6 papers
What Do Agents Learn from Trajectory-SFT: Semantics or Interfaces?
Weizheng Gu, Chengze Li, Zhuohao Yu +6
Large language models are increasingly evaluated as interactive agents, yet standard agent benchmarks conflate two qualitatively distinct sources of success: semantic tool-use and…
A Lightweight Sparse Interaction Network for Time Series Forecasting
Xu Zhang, Qitong Wang, Peng Wang +1
Recent work shows that linear models can outperform several transformer models in long-term time-series forecasting (TSF). However, instead of explicitly performing temporal intera…
Discovering Process-Outcome Credit in Multi-Step LLM Reasoning
Xiangwei Wang, Wei Wang, Ken Chen +2
Reinforcement Learning (RL) serves as a potent paradigm for enhancing reasoning capabilities in Large Language Models (LLMs), yet standard outcome-based approaches often suffer fro…
Meanshift Shape Formation Control Using Discrete Mass Distribution
Yichen Cai, Yuan Gao, Pengpeng Li +3
The density-distribution method has recently become a promising paradigm owing to its adaptability to variations in swarm size. However, existing studies face practical challenges…
Sparse Shortcuts: Facilitating Efficient Fusion in Multimodal Large Language Models
Jingrui Zhang, Feng Liang, Yong Zhang +3
With the remarkable success of large language models (LLMs) in natural language understanding and generation, multimodal large language models (MLLMs) have rapidly advanced in thei…
On-chip electrically reconfigurable octave-bandwidth optical amplification from visible to near-infrared
Guanyu Han, Wenjun Deng, Yu Wang +8
Achieving broadband on-chip optical amplification spanning the visible and near-infrared (NIR) can enable diverse quantum sensing, metrology, and classical communication applicatio…