惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 三生石上(FineUI控件)
S
SegmentFault 最新的问题
阮一峰的网络日志
阮一峰的网络日志
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
雷峰网
雷峰网
Apple Machine Learning Research
Apple Machine Learning Research
WordPress大学
WordPress大学
博客园 - 司徒正美
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
宝玉的分享
宝玉的分享
爱范儿
爱范儿
月光博客
月光博客
The GitHub Blog
The GitHub Blog
M
MIT News - Artificial intelligence
H
Hackread – Cybersecurity News, Data Breaches, AI and More
B
Blog
T
Tailwind CSS Blog
美团技术团队
D
Docker
V
Visual Studio Blog
Martin Fowler
Martin Fowler
博客园 - 聂微东
The Cloudflare Blog

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
Triadic Novelty: A Structural Typology of Science Innovation
[Submitted on 21 Jun 2025 (v1), last revised 25 Aug 2026 (this v · 2025-06-22 · via cs.SI updates on arXiv.org

View PDF

Abstract:Scientific progress depends on novelty, but current evaluation systems often conflate novelty with recognition, favoring work that aligns with existing paradigms over ideas that challenge them. We introduce a theory-driven framework that conceptualizes novelty as a structural process rather than a single scalar outcome. Drawing on network science and theories of scientific discovery, we develop a triadic typology of novelty: Pioneers introduce new topics, Mavericks recombine distant areas of knowledge, and Vanguards reinforce weak but emerging connections. We apply this framework to philanthropic and nonprofit studies, an interdisciplinary and evolving field well suited to examining how novelty is recognized before evaluation norms are fully stabilized. Results show that novelty is not uniformly rewarded: Pioneer contributions are often weakly recognized unless later taken up, Maverick contributions receive consistent recognition, and Vanguard contributions exhibit diminishing returns as ideas become established. By distinguishing how novelty enters and evolves within knowledge structures, the framework offers a citation-independent approach with implications for research evaluation, funding design, and science and innovation policy.

Submission history

From: Jin Ai [view email]
[v1] Sat, 21 Jun 2025 23:09:04 UTC (661 KB)
[v2] Wed, 25 Jun 2025 18:24:05 UTC (677 KB)
[v3] Tue, 25 Aug 2026 21:16:37 UTC (885 KB)