Innovation Discovery System for Networking Research

TL;DR

SciNet is a specialized system leveraging LLMs to generate practical and novel networking research ideas by mimicking human discovery workflows and utilizing a structured dataset.

Contribution

The paper introduces SciNet, a novel idea generation system for networking research that combines structured datasets, workflow simulation, and evaluation methods.

Findings

SciNet outperforms standalone LLMs in idea quality.

It produces practical and novel networking research ideas.

Experimental results validate its effectiveness across multiple LLMs.

Abstract

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.