8 papers
AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression
Zishan Bai, Hanxuan Chen, Jiayi Gu +9
Cloud-native systems have made operational work both more powerful and harder to automate: incidents unfold across microservices, logs and metrics arrive faster than operators can…
Difficulty-Aware Agentic Orchestration for Query-Specific Multi-Agent Workflows
Jinwei Su, Qizhen Lan, Yinghui Xia +6
Large Language Model (LLM)-based agentic systems have shown strong capabilities across various tasks. However, existing multi-agent frameworks often rely on static or task-level wo…
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…
Symphony: A Decentralized Multi-Agent Framework for Scalable Collective Intelligence
Ji Wang, Kashing Chen, Xinyuan Song +4
Most existing Large Language Model (LLM)-based agent frameworks rely on centralized orchestration, incurring high deployment costs, rigid communication topologies, and limited adap…
GoalfyMax: A Protocol-Driven Multi-Agent System for Intelligent Experience Entities
Siyi Wu, Zeyu Wang, Xinyuan Song +3
Modern enterprise environments demand intelligent systems capable of handling complex, dynamic, and multi-faceted tasks with high levels of autonomy and adaptability. However, trad…
Gradientsys: A Multi-Agent LLM Scheduler with ReAct Orchestration
Xinyuan Song, Zeyu Wang, Siyi Wu +2
We present Gradientsys, a next-generation multi-agent scheduling framework that coordinates diverse specialized AI agents using a typed Model-Context Protocol (MCP) and a ReAct-bas…
MountainLion: A Multi-Modal LLM-Based Agent System for Interpretable and Adaptive Financial Trading
Siyi Wu, Junqiao Wang, Zhaoyang Guan +11
Cryptocurrency trading is a challenging task requiring the integration of heterogeneous data from multiple modalities. Traditional deep learning and reinforcement learning approach…
SDF-TopoNet: A Two-Stage Framework for Tubular Structure Segmentation via SDF Pre-training and Topology-Aware Fine-Tuning
Siyi Wu, Leyi Zhao, Haotian Ma +1
Accurate segmentation of tubular and curvilinear structures, such as blood vessels, neurons, and road networks, is crucial in various applications. A key challenge is ensuring topo…