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Multimodal Large Language Models (MLLMs) suffer from hallucinations, creating a critical need for Uncertainty Quantification (UQ) to ensure reliable deployment. However, existing approaches struggle to detect uncertainty caused by superficial associations, especially when the query-relevant signal is weak. We mainly at...
GPT-Policy is introduced, a general-agent framework for in-context robot learning that integrates a context compiler that preserves task-relevant visual transitions, a VLM that proposes robot-tool actions, and a constrained controller that verifies and executes each action and reports its outcome.
The Modality Masking Mechanism (M3), an embarrassingly simple, training-only strategy that requires no architectural changes or large-scale robot pretraining, is introduced to improve robustness of query-based VLA policies for bimanual manipulation.
Inspired by Bayesian posterior inference, this work reformulate backdoor detection as a representation-conditioned image likelihood estimation problem parameterized by a conditional diffusion generative model, and fine-tune a pretrained diffusion model, leveraging its generative prior to map data onto the natural image...
A new attack-agnostic, model-agnostic, and modality-agnostic black-box test-time defense paradigm, inspired by the Platonic Representation Hypothesis, that can perform both representation detection and purification, and achieves substantial performance gains across multiple attacks.
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