exemplar selection method for improving CAD code generation using In-Context Learning.
Opportunity summary
Pain A novel exemplar selection method for improving CAD code generation using In-Context Learning.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A novel exemplar selection method for improving CAD code generation using In-Context Learning. In-Context Learning (ICL) offers a training-free alternative through task-specific exemplars.
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet they underperform on domain-specific tasks such as Computer-Aided Design (CAD) code generation due to scarce training data. In-Context Learning (ICL) offers a…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We demonstrate that maximizing this objective constitutes submodular maximization and provide a polynomial-time greedy algorithm with a (1-1/e)-approximation guarantee.
CAD Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A novel exemplar selection method for improving CAD code generation using In-Context Learning.
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Paper Pack
10.48550/arXiv.2603.12712A novel exemplar selection method for improving CAD code generation using In-Context Learning.
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet they underperform on domain-specific tasks such as Computer-Aided Design (CAD) code generation due to scarce training data. In-Context Learning (ICL) offers a training-free alternative through task-specific exemplars. However, existing selection strategies prioritize similarity or point-wise diversity, often producing redundant selections that fail to satisfy the compositional requirements of complex CAD design specifications. In this work, we propose knowledge sufficiency as a principled objective for exemplar selection that aims to maximally satisfy all requirements within design specifications. To realize this objective, we introduce Design-Specification Tiling (DST), which quantifies knowledge sufficiency through a surrogate tiling ratio by extracting multi-granular design components and measuring the proportion of query components covered by selected exemplars. We demonstrate that maximizing this objective constitutes submodular maximization and provide a polynomial-time greedy algorithm with a (1-1/e)-approximation guarantee. Extensive experiments demonstrate that DST substantially improves CAD code generation quality, consistently outperforming existing exemplar selection strategies in ICL.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
A novel exemplar selection method for improving CAD code generation using In-Context Learning. In-Context Learning (ICL) offers a training-free alternative through task-specific exemplars.
METHOD
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet they underperform on domain-specific tasks such as Computer-Aided Design (CAD) code generation due to scarce training data. In-Context Learning (ICL) offers a training-free alternative...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We demonstrate that maximizing this objective constitutes submodular maximization and provide a polynomial-time greedy algorithm with a (1-1/e)-approximation guarantee.
WHY NOW
CAD Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A novel exemplar selection method for improving CAD code generation using In-Context Learning. In-Context Learning (ICL) offers a training-free alternative through task-specific exemplars.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet they underperform on domain-specific tasks such as Computer-Aided Design (CAD) code generation due to scarce training data. In-Context Learning (ICL) offers a training-free alternative through task-specific exemplars.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We demonstrate that maximizing this objective constitutes submodular maximization and provide a polynomial-time greedy algorithm with a (1-1/e)-approximation guarantee.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
CAD Code Generation moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A novel exemplar selection method for improving CAD code generation using In-Context Learning.
Segment
CAD Code Generation
Adoption evidence
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Commercial read
7.0/10 public viability
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proof status
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ARTIFACTS
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DEFENSIBILITY
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