6 papers
GTA-2: Benchmarking General Tool Agents from Atomic Tool-Use to Open-Ended Workflows
Jize Wang, Xuanxuan Liu, Yining Li +7
The development of general-purpose agents requires a shift from executing simple instructions to completing complex, real-world productivity workflows. However, current tool-use be…
Partial Feedback Online Learning
Shihao Shao, Cong Fang, Zhouchen Lin +1
We study a new learning protocol, termed partial-feedback online learning, where each instance admits a set of acceptable labels, but the learner observes only one acceptable label…
RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents
Jize Wang, Han Wu, Zhiyuan You +9
Mixture-of-Agents (MoA) improves LLM performance through layered collaboration, but its dense topology raises costs and latency. Existing methods employ LLM judges to filter respon…
Binding Agent ID: Unleashing the Power of AI Agents with accountability and credibility
Zibin Lin, Shengli Zhang, Guofu Liao +2
Autonomous AI agents lack traceable accountability mechanisms, creating a fundamental dilemma where systems must either operate as ``downgraded tools'' or risk real-world abuse. Th…
VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs
Guofu Liao, Taotao Wang, Shengli Zhang +3
Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy,…
EmbedGrad: Gradient-Based Prompt Optimization in Embedding Space for Large Language Models
Xiaoming Hou, Jiquan Zhang, Zibin Lin +2
Effectively adapting powerful pretrained foundation models to diverse tasks remains a key challenge in AI deployment. Current approaches primarily follow two paradigms:discrete opt…