T1: Terminal Agent Reinforcement Learning for Long-Horizon Tasks
Agent usage is shifting toward long-horizon tasks such as coding and scientific discovery, among which terminal tasks...
每天自动聚合 AI 领域最新动态
Agent usage is shifting toward long-horizon tasks such as coding and scientific discovery, among which terminal tasks...
Invasive coronary angiography (CAG) is the gold standard for diagnosing coronary artery disease, but interpretation v...
We launch SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates vi...
Autoregressive video world models enable interactive, long-horizon exploration, but flexible control remains challeng...
Code world models represent worlds as executable programs, but this representation alone does not determine how to co...
Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to r...
We introduce NCP-ArchPreview, a latent-space language model that pushes autoregressive pretraining beyond standard ne...
We study how model post-training and test-time inference design affect natural-language proof generation for hard oly...
Quantization has been widely adopted in LLM training and inference to reduce cost and improve efficiency. However, lo...
Large Language Model (LLM) agents are turning language into real-world effects, making safety necessary against both ...
Late-interaction retrieval is the state-of-the-art for visual document search, but it pays for its accuracy in storag...
Reducing audio-encoder depth lowers the inference cost of speech large language models, but removing complete blocks ...