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Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization
arXiv:2605.31508v1 Announce Type: new Abstract: Video Object-Centric Learning (OCL) aims to represent objects as \textit{slot} vectors and maintain their consistency across frames. Slot-Slot Contrastive (SSC) loss has become the cornerstone for state-of-the-art (SOTA) video OCL methods. While highly effective, SSC relies on one-to-one object correspondence across frames and introduces an extra loss.
Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems
arXiv:2606.04306v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed in workflows where generated outputs may directly trigger state-changing actions. This creates an execution-boundary problem: proposed actions must be governed before they are executed. We study this problem through economically consequential multi-agent interactions and argue that deployment-grade agent systems should separate proposal generation from environment-facing execution.
Man 'hours away from losing his vision' urges people to check eyes for symptom
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