16 citations
- Beihang UniversityCN2 papers
- Chinese Academy of SciencesCN2 papers
- Xiangtan UniversityCN2 papers
- Beijing Academy of Quantum Information SciencesCN1 paper
- Beijing Institute of TechnologyCN1 paper
- Carnegie Mellon UniversityUS1 paper
- Caspian UniversityKZ1 paper
- Central University of Finance and EconomicsCN1 paper
- Development Agency of SerbiaRS1 paper
- Guizhou UniversityCN1 paper
- Harbin Institute of TechnologyCN1 paper
- Institute of Modern PhysicsCN1 paper
14 papers
Maestro: Workload-Aware Cross-Cluster Scheduling for LLM-Based Multi-Agent Systems
Jinghao Wang, Xiao Zhou, Xiaoyang Sun +6
Large Language Model based Multi-Agent Systems (LLM-MAS) have emerged as a powerful paradigm for tackling complex tasks by breaking them into collaborative workflows of specialized…
Spectroscopic orbits for three SB2s and one hierarchical triple using SALT data
Mikhail Kovalev, Alexey Kniazev, Oleg Malkov
We confirmed four spectroscopic binary candidates using new observations obtained with SALT. Three SB2 systems (HD 20784, HD 43519A, HD 62153A) exhibit circular orbits with periods…
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection
Zhiyong Cao, Dunqiang Liu, Qi Dai +9
Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-med…
Implementation of transformer-based LLMs with large-scale optoelectronic neurons on a CMOS compatible platform
Neil Na, Chih-Hao Cheng, Shou-Chen Hsu +7
The recent rapid deployment of datacenter infrastructures for performing large language models (LLMs) and related artificial intelligence (AI) applications in the clouds is predict…
Hamiltonian with Energy Levels Corresponding to Riemann Zeros
Xingpao Suo
A Hamiltonian with eigenenergy \( E_n = ρ_n(1 - ρ_n) \) has been constructed, where \( ρ_n \) denotes the \( n \)-th non-trivial zero of the Riemann zeta function. To construct suc…
On Vanishing Variance in Transformer Length Generalization
Ruining Li, Gabrijel Boduljak, Jensen +1
It is a widely known issue that Transformers, when trained on shorter sequences, fail to generalize robustly to longer ones at test time. This raises the question of whether Transf…