5 papers
Enabling Agents to Communicate Entirely in Latent Space
Zhuoyun Du, Runze Wang, Huiyu Bai +6
While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling rich, internal latent states int…
SkillChain: Closing the Loop on Skill Evolution for Image-Based E-Commerce AI Assistants
Yimin Hu, Mengtao Xu, Hao Guo +3
Image-based AI assistants are now deployed at production scale on e-commerce platforms, where a single uploaded image can trigger fundamentally different user intents: product sear…
ReCal: Reward Calibration for RL-based LLM Routing
Qihang Yu, Hanwen Tong, Zhengqi Zhang +5
Large language model (LLM) routing has emerged as an effective paradigm for leveraging the complementary strengths of multiple LLMs through dynamic model and reasoning-strategy sel…
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization
Shan He, Runze Wang, Zhuoyun Du +4
Designing and optimizing multi-agent systems (MAS) is a complex, labor-intensive process of "Agent Engineering." Existing automatic optimization methods, primarily focused on flat…
Online-PVLM: Advancing Personalized VLMs with Online Concept Learning
Huiyu Bai, Runze Wang, Zhuoyun Du +6
Personalized Visual Language Models (VLMs) are gaining increasing attention for their formidable ability in user-specific concepts aligned interactions (e.g., identifying a user's…