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LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolutio...
Simulating realistic user shopping behavior underpins offline evaluation and reinforcement learning in e-commerce sce...
We examine how hadith computational science is being reshaped by transformer models, retrieval-grounded pipelines, an...
Agents learn to act through interaction with environments, yet the environments used for training are often manually ...
Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continu...
Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computa...
On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's ow...
Real-world image search queries are multimodal and compositional: ``find this shirt in pink'' specifies an entity to ...
Small language models are usually built like large ones and then squeezed onto a CPU afterwards. We did the opposite:...
Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditionin...
Take three frontier mixture-of-experts models (Alibaba, OpenAI, NVIDIA; 3.6-4.0B active parameters each) and fine-tun...
Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be gras...