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Coding agents are failing to meet user requests, with a mere 31.5% success rate, highlighting a critical gap in requirement recovery that must be addressed.
Resolve rates mask critical insights about coding agent performance, but TraceProbe uncovers the hidden trajectory structures that explain why some runs succeed while others fail.
Forget retraining: model editing and constrained decoding can keep service recommendations fresh and valid in ever-changing software ecosystems.
Forget parallel corpora: CodePivot shows you can train a 7B model to beat behemoth LLMs at multilingual code transpilation by pivoting through Python and using a clever RL reward.
