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This paper proposes a mathematical formulation of slow thinking and active perception, introducing a theory called 'active lifting' that derives design, training, and inference processes for slow thinking large language models.
The paper introduces a certified sensing clock for world models that provides a proactive, analytically-derived deadline for when an agent must re-sense, based on the model's validity horizon. It proposes a drift-aware deployment method and demonstrates its effectiveness on a frozen 3D VN-JEPA model, reducing tail violations compared to baseline schedulers.
Introduces OmniAgent, an omni-modal agent that uses an iterative Observation-Thought-Action cycle with active perception to achieve superior long video understanding, outperforming larger models like Qwen2.5-VL-72B on benchmarks.
Co-GLANCE is a real-time onboard perception and decision-making system for heterogeneous robot teams that distills vision-language model capabilities into efficient models and uses conformal prediction with selective abstention to quantify and resolve perceptual uncertainty, outperforming cloud-based VLM baselines by 25-36% while achieving 350x lower latency.
ActiveMimic is a pretraining framework that recovers camera and wrist trajectories from egocentric human video to model active perception as a viewpoint action, enabling robot pretraining that matches the performance of models trained directly on robot data.