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关键词追踪：code generation benchmark
这个页面会长期追踪你配置里关心的关键词，并把命中的论文按日期沉淀下来。
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最近一次命中来自 Terminal and SWE Agents：VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination
2026-06-15
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命中明细
按日期回看匹配到这个关键词的论文标题，并保留来源 feed 信息。
2026-06-17
VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination
MDLMs generate text by denoising a preallocated masked response canvas, making response-length modeling central to instruction tuning. Existing MDLMs often inherit the autoregress…
2026-06-16
No Resource, No Benchmarks, No Problem? Evaluating and Improving LLMs for Code Generation in No-Resource Languages
Large Language Models (LLMs) have significantly advanced the automation of software engineering tasks. One prominent example is code generation, where an LLM produces code in a sp…
2026-06-05
Closing the Loop on Latent Reasoning via Test-Time Reconstruction
Recent work moves intermediate reasoning from natural-language traces into latent or cache-level representations to reduce token overhead and avoid a discrete communication bottle…
2026-05-18
Syntax Without Semantics: Teaching Large Language Models to Code in an Unseen Language
Large language models (LLMs) achieve high pass rates on code generation benchmarks, yet whether they can transfer this ability to languages absent from pretraining remains poorly…