Innovation Discovery System for Networking Research

2026-03-27

AI Summary

Current large language models (LLMs) struggle to generate genuinely innovative networking research ideas, often limited to recombining existing solutions due to a lack of structured knowledge support. To address this limitation, this work proposes SciNet, a novel system that first constructs the first structured dataset of research ideas specifically for the networking domain. It then introduces a three-stage framework—problem formulation, inspiration retrieval, and idea generation—that emulates human scientific reasoning and integrates retrieval-augmented mechanisms to guide LLMs effectively. Furthermore, SciNet incorporates a multi-dimensional evaluation metric that jointly considers novelty and practicality. Experimental results demonstrate that SciNet consistently produces high-quality, innovative, and feasible networking research ideas across multiple LLMs, significantly outperforming baseline approaches.

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.

Problem

• Innovation Discovery
• Networking Research
• Large Language Models
• Scientific Hypothesis Generation
• Idea-level Knowledge

Innovation

• scientific discovery
• idea generation
• large language models
• networking research
• structured knowledge