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This work introduces Mixture-of-Experts for Communication-Aware Incremental Learning (MECAIL), the first method that meets this strict requirement, in which each new domain or environment is served by a small expert network that adapts the base model.
SceneBench is introduced, a benchmark of 966 photorealistic 3D scenes reconstructed with Gaussian Splatting and densely annotated with hierarchical semantics spanning scenes, rooms, functional areas, object groups, and individual objects that provides a realistic testbed for developing and evaluating models capable of...
Kairos is introduced, a video dataset for video-language modeling with time-resolved annotations that supports fine-grained evaluation, long-range modeling and reasoning, instruction data construction, representation learning, and video generation.
Experiments show that FRAMEWORKERS outperforms strong LLM planners in routing accuracy, recovers reliably from runtime failures, generalizes to unseen sub-agents without retraining, and achieves higher end-to-end video quality and broader task coverage than fixed pipelines, single-agent systems, and prior multi-agent a...
Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors, combined with an adaptive event-count selection strategy and a parameter-free trail filter, reaches sub-pixel accuracy at the lowest latenc...
A cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios, and two official Codabench tracks.
This work argues that successful workflow generation requires modeling knowledge itself, including its structure, hierarchy, and reasoning dynamics, and proposes a knowledge-centric framework that learns to invert, inject, and infer with knowledge across multiple abstraction levels.
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