Skeleton Coding uses program structure to steer coding agents
Across four models and three benchmarks, the best skeletons cut mean test error 48.4% versus the best behavioral specs.
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Across four models and three benchmarks, the best skeletons cut mean test error 48.4% versus the best behavioral specs.
FrugalEvo pairs a strong strategist LLM with a cheap implementer, achieving state-of-the-art circle packing for $1.68 and beating costlier baselines.
FrugalEvo pairs a costly strategy model with cheap code refinement and beats evolutionary baselines at far lower spend.
MatLoom turns text prompts into compact executable PBR material programs using pretrained language models, beating diffusion baselines.
RAPID turns one visual human demonstration into verified, reusable robot programs via an agentic coding loop.
Researchers introduce Echo, a matching decompiler using trusted back-translation that roughly doubles exact-match rates and outperforms GPT-5.6 and Codex on Mirai.
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