Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Perspectra: Choosing Your Experts Enhances Critical Thinking in Multi-Agent Research Ideation
Early-stage interdisciplinary research ideation is often challenged by limited expert access, uncertainty about what to ask, and the cognitive burden of synthesizing unfamiliar domain perspectives.…
Omakase: proactive assistance with actionable suggestions for evolving scientific research projects
As AI agents become increasingly capable of complex knowledge tasks, the lack of context limits their capability to proactively reason about a user's latent needs throughout a long evolving project.…
FloeNet: A mass-conserving global sea ice emulator that generalizes across climates
We introduce FloeNet, a machine-learning emulator trained on the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. FloeNet is a mass-conserving model, emulating 6-hour mass and area…
Meta-Reinforcement Learning with Self-Reflection for Agentic Search
This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single independent…
Deep Research, Shallow Evaluation: A Case Study in Meta-Evaluation for Long-Form QA Benchmarks
Recent advances have made long-form report-generating systems widely available. This has prompted evaluation frameworks that use LLM-as-judge protocols and claim verification, along with…
Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset
AI-powered scientific research tools are rapidly being integrated into research workflows, yet the field lacks a clear lens into how researchers use these systems in real-world settings. We present…
PreScience: A Dataset and Benchmark for Scientific Forecasting
Can AI systems trained on the existing scientific record forecast the advances that will follow? We introduce PreScience, a dataset and benchmark for scientific forecasting built around 98K recent…
Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators
The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typically activated in…
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…