AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Multimodal inputs are inherently heterogeneous, not only across modalities but also in the information pathways required for effective prediction. To address this limitation, we propose InstMoE, an adaptive expert routing framework for multimodal learning. InstMoE dynamically routes each input to specialized unimodal a...
Experimental results show that incorporating user traits can aid personalized prediction, as this work aims to shift the research paradigm from developing agents suited for the average user to designing systems tailored to specific individuals.
The results show that affective perception depends not only on the observed behavior, but also on how evidence is acquired, and by making observation acquisition experimentally controllable, AffectSim provides a foundation for studying embodied affective perception in interactive 3D environments.
This work advances SPI research from static images to dynamic videos and ensures more comprehensive scenario coverage during benchmarking with RobotEQ-Video, a hierarchical world-state taxonomy organized into a four-level coarse-to-fine structure.
This work introduces VideoGAIA, an agentic video understanding benchmark for general artificial intelligence (AI) assistants, and formulates video understanding as a multi-turn, tool-augmented interaction process, where models must iteratively perceive videos, invoke external tools, gather complementary information, an...
This work aims to advance the development of socially desirable embodied agents in open-domain environments by extending the concept of proactive assistance to Social Proactive Intelligence (SPI), characterized by diverse scenarios, social understanding, and robot-centric behaviors.
This paper introduces OneEmo, a unified affective generalist capable of mastering emotion perception, comprehension, and interaction, and proposes Emo-Chord, a novel reinforcement learning strategy that stabilizes optimization through unified multi-task reward allocation.
AC-VLA is introduced, a plug-and-play Action Compositional learning framework comprising two architecture-agnostic components that achieves a ~28% absolute improvement on compositional OOD tasks while maintaining near-perfect in-distribution performance.
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