arXiv:2509. 21945v2 Announce Type: replace-cross Abstract: To efficiently tune configuration for better software system performance (e.
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arXiv:2607. 16476v1 Announce Type: cross Abstract: Software configuration tuning is crucial for optimising system performance, and various optimisers have emerged over the last decade.
arXiv:2607. 10420v1 Announce Type: cross Abstract: In software analytics, rerunning the same analysis twice often yields different models and conclusions.
arXiv:2606. 17660v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and na\"ive runs can even degrade model performance.
arXiv:2606. 20512v1 Announce Type: cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test suite, which workflows have historically led to wrong fixes) that does not exist in the code itself.
arXiv:2607. 19653v1 Announce Type: cross Abstract: Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases.
arXiv:2605. 22949v3 Announce Type: replace Abstract: Foundation-model pools are increasingly used as black-box responders in coordinated systems where a coordinator must decide which response to trust.