3 citations · 3 across the 6 of their papers we have counts for
7 papers
Learning from Teacher Continuations at Student States
Haojin Wang, Dylan Zhang, Huaibo Chen +8
We present OLIVE (OnLine InterVEntion). At each iteration, the evolving student policy generates a new prefix, the teacher continues it autoregressively, and the student is updated…
Metis: Memory Foundation Model
Zeyu Zhang, Ziliang Guo, Yihang Sun +14
Recent advances in AI agents have increasingly internalized native capabilities into their underlying foundation models, giving rise to multimodal foundation models and large reaso…
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
Xixuan Hao, Zeyu Zhang, Zehao Lin +6
Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions. Existing benchmarks, however, evaluate…
-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues
Peixuan Han, Hongyi Du, Jiayu Liu +3
Personalization is a crucial capability of modern language agents. However, current research primarily positions personalized agents as passive responders to user preferences, limi…
GMTRouter: Personalized LLM Router over Multi-turn User Interactions
Yihang Sun, Encheng Xie, Tao Feng +1
Large Language Model (LLM) routing has demonstrated strong capability in balancing response quality with computational cost. As users exhibit diverse preferences, personalization h…
GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation
Tao Feng, Yihang Sun, Jiaxuan You
The powerful capabilities of Large Language Models (LLMs) have led to their growing use in evaluating human-generated content, particularly in evaluating research ideas within acad…
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Ceyao Zhang, Kaijie Yang, Siyi Hu +12
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely…