Omni-DuplexEval provides a new benchmark and automatic evaluation method for real-time duplex omni-modal interaction, showing state-of-the-art models reach only 39.6% overall and 20% on proactive reminders.
A survey on video large language models: Benchmarks and evaluation methodologies
2 Pith papers cite this work. Polarity classification is still indexing.
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PINNs solve PDEs through dense, non-unique weight matrices that look nothing like finite-difference stencils, and independent runs reach similar solutions via different internal dynamics.
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Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction
Omni-DuplexEval provides a new benchmark and automatic evaluation method for real-time duplex omni-modal interaction, showing state-of-the-art models reach only 39.6% overall and 20% on proactive reminders.
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Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
PINNs solve PDEs through dense, non-unique weight matrices that look nothing like finite-difference stencils, and independent runs reach similar solutions via different internal dynamics.