7 papers
Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance
Can Wang, Haoran Chen, Li Yu +4
The performance bottleneck of agents is increasingly shifting from model capability to the robustness of their execution processes. Tools play a central role as the primary interfa…
RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation
Renzhi Wu, Zikun Cui, Junjie Yang +10
Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet exi…
CMSL: Constructive Multi-Sequence Learning for Recommendation Systems
Zikun Cui, Renzhi Wu, Junjie Yang +10
Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuanc…
Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
Zouying Cao, Jiaji Deng, Li Yu +4
Procedural memory enables large language model (LLM) agents to internalize "how-to" knowledge, theoretically reducing redundant trial-and-error. However, existing frameworks predom…
Ostrakon-VL: Towards Domain-Expert MLLM for Food-Service and Retail Stores
Zhiyong Shen, Gongpeng Zhao, Jun Zhou +10
Multimodal Large Language Models (MLLMs) have recently achieved substantial progress in general-purpose perception and reasoning. Nevertheless, their deployment in Food-Service and…
AgentEvolver: Towards Efficient Self-Evolving Agent System
Yunpeng Zhai, Shuchang Tao, Cheng Chen +10
Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in d…