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Learning Adaptive Parallel Execution for Efficient Code Localization

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Learning Adaptive Parallel Execution for Efficient Code Localization

Announce Type: replace Abstract: Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9% redundant invocation rate, which negates parallelism benefits. We propose FuseSearch, reformulating parallel code localization as a joint quality-efficiency optimization} task.

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Microsoft’s AI chief says superintelligence is near, but won’t take your job

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Ask HN: What are tools you have made for yourself since the advent of AI?

I've made a number of ceramic molds for slumping fused glass into bowls. As well as wooden templates for ceramic mugs. I've devised a few carrying tools to move glass frit paintings from my studio down to my barn where the kilns sit without spilling the glass.

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