Towards a new paradigm of scientific discovery with socialized artificial intelligence
Not on the map. Why?
Abstract (arXiv)
Scientific discovery has advanced through successive transformations in the organization of knowledge. Observation and experimentation established the empirical foundations of science. Theory made it possible to derive general principles from particular phenomena. Computation extended inquiry into systems beyond direct observation, while data-intensive methods opened new spaces of pattern and prediction. Science now confronts a different frontier. The central challenge is no longer simply to produce more information, but to organize expanding knowledge, reasoning, and evidence into a coherent process of discovery. Here, we introduce Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence. BLAZE conceives AI not as an assistant for isolated research tasks, but as an organizational infrastructure for scientific discovery. It connects persistent knowledge, collective reasoning, empirical validation, and human judgment within a continuous research lifecycle, transforming fragmented activities into a cumulative process of inquiry, criticism, and revision. The central premise of BLAZE is that scientific intelligence does not arise from computation alone. It emerges from the sustained interaction among knowledge, hypotheses, experiments, and collective verification. By organizing humans and machines within a shared scientific process, BLAZE makes discovery more traceable, reproducible, and cumulative while preserving human creativity, judgment, and responsibility. Socialized scientific intelligence may provide a foundation for the next era of science. Its purpose is not to replace human discovery, but to extend the scale, depth, and continuity of collective scientific inquiry.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 43 of its 90 references that are in the map. First posted on arXiv 3 Aug 2026.
Builds on
43 of 90 references.
References · 43
- 2017On Calibration of Modern Neural Networkscited by 4,860
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
- 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
- 2024A Survey on LLM-as-a-Judgecited by 1,384
- 2017Selective Classification for Deep Neural Networkscited by 799
- 2025AlphaEvolve: A coding agent for scientific and algorithmic discoverycited by 763
- 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 609
- 2024MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineeringcited by 429
- 2020SPECTER: Document-level Representation Learning using Citation-informed Transformerscited by 426
- 2025Agent Laboratory: Using LLM Agents as Research Assistantscited by 405
- 2025Accelerating scientific discovery with Co-Scientistcited by 353
31 more
- 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentationcited by 339
- 2020Consistent Estimators for Learning to Defer to an Expertcited by 262
- 2025AIDE: AI-Driven Exploration in the Space of Codecited by 206
- 2024ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Modelscited by 200
- 2023Can large language models provide useful feedback on research papers? A large-scale empirical analysiscited by 195
- 2026Towards End-to-End Automation of AI Researchcited by 150
- 2024AutoSurvey: Large Language Models Can Automatically Write Surveyscited by 116
- 2014Growth rates of modern science: A bibliometric analysis based on the number of publications and cited referencescited by 63
- 2025MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Researchcited by 57
- 2024Risks of AI Scientists: Prioritizing Safeguarding Over Autonomycited by 49
- 2024CiteME: Can Language Models Accurately Cite Scientific Claims?cited by 45
- 2026EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discoverycited by 41
- 2023GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Studycited by 40
- 2025A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontierscited by 34
- 2024Automating the Search for Artificial Life with Foundation Modelscited by 20
- 2025A Survey on Hypothesis Generation for Scientific Discovery in the Era of Large Language Modelscited by 17
- 2026AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discoverycited by 17
- 2024HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Predictioncited by 15
- 2025MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Searchcited by 13
- 2025AgenticSciML: Collaborative Multi-Agent Systems for Emergent Discovery in Scientific Machine Learningcited by 13
- 2026ARIS: Autonomous Research via Adversarial Multi-Agent Collaborationcited by 12
- 2025CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?cited by 11
- 2025AlphaResearch: Accelerating New Algorithm Discovery with Language Modelscited by 9
- 2025An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Designcited by 8
- 2025Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automationcited by 8
- 2025A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Taskscited by 7
- 2025Towards autonomous quantum physics research using LLM agents with access to intelligent toolscited by 7
- 2013Micropublications: a Semantic Model for Claims, Evidence, Arguments and Annotations in Biomedical Communicationscited by 6
- 2025Foundation Models for Scientific Discovery: From Paradigm Enhancement to Paradigm Transitioncited by 6
- 2026FARS: A Fully Automated Research System Deployed at Scalecited by 5
- 2025Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Researchcited by 2
Built on by
Not cited yet.