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Maarten Sap

I am an assistant professor at CMU's LTI department with a courtesy appointment in HCII, and a part-time research scientist and AI safety lead at the Allen Institute for AI (AI2). My research focuses on (1) measuring and improving AI systems' social and interactional intelligence, (2) assessing and combatting social inequality, safety risks, and socio-cultural biases in human- or AI-generated language, and (3) building narrative language technologies for prosocial outcomes. I was named a 2025 Packard Fellow and a recipient of the 2025 Okawa Research Award.

I received my PhD from the University of Washington where I was advised by Noah Smith and Yejin Choi.
[bio for talks]

Recent updates:

December 2025 🏅📃: Very excited to have our paper Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond) selected for a Best Paper Award at NeurIPS 2025 (Datasets and Benchmarks Track)!! Huge congrats to the first author Liwei Jiang!!!

November 2025 💎🚀: Honored to be a Spring 2025 recipient of the Amazon Research Award for our project on measuring AI agentic safety!

October 2025 🏅⭐: I’m super excited and grateful to announce that I'm part of the 2025 class of Packard Fellows. The Packard Foundation and this fellowship will allow me to explore exciting research directions towards culturally responsible and safe AI 🌍🌈

October 2025 🔍🧑‍🎓: Due to my lab being quite full already, I'm not taking looking for any new students in this upcoming PhD application cycle 😟.

October 2025 🇨🇦🎉: Excited to be attending COLM 2025 in Montreal this October! I'll be giving a talk at the Social Sim Workshop on Unlocking Social Intelligence in AI agents. I'm also thrilled that five papers I co-authored will be presented by my amazing collaborators at COLM: HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions (led by Xuhui Zhou et al.), ALFA: Aligning LLMs to Ask Good Questions: A Case Study in Clinical Reasoning (co-led by Jimin Mun et al.), PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages, Fluid Language Model Benchmarking, and The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains.

August 2025 🌟: Incredibly honored to be one of 7 US recipients of the 2025 Okawa Research Grant from the Okawa Foundation!

August 2025 🧑‍🎓: Welcoming my first postdoc, Vasudha Varadarajan, to the lab!

[older news]

My research group:

Dan Chechelnitsky

CMU Portugal LTI PhD student
co-advised with Chrysoula Zerva

Joel Mire

LTI PhD student

Karina Halevy

LTI PhD student
co-advised with Mona Diab

Jimin Mun

LTI PhD student

Jocelyn Shen

MIT PhD student
co-advised with Cynthia Breazeal

Kynnedy Smith

HCII PhD student
co-advised with Motahhare Eslami

Vasudha Varadarajan

LTI Postdoc

Akhila Yerukola

LTI PhD student

Mingqian Zheng

LTI PhD student
co-advised with Carolyn Rosé

Xuhui Zhou

LTI PhD student

Overarching Research Themes

Themes extracted and images generated with the OpenAI API; there may be inconsistencies.

Navigating AI Ethics and Responsibility

My research group explores the ethical considerations and the implications of artificial intelligence technologies in society. We've examined frameworks like [OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety](https://arxiv.org/abs/2507.06134), which aims to establish safety protocols for AI agents. Another significant contribution is the work on [Let Them Down Easy! Contextual Effects of LLM Guardrails on User Perceptions and Preferences](https://arxiv.org/abs/2506.00195), highlighting how protective measures can shape user experiences. We also discuss the wider societal impacts in 'PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm', emphasizing the importance of understanding multifaceted perspectives on AI interactions.

Understanding Narrative Dynamics

My research group explores how narratives shape human understanding and emotional processing. A key paper, [Social Story Frames: Contextual Reasoning about Narrative Intent and Reception](https://arxiv.org/abs/2512.15925), focuses on how different contexts affect the interpretation of narratives and communication. We also delve into the nuances of personal stories with 'HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs', emphasizing the emotional connections in storytelling. Additionally, our work on 'Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication' examines how individual histories impact the reception of potentially harmful dialogue.

Social Intelligence in AI Interactions

My research group explores the evolving capabilities of AI in understanding and simulating social interactions. The paper [SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactions](https://arxiv.org/abs/2506.23046) is pivotal in examining how AI agents can interpret and engage with diverse social cues. We also scrutinize the limitations of current models in understanding nuanced interactions in 'Is This the Real Life? Is This Just Fantasy? The Misleading Success of Simulating Social Interactions With LLMs', emphasizing potential gaps in AI empathic responses. Moreover, our framework introduced in 'SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents' lays groundwork for measuring social metrics in AI systems.

Advancements in Large Language Models

My research group explores innovative approaches to enhancing the performance and reliability of large language models (LLMs). An important study, [Martingale Score: An Unsupervised Metric for Bayesian Rationality in LLM Reasoning](https://arxiv.org/abs/2512.02914), proposes a new metric for evaluating the reasoning capabilities of LLMs and their alignment with human-like rationality. Additionally, our team investigates the challenges posed by linguistic diversity through the lens of 'Out of Style: RAG's Fragility to Linguistic Variation', which reveals critical vulnerabilities in retrieval-augmented generation models. Lastly, our inquiry into 'Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations' underscores the potential and risks of reasoning capabilities in LLMs.

More about me