Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of Vision-Language-Action Models via Latent Iterative Reasoning
Current Vision-Language-Action (VLA) models rely on fixed computational depth, expending the same amount of compute on simple adjustments and complex multi-step manipulation. While Chain-of-Thought…
HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model
Kilometer-scale simulations of the atmosphere are an important tool for assessing local weather extremes and climate impacts, but computational expense limits their use to small regions, short…
Leveraging In-Context Learning for Language Model Agents
In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to improve…
A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers
Recent theoretical results show transformers cannot express sequential reasoning problems over long inputs, intuitively because their computational *depth* is bounded. However, prior work treats the…
Exact Expressive Power of Transformers with Padding
Chain of thought is a natural inference-time method for increasing the computational power of transformer-based large language models (LLMs), but comes at the cost of sequential decoding. Are there…
Language Modeling by Language Models
Can we leverage LLMs to model the process of discovering novel language model (LM) architectures? Inspired by real research, we propose a multi-agent LLM approach that simulates the conventional…
Open-ended Scientific Discovery via Bayesian Surprise
The promise of autonomous scientific discovery (ASD) hinges not only on answering questions, but also on knowing which questions to ask. Most recent works in ASD explore the use of large language…
SciArena: An Open Evaluation Platform for Non-Verifiable Scientific Literature-Grounded Tasks
We present SciArena, an open and collaborative platform for evaluating foundation models on scientific literature-grounded tasks. Unlike traditional benchmarks for scientific literature…
Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing
Large Language Models have seen expanding application across domains, yet their effectiveness as assistive tools for scientific writing—an endeavor requiring precision, multimodal synthesis, and…
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following instances for training and evaluation, covering 54 tasks. These tasks span…