Agents
TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration
Unfortunately, these models are "isolated agents" with heterogeneous
Unravelling in Collaborative Learning
Collaborative learning offers a promising avenue for leveraging decentralized data. However, collaboration in groups of strategic learners is not a given.
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving Chang Gao
It employs four LLM-based agents: strategy generator, executor, optimizer, and evaluator, working together to generate, evaluate, and select promising strategies for a given task. Experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT -SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (34.2%
Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving
Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent scene uncertainty and heterogeneous interaction.
PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
As language models (LMs) are widely utilized in personalized communication scenarios ( e.g., sending emails, writing social media posts) and endowed with a
