3 citations · 5 across the 11 of their papers we have counts for
13 papers
Memory-Aware Reward Modeling: Learning Human Preferences for LLM Memory Use
Yijiang River Dong, Zexue He, Cheng Yu +9
Memory enables language model assistants to maintain continuity across sessions and generate personalized responses, but knowing more about a user does not necessarily produce resp…
AI Should Facilitate Democratic Deliberation at Scale
José Ramón Enríquez, Jiaxin Pei, Alex Pentland
AI systems can strengthen democracy by supporting deliberation at scale by addressing cognitive, social, platform-design, and market-driven frictions, while preserving human agency…
Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing
Shenzhe Zhu, Haoqian Zhang, Xu Yang +7
Teachers, conference chairs, and public readers all judge writing from limited evidence, seeing only a finished document and not the process that produced it. Final text alone cann…
Learning from Hindsight for VLA Reinforcement Learning
Iris Xu, Sunshine Jiang, John Marangola +8
Reinforcement learning is increasingly used to fine-tune vision-language-action (VLA) models, but robot interaction is expensive and learning becomes highly sample inefficient when…
Learning User Simulators with Turing Rewards
Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu +5
Learning to simulate human users in interactive settings could advance the training of agent assistants, evaluation of personalization systems, research in the social sciences, and…
Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads
Yasmine Omri, Ziyu Gan, Zachary Broveak +6
LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale requires agents to persistentl…
AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
Zhangchen Xu, Junda Chen, Yue Huang +16
Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifac…
How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks
Longju Bai, Zhemin Huang, Xingyao Wang +5
The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that require a significant amount of t…
Multi-User Large Language Model Agents
Shu Yang, Shenzhe Zhu, Hao Zhu +5
Large language models (LLMs) and LLM-based agents are increasingly deployed as assistants in planning and decision making, yet most existing systems are implicitly optimized for a…
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks
Zexue He, Yu Wang, Churan Zhi +11
Existing evaluations of agents with memory typically assess memorization and action in isolation. One class of benchmarks evaluates memorization by testing recall of past conversat…
Generative AI collective behavior needs an interactionist paradigm
Laura Ferrarotti, Gian Maria Campedelli, Roberto Dessì +7
In this article, we argue that understanding the collective behavior of agents based on large language models (LLMs) is an essential area of inquiry, with important implications in…
ID-RAG: Identity Retrieval-Augmented Generation for Long-Horizon Persona Coherence in Generative Agents
Daniel Platnick, Mohamed E. Bengueddache, Marjan Alirezaie +3
Generative agents powered by language models are increasingly deployed for long-horizon tasks. However, as long-term memory context grows over time, they struggle to maintain coher…
Flexible social inference facilitates targeted social learning when rewards are not observable
Robert D. Hawkins, Andrew M. Berdahl, Alex "Sandy" Pentland +3
Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments…