Mochun Long | Searcharxiv

arXiv2026

Innovation Discovery System for Networking Research

As networking systems become increasingly complex, achieving disruptive innovation grows more challenging. At the same time, recent progress in Large Language Models (LLMs) has shown strong potential for scientific hypothesis formation and idea generation. Nevertheless, applying LLMs effectively to networking research remains difficult for two main reasons: standalone LLMs tend to generate ideas by recombining existing solutions, and current open-source networking resources do not provide the structured, idea-level knowledge necessary for data-driven scientific discovery. To bridge this gap, we present SciNet, a research idea generation system specifically designed for networking. SciNet is built upon three key components: (1) constructing a networking-oriented scientific discovery dataset from top-tier networking conferences, (2) simulating the human idea discovery workflow through problem setting, inspiration retrieval, and idea generation, and (3) developing an idea evaluation method that jointly measures novelty and practicality. Experimental results show that \system consistently produces practical and novel networking research ideas across multiple LLM backbones, and outperforms standalone LLM-based generation in overall idea quality.

cs.NI↗

arXiv2025

A Systematic Evaluation of On-Device LLMs: Quantization, Performance, and Resources

Deploying Large Language Models (LLMs) on edge devices enhances privacy but faces performance hurdles due to limited resources. We introduce a systematic methodology to evaluate on-device LLMs, balancing capability, efficiency, and resource constraints. Through an extensive analysis of models (0.5B-14B) and seven post-training quantization (PTQ) methods on commodity hardware, we demonstrate that: 1) Heavily quantized large models consistently outperform smaller, high-precision models, with a performance threshold at ~3.5 effective bits-per-weight (BPW); 2) Resource utilization scales linearly with BPW, though power and memory footprints vary by quantization algorithm; and 3) With a reduction in model size, the primary constraint on throughput transitions from communication overhead to computational latency. We conclude by offering guidelines for optimizing LLMs in resource-constrained edge environments. Our codebase is available at https://anonymous.4open.science/r/LLMOnDevice/.

cs.LG↗