Agentic information retrieval

W Zhang, J Liao, N Li, K Du - arxiv preprint arxiv:2410.09713, 2024 - arxiv.org
What will information entry look like in the next generation of digital products? Since the
1970s, user access to relevant information has relied on domain-specific architectures of …

Multi-llm-agent systems: Techniques and business perspectives

Y Yang, Q Peng, J Wang, W Zhang - arxiv preprint arxiv:2411.14033, 2024 - arxiv.org
In the era of (multi-modal) large language models, most operational processes can be
reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and …

Hammerbench: Fine-grained function-calling evaluation in real mobile device scenarios

J Wang, J Zhou, M Wen, X Mo, H Zhang, Q Lin… - arxiv preprint arxiv …, 2024 - arxiv.org
Evaluating the capabilities of large language models (LLMs) in human-LLM interactions
remains challenging due to the inherent complexity and openness of dialogue processes …

Boosting Tool Use of Large Language Models via Iterative Reinforced Fine-Tuning

Y Zeng, X Ding, Y Wang, W Liu, W Ning, Y Hou… - arxiv preprint arxiv …, 2025 - arxiv.org
Augmenting large language models (LLMs) with external tools is a promising approach to
enhance their capabilities. Effectively leveraging this potential for complex tasks hinges …

ACEBench: Who Wins the Match Point in Tool Learning?

C Chen, X Hao, W Liu, X Huang, X Zeng, S Yu… - arxiv preprint arxiv …, 2025 - arxiv.org
Large language models (LLMs) have demonstrated significant potential in decision-making
and reasoning, especially when combined with various tools to effectively solve complex …

Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond

S Han - arxiv preprint arxiv:2410.18114, 2024 - arxiv.org
The advancements in generative AI inevitably raise concerns about their risks and safety
implications, which, in return, catalyzes significant progress in AI safety. However, as this …