Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Understanding the Logic of Direct Preference Alignment through Logic
Recent direct preference alignment algorithms (DPA), such as DPO, have shown great promise in aligning large language models to human preferences. While this has motivated the development of many…
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation
Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore…
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 input lengths, intuitively because their computational depth is bounded. However, prior work…
ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
We investigate the logical reasoning capabilities of large language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a comprehensive…
Understanding the Logic of Direct Preference Alignment through Logic
Recent direct preference alignment algorithms (DPA), such as DPO, have shown great promise in aligning large language models to human preferences. While this has motivated the development of many…
The One RING: a Robotic Indoor Navigation Generalist
Modern robots vary significantly in shape, size, and sensor configurations used to perceive and interact with their environments. However, most navigation policies are embodiment-specific; a policy…
Paloma: A Benchmark for Evaluating Language Model Fit
Language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains$\unicode{x2013}$varying distributions of…
DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents
Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reasoning is challenging…
The Art of Saying No: Contextual Noncompliance in Language Models
Chat-based language models are designed to be helpful, yet they should not comply with every user request. While most existing work primarily focuses on refusal of"unsafe"queries, we posit that the…
MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization
In multilingual settings, non-Latin scripts and low-resource languages are usually disadvantaged in terms of language models' utility, efficiency, and cost. Specifically, previous studies have…