5 papers
LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries
Ming Yin, Dinghan Shen, Silei Xu +11
Tool calling has emerged as a critical capability for AI agents. In contrast to conventional tool calling frameworks that rely on static, provider-specific tool definitions, the Mo…
LogicIF: Towards Complex Logic Instruction Following
Mian Zhang, Shujian Liu, Sixun Dong +10
Instruction following has catalyzed the recent era of Large Language Models (LLMs) and is the foundational skill underpinning more advanced capabilities such as reasoning and agent…
From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs
Jiaxiang Chen, Zhuo Wang, Mingxi Zou +4
Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration,…
TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning
Simin Ma, Shujian Liu, Jun Tan +7
Diverse instruction data is vital for effective instruction tuning of large language models, as it enables the model to generalize across different types of inputs . Building such…
Developing a Reliable, Fast, General-Purpose Hallucination Detection and Mitigation Service
Song Wang, Xun Wang, Jie Mei +6
Hallucination, a phenomenon where large language models (LLMs) produce output that is factually incorrect or unrelated to the input, is a major challenge for LLM applications that…