World models enable agents to plan by predicting future states of the environment, but their predictions can become unreliable when test-time dynamics differ from those seen during training. We present JEPA-TTT, which adapts the latent dynamics predictor of a pretrained action-conditioned Joint-Embedding Predictive Arc...
Zheyuan Zhang, Su-Yu Ye, Nakul Agarwal et al.· 0 citations
This work proposes ARIA (autoencoder-gated inference-time unlearning), a test-time unlearning method that leaves model weights intact and gates access to unwanted knowledge only when generation enters a forget-related state and introduces three post-unlearning adversarial attacks targeting weight-space and decoding-spa...
Ping-Zhi Li, Jinhao Duan, Vaishnav Tadiparthi et al.· 0 citations
BayesBeliefAgent is introduced, which pairs a hierarchical LLM planner with a Bayesian tracking module and evaluates performance using replanning efficiency and the belief-action gap: the fraction of total decisions where an agent with a correct partner estimate executes a non-complementary skill.
Harsh Goel, Aditya Sai Ellendula, Vaishnav Tadiparthi et al.· 1 citation
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