Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite
AI agents hold great real-world promise, with the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and even proposing new…
Improving Attributed Long-form Question Answering with Intent Awareness
Large language models (LLMs) are increasingly being used to generate comprehensive, knowledge-intensive reports. However, while these models are trained on diverse academic papers and reports, they…
On the Reasoning Abilities of Masked Diffusion Language Models
Masked diffusion models (MDMs) for text offer a compelling alternative to traditional autoregressive language models. Parallel generation makes them efficient, but their computational capabilities…
Probabilistic Programs of Thought
LMs are widely used for code generation and mathematical reasoning tasks where they are required to generate structured output. They either need to reason about code, generate code for a given…
Cocoa: Co-Planning and Co-Execution with AI Agents
As AI agents take on increasingly long-running tasks involving sophisticated planning and execution, there is a corresponding need for novel interaction designs that enable deeper human-agent…
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…
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…