#edge-deployment2 articles

2 articles

AIBullisharXiv – CS AI · 7h ago7/10

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SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization

SpecQuant introduces a novel quantization framework using spectral decomposition to compress large language models to 4-bit precision for both weights and activations, achieving only 1.5% accuracy loss on LLaMA-3 8B while enabling 2x faster inference and 3x memory reduction. The technique exploits frequency domain properties to preserve essential signal components while suppressing high-frequency noise, addressing a critical challenge in deploying LLMs on edge devices.

AIBullisharXiv – CS AI · 7h ago6/10

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EmoMAS: Emotion-Aware Multi-Agent System for High-Stakes Edge-Deployable Negotiation with Bayesian Orchestration

Researchers introduce EmoMAS, a Bayesian multi-agent framework that enables small language models to perform sophisticated negotiation by treating emotional intelligence as a strategic variable. The system coordinates game-theoretic, reinforcement learning, and psychological agents to optimize negotiation outcomes while maintaining privacy through edge deployment, demonstrating performance comparable to larger models across high-stakes domains.