Maarten Sap Twitter Profile Google Scholar Profile Semantic Scholar Profile

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

Malia Morgan

Pre-doctoral Young Investigator at Ai2

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.

Human-centered AI and responsibility

My research group explores how to build AI systems that are safer, more transparent, and better aligned with human values in real interactions. A key thread is understanding when people trust or reject AI behavior, as in [When Should AI Read the Room? Public Perceptions of Social Intelligence in AI Agents](https://arxiv.org/abs/2605.29938), which studies public expectations for socially aware agents. We also examine practical safety and harm prevention through work like [OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety](https://arxiv.org/abs/2507.06134) and [PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm](https://arxiv.org/abs/2601.08951). Together, these papers reflect ongoing research on evaluation frameworks, user perceptions, and the responsible deployment of AI in human-centered settings.

Narratives and story understanding

My research group explores how AI can analyze, model, and respond to narrative structure, intent, and emotional nuance in stories and personal accounts. Work such as [Social Story Frames: Contextual Reasoning about Narrative Intent and Reception](https://arxiv.org/abs/2512.15925) focuses on how narrative framing shapes interpretation and audience response. We also study affective and communicative dimensions of stories through [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://arxiv.org/abs/2405.17633) and [Modeling Empathic Similarity in Personal Narratives](https://arxiv.org/abs/2305.14246). This line of research helps connect language models to deeper forms of story comprehension, including empathy, reception, and contextual meaning.

Social intelligence and agent interaction

My research group explores how AI agents reason about people, relationships, and group dynamics in multi-step interactions. A central focus is theory of mind and information management, highlighted by [SOTOPIA-ToM: Evaluating Information Management in Multi-Agent Interaction with Theory of Mind](https://arxiv.org/abs/2605.02307) and [Mind the Sim2Real Gap in User Simulation for Agentic Tasks](https://arxiv.org/abs/2603.11245). We also study how to train and evaluate socially adaptive systems with [Reinforcing Human Behavior Simulation via Verbal Feedback](https://arxiv.org/abs/2605.20506). These papers show a broader effort to make agentic systems more socially aware, more realistic in simulation, and better at modeling human intentions during interaction.

LLM reasoning and adaptation

My research group explores how large language models can become more robust, adaptable, and reliable across changing contexts and tasks. A major direction is improving interaction quality and reducing brittle behavior, as seen in [Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations](https://arxiv.org/abs/2604.27093). We also investigate how models handle ambiguity and uncertainty in [Out of Style: RAG's Fragility to Linguistic Variation](https://arxiv.org/abs/2504.08231) and [Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations](https://arxiv.org/abs/2511.12001). Overall, this theme reflects ongoing work on robustness, reasoning behavior, and adaptation to diverse user and data conditions.

More about me