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Time series anomaly detection (TSAD) underpins applications in predictive maintenance, finance, and cloud computing, ...
Pre-pretraining language models (LMs) on symbolic data can accelerate and improve natural language acquisition. Howev...
Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-languag...
As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, under...
Test-time scaling improves LLM reasoning by generating and aggregating multiple candidate answers, yet many pipelines...
Modern large language models - transformers and diffusion language models - are built around two canonical algorithmi...
Human input reaches language models by typing or speaking, and each channel leaves a distinct signature: orthographic...
On-policy training has emerged as a powerful post-training paradigm for improving the reasoning capabilities of large...
We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video stream...
Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed progr...
Large language models can solve substantially harder reasoning problems with more inference-time compute. The term "t...
Tool-Integrated Reasoning (TIR) enables LLMs to solve complex tasks through iterative tool interactions. However, exi...