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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

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.

Ethics in AI and Human-Centric Design

My research group explores the critical intersection of ethics, responsible AI, and human-centered design. We emphasize the importance of understanding diverse perspectives in technology applications, as highlighted in the paper [Translating With Feeling: Centering Translator Perspectives within Translation Technologies](https://arxiv.org/abs/2604.00758). Another noteworthy contribution is [Examining the Effect of Explanations of AI Privacy Redaction in AI-mediated Interactions](https://arxiv.org/abs/2603.24735), which scrutinizes how AI interfaces communicate privacy concerns to users. Additionally, [PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm](https://arxiv.org/abs/2601.08951) proposes a novel approach for assessing the potential harms AI may present to various user groups, fostering more inclusive AI solutions.

Narrative and Storytelling Frameworks

My research group explores the intricacies of narrative analyses and how stories influence perception and understanding. We dive deep into contextual reasoning with the paper [Social Story Frames: Contextual Reasoning about Narrative Intent and Reception](https://arxiv.org/abs/2512.15925), which analyzes how narratives are constructed and interpreted. Furthermore, [The Empirical Variability of Narrative Perceptions of Social Media Texts](https://aclanthology.org/2024.emnlp-main.1113/) investigates differing interpretations of narratives in digital contexts, emphasizing the role of audience perspective. Lastly, the work on [HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs](https://arxiv.org/abs/2405.17633) examines how narrative style impacts empathy in storytelling.

Social Intelligence in AI Agents

My research group explores the dynamics of social intelligence within AI agents and their interactions with humans. A significant contribution in this area is presented in [Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies](http://arxiv.org/abs/2604.15607), which investigates factors that affect the collaboration between humans and AI. Additionally, we analyze how AI can simulate social situations with [Mind the Sim2Real Gap in User Simulation for Agentic Tasks](https://arxiv.org/abs/2603.11245), which underscores the challenges of transferring simulated behaviors to the real world. Our research also includes frameworks for evaluating AI agents, as seen in [OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety](https://arxiv.org/abs/2507.06134).

Complex Reasoning in Language Models

My research group explores advanced reasoning techniques in large language models (LLMs) to enhance their contextual understanding and interaction capabilities. We highlight the innovative approach in [Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations](https://arxiv.org/abs/2511.12001), which examines the consequences of different reasoning methodologies. Further, the research on [ALFA: Aligning LLMs to Ask Good Questions: A Case Study in Clinical Reasoning](https://arxiv.org/abs/2502.14860) illustrates how LLMs can be trained to engage effectively in clinical contexts. Lastly, [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://arxiv.org/abs/2212.10465) focuses on improving dialogues through enhanced commonsense reasoning capabilities.

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