37 citations · 47 across the 15 of their papers we have counts for
16 papers
When Successful Strategies Fail: Adaptation to Environmental Novelty in Terminal Agents
Janvijay Singh, Vaishnavi Shrivastava, Dilek Hakkani-Tur +2
LLM agents increasingly solve long-horizon tasks by autonomously interacting with their environment. In doing so, their strategies rely on assumptions about that environment: which…
From Passive Delegates to Strategic Negotiators: Reinforcing Social Reasoning in Small Language Models with SocialRL
Wenyue Hua, Zachary Huang, Tyler Payne +3
AI agents increasingly act on their users' behalf, handling tasks such as scheduling meetings, comparing offers, and haggling over prices. These principal-driven tasks routinely pl…
HorizonBench: Long-Horizon Personalization with Evolving Preferences
Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar +9
User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this prob…
Learning to Interrupt in Language-based Multi-agent Communication
Danqing Wang, Da Yin, Ruta Desai +3
When a colleague starts explaining something you already understand, you interrupt them. This simple act, a listener taking control of the conversation, is natural in human communi…
Cold-Start Personalization via Training-Free Priors from Structured World Models
Avinandan Bose, Shuyue Stella Li, Faeze Brahman +6
Cold-start personalization requires inferring user preferences through interaction when no user-specific historical data is available. The core challenge is a routing problem: each…
Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents
Zhihan Liu, Lin Guan, Yixin Nie +6
Generalist LLM agents are often post-trained on a narrow set of environments but deployed across far broader, unseen domains. In this work, we investigate the challenge of agentic…
Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang +9
Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reas…
Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
Dong Wang, Yang Li, Ansong Ni +12
Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sensitive. Many such generation task…
The Majority is not always right: RL training for solution aggregation
Wenting Zhao, Pranjal Aggarwal, Swarnadeep Saha +3
Scaling up test-time compute, by generating multiple independent solutions and selecting or aggregating among them, has become a central paradigm for improving large language model…
PrefPalette: Personalized Preference Modeling with Latent Attributes
Shuyue Stella Li, Melanie Sclar, Hunter Lang +7
Personalizing AI systems requires understanding not just what users prefer, but the reasons that underlie those preferences - yet current preference models typically treat human ju…
Embodied AI Agents: Modeling the World
Pascale Fung, Yoram Bachrach, Asli Celikyilmaz +18
This paper describes our research on AI agents embodied in visual, virtual or physical forms, enabling them to interact with both users and their environments. These agents, which…
EgoToM: Benchmarking Theory of Mind Reasoning from Egocentric Videos
Yuxuan Li, Vijay Veerabadran, Michael L. Iuzzolino +3
We introduce EgoToM, a new video question-answering benchmark that extends Theory-of-Mind (ToM) evaluation to egocentric domains. Using a causal ToM model, we generate multi-choice…
reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs
Zhaofeng Wu, Michihiro Yasunaga, Andrew Cohen +3
Reward models have become a staple in modern NLP, serving as not only a scalable text evaluator, but also an indispensable component in many alignment recipes and inference-time al…
Adaptive Decoding via Latent Preference Optimization
Shehzaad Dhuliawala, Ilia Kulikov, Ping Yu +4
During language model decoding, it is known that using higher temperature sampling gives more creative responses, while lower temperatures are more factually accurate. However, suc…
An Introduction to Vision-Language Modeling
Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay +38
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guid…
Towards Safety and Helpfulness Balanced Responses via Controllable Large Language Models
Yi-Lin Tuan, Xilun Chen, Eric Michael Smith +5
As large language models (LLMs) become easily accessible nowadays, the trade-off between safety and helpfulness can significantly impact user experience. A model that prioritizes s…