8 papers
Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions
Pengyu Zhu, Lijun Li, Longju Yang +2
Deep Research agents conduct long-horizon investigations by iteratively planning, retrieving evidence, and generating reports. However, it remains unclear whether they can resist a…
STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamics
Tingfeng Hui, Hao Xu, Pengyu Zhu +5
Large language models (LLMs) deployed in real-world agentic applications must be capable of replanning and adapting when mid-task disruptions invalidate their prior decisions. Exis…
EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation
Yi Liu, TingFeng Hui, Wei Zhang +4
Scalable AI agents training relies on interactive environments that faithfully simulate the consequences of agent actions. Manually crafted environments are expensive to build, bri…
A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
Kaiwen Luo, Zhenhong Zhou, Leyan Wang +32
Advances in Large Language Models (LLMs) have paved the way for Multimodal Large Language Models (MLLMs). Among these, Large Audio Language Models (LALMs) are essential for realizi…
LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models
Wei Zhang, Lintong Du, Yuanhe Zhang +4
Despite the strong performance of Large Language Models (LLMs) on complex instruction-following tasks, precise control of output length remains a persistent challenge. Existing met…
DecIF: Improving Instruction-Following through Meta-Decomposition
Tingfeng Hui, Pengyu Zhu, Bowen Ping +4
Instruction-following has emerged as a crucial capability for large language models (LLMs). However, existing approaches often rely on pre-existing documents or external resources…
Smaller Language Models Are Better Instruction Evolvers
Tingfeng Hui, Lulu Zhao, Guanting Dong +3
Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they…
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging
Tingfeng Hui, Zhenyu Zhang, Shuohuan Wang +3
Mixture-of-Experts (MoE) shines brightly in large language models (LLMs) and demonstrates outstanding performance in plentiful natural language processing tasks. However, existing…