2 citations · 6 across the 9 of their papers we have counts for
11 papers
Can Agents Design Libraries for Agents?
Gabriel Orlanski, Alex L. Zhang, Avi Trost +4
Agents increasingly build on code written by other agents, and they reimplement rather than reuse, growing the codebases later agents must work in. To measure how well agents desig…
Prime Agent: A Self-Improving RLM Harness
Seth Karten, Alex L. Zhang, Kevin Thomas +8
Language models are sequential processors, but long-horizon agency requires external information and computation beyond model weights and active context. Prime Agent is an open-sou…
Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels
Wandong Sun, Yongbo Su, Leoric Huang +10
Achieving robust vision-based humanoid locomotion remains challenging due to two fundamental issues: the sim-to-real gap introduces significant perception noise that degrades perfo…
Recursive Language Models
Alex L. Zhang, Tim Kraska, Omar Khattab
We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a genera…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
VideoGameBench: Can Vision-Language Models complete popular video games?
Alex L. Zhang, Thomas L. Griffiths, Karthik R. Narasimhan +1
Vision-language models (VLMs) have achieved strong results on coding and math benchmarks that are challenging for humans, yet their ability to perform tasks that come naturally to…
CodeCriticBench: A Holistic Code Critique Benchmark for Large Language Models
Alexander Zhang, Marcus Dong, Jiaheng Liu +15
The critique capacity of Large Language Models (LLMs) is essential for reasoning abilities, which can provide necessary suggestions (e.g., detailed analysis and constructive feedba…
KernelBench: Can LLMs Write Efficient GPU Kernels?
Anne Ouyang, Simon Guo, Simran Arora +4
Efficient GPU kernels are crucial for building performant machine learning architectures, but writing them is a time-consuming challenge that requires significant expertise; theref…
SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?
John Yang, Carlos E. Jimenez, Alex L. Zhang +10
Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which…
Language-Guided World Models: A Model-Based Approach to AI Control
Alex Zhang, Khanh Nguyen, Jens Tuyls +2
This paper introduces the concept of Language-Guided World Models (LWMs) -- probabilistic models that can simulate environments by reading texts. Agents equipped with these models…
Appearance Codes using Joint Embedding Learning of Multiple Modalities
Alex Zhang, Evan Dogariu
The use of appearance codes in recent work on generative modeling has enabled novel view renders with variable appearance and illumination, such as day-time and night-time renders…