CORTIS is a text-only adaptation method for spoken language models that enables direct speech-to-structured-output generation for task-oriented agents and matches or exceeds ASR-LLM cascades under acoustic degradation.
Octopus v2: On-device language model for super agent
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Personal agents require edge deployment to preserve high-fidelity local context and zero-latency loops, as claimed through three structural shifts away from cloud-centric designs.
ShadowNPU presents shadowAttn, a co-designed sparse attention system that uses NPU pilot compute and techniques like graph bucketing and per-head sparsity to minimize CPU/GPU fallback during on-device LLM inference while maintaining accuracy.
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.
citing papers explorer
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CORTIS: Text-Only Adaptation of Spoken Language Models for Task-Oriented Voice Agents
CORTIS is a text-only adaptation method for spoken language models that enables direct speech-to-structured-output generation for task-oriented agents and matches or exceeds ASR-LLM cascades under acoustic degradation.
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Beyond Scaling: Agents Are Heading to the Edge
Personal agents require edge deployment to preserve high-fidelity local context and zero-latency loops, as claimed through three structural shifts away from cloud-centric designs.
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ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference
ShadowNPU presents shadowAttn, a co-designed sparse attention system that uses NPU pilot compute and techniques like graph bucketing and per-head sparsity to minimize CPU/GPU fallback during on-device LLM inference while maintaining accuracy.
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Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.