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The same harness runs across five backend LLMs from three model families, indicating the harness generalizes across backends without tuning, even as different models induce distinct execution styles under the same workflow.
This study formalizes Autonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers that drive model specialization through end-to-end data curation, and charts a path toward agent-driven model specialization.
AutoSciRub is presented, an evaluation-first framework that induces a task-specific executable rubric before research execution and uses it to guide execution, criterion-level verification as well as iterative revision.
This work introduces PRISM Edit, which optimizes a single polysemous representation across temporal contexts and leverages the model's inherent modulation pathway to route it to temporally correct predictions without requiring any architectural modification.
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