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Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function. Howe...
Symbolic Regression (SR) aims to discover analytical equations from observational data and plays a central role in sc...
Games and simulators make valuable benchmarks by turning decisions into measurable outcomes, but many current suites ...
The Abstraction and Reasoning Corpus (ARC) tests whether a model can infer an unseen transformation from a few input-...
Reading a social situation often depends on behavior, not words alone. We introduce FriendBench, a benchmark for infe...
The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable ...
Imitation learning (IL)---training an agent to replicate expert behavior from demonstrations---underpins applications...
Convolutional neural networks (CNNs) are widely used for time-series classification, but their deployment in critical...
Traditional static assessments rely on a subtractive, deficit-based grading model that often penalizes ambition and o...
As LLMs evolve from code completion systems into autonomous scientific agents, evaluating their ability to conduct ex...
Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency,...
Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-def...