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The Noisy Test-time Reinforcement Learning framework (NTRL-Code) is proposed, which enables robust self-evolution of code LLMs using only unlabeled noisy data during the testing stage, and employs an abstract-syntax-tree (AST)-based structural aggregation mechanism to estimate a proxy target from multiple candidate pro...
DeepDiscovery is presented, a task-level repository-understanding method for large industrial codebases that uses a two-stage \textit{Location--Inference} framework to localize high-confidence task anchors and recover broader task-relevant context over multi-relational repository structure under budget constraints.
This work proposes a novel deep learning framework that leverages the powerful spatiotemporal information processing capabilities of Transformers and the strong multi-task learning abilities of Mixture of Experts to generate real-time, context-aware audio instructions for TBT driving navigation.
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