Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
A prevailing view in robot learning is that simulation alone is not enough; effective sim-to-real transfer is widely believed to require at least some real-world data collection or task-specific…
TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation
A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot. In…
VLS: Steering Pretrained Robot Policies via Vision-Language Models
Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted support surface, or amid mild clutter? Such failures rarely reflect missing…
HARPA: A Testability-Driven, Literature-Grounded Framework for Research Ideation
While there has been a surge of interest in automated scientific discovery (ASD), especially with the emergence of LLMs, it remains challenging for tools to generate hypotheses that are both…
LitPivot: Developing Well-Situated Research Ideas Through Dynamic Contextualization and Critique within the Literature Landscape
Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This requires iteratively reviewing literature and refining an…
Context-Aware RL for Agentic and Multimodal LLMs
Large language models (LLMs) often fail when answering requires identifying a small but decisive piece of evidence within a long or complex context, such as a single line in a tool trace or a subtle…
Cracks in the Foundation: Seemingly Minor Architectural Choices Impact Long Context Extension
One might imagine that architectural variations within the dense transformer paradigm have a limited effect on accuracy. However, we demonstrate that this is not the case in the long context…
Olmo Hybrid: From Theory to Practice and Back
Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attention. Yet there is no…
FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models
Recent advances in robotic manipulation have integrated low-level robotic control into Vision-Language Models (VLMs), extending them into Vision-Language-Action (VLA) models. Although…
From Token Probabilities to Semantic Constraints: Towards Declarative Probabilistic Evaluation of Language Models
While Large Language Models have improved rapidly, many fundamental questions remain about how to evaluate the knowledge and reasoning abilities they acquire, and how such evaluations relate to the…