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Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an...
Linear Vision Transformers (ViTs) are designed to replace the attention in Softmax ViTs with the linear-complexity at...
Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Cont...
Looped Transformers reuse one block of layers several times: by spending extra computation they push a model of fixed...
When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnit...
Unified multimodal models can both look at and render images, so in principle they can repair their own generations: ...
One deployed language model must often serve many compute budgets, yet serving each budget still means a separate tra...
Realistic and editable animal fur reconstruction from multi-view images is challenging due to fine-scale detail, self...
Reliable AI safeguards require both control mechanisms that reduce unsafe behavior and monitoring mechanisms that det...
Most modern multimodal large language models (MLLMs) build on a pretrained visual encoder that provides a strong visu...
Diffusion Transformers (DiTs) have established themselves as a scalable backbone for high-fidelity image synthesis. H...
Multi-vector retrievers built on vision-language models lead visual document retrieval (VDR), but they run a multi-bi...