Author

Vaishnav Tadiparthi

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#machine learning Preprint Sep 2026

JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts

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

#machine learning Preprint Sep 2026

Test-Time Unlearning via Sparse Autoencoder

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

Preprint Aug 2026

Bayesian Partner Modelling enables Adaptive Replanning for LLM Coordination

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