256 citations · 261 across the 19 of their papers we have counts for
16 papers · 1 filter
SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information
Junjie Ye, Zhuohui Sheng, Shaofan Liu +12
Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps)…
IACM-RL: Intent-Aware Context Management and Reinforcement Learning for Complex Tool Invocation under Dynamic Intent Fluctuations
Dingwei Zhu, Jiahan Li, Chengjun Pan +22
Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustness via implicit history sca…
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
AI Can Learn Scientific Taste
Jingqi Tong, Mingzhe Li, Hangcheng Li +20
Scientific discovery depends on expert judgement and foresight, which we call scientific taste: the ability to judge and propose research ideas with the potential for long-term sci…
CL-bench: A Benchmark for Context Learning
Shihan Dou, Ming Zhang, Zhangyue Yin +24
Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn f…
SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents
Yujiong Shen, Yajie Yang, Zhiheng Xi +17
Scientific reasoning inherently demands integrating sophisticated toolkits to navigate domain-specific knowledge. Yet, current benchmarks largely overlook agents' ability to orches…