Go from paper to working code

Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs.

Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs focuses on instruction tuning.

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.

InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection focuses on agentic tool use.

Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction is the primary contribution described in this paper.

ViTamin: Designing Scalable Vision Models in the Vision-Language Era is the primary contribution described in this paper.

MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache is the primary contribution described in this paper.

YOLOE: Real-Time Seeing Anything is the primary contribution described in this paper.

Attention-based Extraction of Structured Information from Street View Imagery presents a transformer method.

We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort.

Partial differential equations (PDEs) are fundamental to modeling physical systems, yet solving them remains a complex challenge.

No time to train! Training-Free Reference-Based Instance Segmentation is the primary contribution described in this paper.