Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Intent-Aware Schema Generation And Refinement For Literature Review Tables
The increasing volume of academic literature makes it essential for researchers to organize, compare, and contrast collections of documents. Large language models (LLMs) can support this process by…
Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text Inputs in Multimodal LLMs
Large language models (LLMs) and their multimodal variants can now process visual inputs, including images of text. This raises an intriguing question: can we compress textual inputs by feeding them…
MoNaCo: More Natural and Complex Questions for Reasoning Across Dozens of Documents
Automated agents, powered by Large language models (LLMs), are emerging as the go-to tool for querying information. However, evaluation benchmarks for LLM agents rarely feature natural questions…
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…
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…
Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning
Large language models (LLMs) often fail to ask effective questions under uncertainty, making them unreliable in domains where proactive information-gathering is essential for decisionmaking. We…
HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions
AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks. We present HAICOSYSTEM, a framework examining AI agent safety…
ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Data
Language models (LMs) can memorize and reproduce segments from their pretraining data verbatim even in non-adversarial settings, raising concerns about copyright, plagiarism, privacy, and…
SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other…
FlexOlmo: Open Language Models for Flexible Data Use
We introduce FlexOlmo, a new class of language models (LMs) that supports (1) distributed training without data sharing, where different model parameters are independently trained on closed…