6 papers
CARE: Context-Aware Ranking Evolution with Executable Scoring Programs for Budgeted Reaction Optimization
Guanyu Liu, Weiyi Kong, Chao Tang +5
High-throughput experimentation can evaluate many reaction conditions, yet combinatorial condition spaces still exceed the available experiment budget. This makes experiment select…
See, Foresee, Act: Intent-Aware World Modeling for Proactive Retail Assistance
Honghui Zhang, Anna Min, Chenmeinian Guo +9
World models allow agents to anticipate action consequences, but proactive assistance also requires interpreting incomplete observations and deciding whether to intervene before a…
Symphony-Coord: Adaptive Routing for Multi-Agent LLM Systems
Zhaoyang Guan, Huixi Cao, Ming Zhong +6
Multi-agent large language model systems can tackle complex multi-step tasks by decomposing work and coordinating specialized behaviors. However, current coordination mechanisms ty…
Adaptive Detector-Verifier Framework for Zero-Shot Polyp Detection in Open-World Settings
Shengkai Xu, Hsiang Lun Kao, Tianxiang Xu +9
Polyp detectors trained on clean datasets often underperform in real-world endoscopy, where illumination changes, motion blur, and occlusions degrade image quality. Existing approa…
MAPGD: Multi-Agent Prompt Gradient Descent for Collaborative Prompt Optimization
Yichen Han, Yuhang Han, Siteng Huang +7
Prompt engineering is crucial for fully leveraging large language models (LLMs), yet most existing optimization methods follow a single trajectory, resulting in limited adaptabilit…
Meta-Learning Reinforcement Learning for Crypto-Return Prediction
Junqiao Wang, Zhaoyang Guan, Guanyu Liu +7
Predicting cryptocurrency returns is notoriously difficult: price movements are driven by a fast-shifting blend of on-chain activity, news flow, and social sentiment, while labeled…