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This work introduces \lifd{} (Look, Imagine, Focus, and Do), a framework for persistent, 3D-aware scene memory that learns scene tokens through multi-view agreement, then completes them from a single RGB view and recurrent memory using rectified flow.
Learned world models are becoming essential to general-purpose agents: by predicting action consequences, they support planning and decision-making while reducing reliance on costly trial and error. This reliance creates a fundamental ambiguity: when a world-model-guided decision fails, the trajectory alone may not rev...
StateTrace is proposed, a novel object-centric framework that endows VideoLLMs with an explicit mechanism for hidden state reasoning in long videos, and builds a reusable spatiotemporal state memory that organizes object trajectories, inter-object relations, and state-transition events into a structured reasoning subst...
Trajectory-Aware Commit Gating (TACG), a training-free gate-level decoder that anchors token identities to the base posterior and uses trajectory-aware signals only to decide whether the current proposal is ready to commit, is proposed.
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