# Knowledge Injection

The work introduces AutoDataBench, a controlled testbed that isolates data intelligence under fixed training pipelines and resource budgets across tool-use data diagnosis and repair, retrieval data organization, and knowledge injection, evaluates seven frontier LLMs on iterative data decisions, finds that agent-curated data approaches expert references on tool use and retrieval while strong in-distribution scores do not guarantee out-of-distribution transfer, and shows that reusing optimization trajectories as mid-training data improves downstream coding performance.

The work introduces AutoDataBench, a controlled testbed that isolates data intelligence under fixed training pipelines and resource budgets across tool-use data diagnosis and repair, retrieval data organization, and knowledge injection, evaluates seven frontier LLMs on iterative data decisions, finds that agent-curated data approaches expert references on tool use and retrieval while strong in-distribution scores do not guarantee out-of-distribution transfer, and shows that reusing optimization trajectories as mid-training data improves downstream coding performance.