ECTraj enhances consistency models for multi-agent trajectory prediction via improved student-teacher supervision and conditional top-K generation, yielding faster inference and competitive accuracy on Argoverse 2.
International Conference on Learning Representations (ICLR) , year=
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Cumulative flow maps unify few-step generative modeling for diffusion and flow models via cumulative transport and parameterization with minimal changes to time embeddings and objectives.
A malicious agent in multi-agent LLM consensus systems can be trained via a surrogate world model and RL to reduce consensus rates and prolong disagreement more effectively than direct prompt attacks.
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Enhancing Consistency Models for Multi-Agent Trajectory Prediction
ECTraj enhances consistency models for multi-agent trajectory prediction via improved student-teacher supervision and conditional top-K generation, yielding faster inference and competitive accuracy on Argoverse 2.
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A Few-Step Generative Model on Cumulative Flow Maps
Cumulative flow maps unify few-step generative modeling for diffusion and flow models via cumulative transport and parameterization with minimal changes to time embeddings and objectives.
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Insider Attacks in Multi-Agent LLM Consensus Systems
A malicious agent in multi-agent LLM consensus systems can be trained via a surrogate world model and RL to reduce consensus rates and prolong disagreement more effectively than direct prompt attacks.