LWS is a text-first paradigm for full-duplex speech LLMs that treats visible writing as a primary output channel alongside audio input and spoken response, implemented via token schema and synthetic per-second annotations.
Larkin and Herbert A
5 Pith papers cite this work. Polarity classification is still indexing.
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Foundation models are large adaptable AI systems with emergent capabilities that offer broad opportunities but carry risks from homogenization, opacity, and inherited defects across downstream applications.
Introduces a modality-switching mechanism for LLMs on spatial reasoning tasks using a trustworthiness and complexity based metric, showing up to 42% performance improvement.
The paper proposes that reusable agent skills should incorporate visual elements alongside text, introduces three forms of visual skills and an automatic conversion system, and reports better performance on GUI and visual-centric tasks.
Modified feedback alignment in convolutional networks produces representations geometrically aligned with backpropagation on CIFAR-10.
citing papers explorer
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Liberating LLM Capabilities in Full-Duplex Speech Models
LWS is a text-first paradigm for full-duplex speech LLMs that treats visible writing as a primary output channel alongside audio input and spoken response, implemented via token schema and synthetic per-second annotations.
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Spatial Reasoning via Modality Switching Between Language and Symbolic Representation
Introduces a modality-switching mechanism for LLMs on spatial reasoning tasks using a trustworthiness and complexity based metric, showing up to 42% performance improvement.
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Agent Skills Should Go Beyond Text: The Case for Visual Skills
The paper proposes that reusable agent skills should incorporate visual elements alongside text, introduces three forms of visual skills and an automatic conversion system, and reports better performance on GUI and visual-centric tasks.
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Biological Plausibility and Representational Alignment of Feedback Alignment in Convolutional Networks
Modified feedback alignment in convolutional networks produces representations geometrically aligned with backpropagation on CIFAR-10.