2 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning
Zhenyu Pan, Yiting Zhang, Zhuo Liu +13
LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to jailbreak, prompt-injection, and…
StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars
Weijian Li, Hong-Yu Chen, Nabeel Rehemtulla +6
Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling. Astronomical time series of stellar fluxe…
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety
Zhenyu Pan, Yiting Zhang, Yutong Zhang +8
Multi-agent systems (MAS) built on multimodal large language models exhibit strong collaboration and performance. However, their growing openness and interaction complexity pose se…
Transformers and Their Roles as Time Series Foundation Models
Dennis Wu, Yihan He, Yuan Cao +2
We give a comprehensive analysis of transformers as time series foundation models, focusing on their approximation and generalization capabilities. First, we demonstrate that there…
Learning Spectral Methods by Transformers
Yihan He, Yuan Cao, Hong-Yu Chen +3
Transformers demonstrate significant advantages as the building block of modern LLMs. In this work, we study the capacities of Transformers in performing unsupervised learning. We…
Transformers Simulate MLE for Sequence Generation in Bayesian Networks
Yuan Cao, Yihan He, Dennis Wu +3
Transformers have achieved significant success in various fields, notably excelling in tasks involving sequential data like natural language processing. Despite these achievements,…