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This work introduces BOTTLED, a benchmark in which agents receive an entire unlabelled workload and must complete it under fixed time, compute and LLM API budgets, and finds that strong zero-shot task performance does not reliably translate into strong bottling capabilities.
A hijacking experiment is designed that grafts hidden states of the target model into an oracle trained only on the retain set, which serves as a plug-and-play component that further improves existing methods.
Data attribution methods using gradient similarity are widely used to analyze and select training data for large language models, but what gradient similarity actually measures is debated. Some interpret it as identifying task-relevant skills, while other work reports that surface form is the main factor. We resolve th...
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