Understanding student behavior in the classroom is essential to improve both pedagogical quality and student engagement. Existing methods for predicting student engagement typically require substantial annotated data to model the diversity of student behaviors, yet privacy concerns often restrict researchers to their own proprietary datasets. Moreover, the classroom context, represented in peers'
Large language models (LLMs) are increasingly used for recommendation reranking, but their listwise predictions can depend on the order in which candidates are presented. This creates a mismatch between the set-based nature of recommendation and the sequence-based computation of decoder-only LLMs, where permuting an otherwise identical candidate set can change item scores and final rankings. Such
Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models, current approaches still yield clinically uninterpretable and weakly discriminative representations, inefficiently capturing global dependencies and neglecting impor
Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics, fiscal periods, and numeric values. However, existing LLM-based rerankers primarily optimize semantic relevance, leading to unstable rankings and opaque decisions on long documents. We propose FinCards, a structured reranking framework that reframes financia
Solving practical multi-depot vehicle routing problems (MDVRP) is a challenging optimization task central to modern logistics, increasingly driven by e-commerce. To address the MDVRP's computational complexity, neural-based combinatorial optimization methods offer a promising scalable alternative to traditional approaches. However, neural-based methods typically rely on rigid architectures and inp
Capital is concentrating in a few outsized pools rather than a broad spread of rounds. The cycle points to balance-sheet scale and institutional control, not early-stage dispersion.
Capital is concentrating in a few outsized pools rather than a broad spread of rounds. The cycle points to balance-sheet scale and institutional control, not early-stage dispersion.
Capital is concentrating in a few outsized pools rather than a broad spread of rounds. The cycle points to balance-sheet scale and institutional control, not early-stage dispersion.
OpenAI accounts for the day’s clearest personnel signal, with founding and senior-leadership moves reinforcing how much trajectory can hinge on a small set of roles. The desk is about control points, not headcount.
OpenAI accounts for the day’s clearest personnel signal, with founding and senior-leadership moves reinforcing how much trajectory can hinge on a small set of roles. The desk is about control points, not headcount.
OpenAI accounts for the day’s clearest personnel signal, with founding and senior-leadership moves reinforcing how much trajectory can hinge on a small set of roles. The desk is about control points, not headcount.
Benchmark updates span general intelligence, multimodal software repair, and verified coding tasks, giving subscribers three different surfaces for comparing model progress. The mix matters because it shows where scores are still moving and where evaluation is tightening.
Benchmark updates span general intelligence, multimodal software repair, and verified coding tasks, giving subscribers three different surfaces for comparing model progress. The mix matters because it shows where scores are still moving and where evaluation is tightening.
Benchmark updates span general intelligence, multimodal software repair, and verified coding tasks, giving subscribers three different surfaces for comparing model progress. The mix matters because it shows where scores are still moving and where evaluation is tightening.