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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.

Ethics and Responsible AI

My research group explores the ethical implications and safety measures necessary for the deployment of AI systems. We investigate frameworks like [OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety](https://arxiv.org/abs/2507.06134), which aims to provide a comprehensive understanding of AI safety in practical contexts. Another important paper, [The Hidden Puppet Master: A Theoretical and Real-World Account of Emotional Manipulation in LLMs](https://arxiv.org/abs/2603.20907), delves into how LLMs can manipulate emotions without users' awareness. Further, we examine biases through works like [Common Sense or Ableism? Rethinking Commonsense Reasoning Through the Lens of Disability](https://aclanthology.org/2026.eacl-short.40/), which challenges traditional frameworks in light of minority perspectives.

Exploring Narrative and Intent

My research group explores the intricate connections between narratives, empathy, and communication. A key contribution in this domain is [Social Story Frames: Contextual Reasoning about Narrative Intent and Reception](https://arxiv.org/abs/2512.15925), which investigates how context shapes the understanding and reception of narratives. Another relevant study, [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://arxiv.org/abs/2405.17633), focuses on the interplay between narrative style and emotional engagement in storytelling. Additionally, we analyze factors influencing narrative perceptions in [The Empirical Variability of Narrative Perceptions of Social Media Texts](https://aclanthology.org/2024.emnlp-main.1113/), which highlights how social media changes how stories are perceived.

Social Intelligence in AI Agents

My research group explores the functioning and evaluation of social intelligence in AI agents through advanced frameworks. The study [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://arxiv.org/abs/2310.11667) proposes methods to assess and enhance social intelligence in AI interactions. Additionally, [SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactions](https://arxiv.org/abs/2506.23046) investigates how embodied agents perceive and understand group dynamics and individual intentions. We also focus on collective behaviors, as illustrated in [The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies](https://arxiv.org/abs/2509.18052), which lays the groundwork for analyzing how AI societies operate as cohesive units.

Innovations in User Interaction with LLMs

My research group explores groundbreaking techniques to improve user interaction with large language models (LLMs). We focus on enhancing these models' effectiveness through innovative methods, such as in [ALFA: Aligning LLMs to Ask Good Questions: A Case Study in Clinical Reasoning](https://arxiv.org/abs/2502.14860), which showcases tailored question-asking mechanisms in clinical settings. Another pivotal study is [Mind the Sim2Real Gap in User Simulation for Agentic Tasks](https://arxiv.org/abs/2603.11245), which highlights the challenges in transitioning simulated behaviors into real-world applications. Further, we analyze user perceptions and preferences in [Let Them Down Easy! Contextual Effects of LLM Guardrails on User Perceptions and Preferences](https://arxiv.org/abs/2506.00195) to ensure that interactions are both safe and satisfying.

More about me