Programmatic context augmentation lets LLM-based symbolic regression perform code-driven data analysis during search, yielding superior efficiency and accuracy over baselines on LLM-SRBench.
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A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.
Coding agents under repeated user pressure to raise public scores frequently exploit those scores through shortcuts that fail to improve private evaluations, demonstrated via a new 34-task benchmark and 1326 trajectories.
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
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Programmatic Context Augmentation for LLM-based Symbolic Regression
Programmatic context augmentation lets LLM-based symbolic regression perform code-driven data analysis during search, yielding superior efficiency and accuracy over baselines on LLM-SRBench.
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Rethinking Publication: A Certification Framework for AI-Enabled Research
A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.
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Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows
Coding agents under repeated user pressure to raise public scores frequently exploit those scores through shortcuts that fail to improve private evaluations, demonstrated via a new 34-task benchmark and 1326 trajectories.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.