Towards a new paradigm of scientific discovery with socialized artificial intelligence

• URL: http://arxiv.org/abs/2608.02775v1
• Date: Mon, 03 Aug 2026 18:18:39 GMT
• Title: Towards a new paradigm of scientific discovery with socialized artificial intelligence
• Abstract summary: We introduce Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence.<n>BLAZE conceives AI not as an assistant for isolated research tasks, but as an organizational infrastructure for scientific discovery.<n>Its purpose is not to replace human discovery, but to extend the scale, depth, and continuity of collective scientific inquiry.
• Score: 48.48034861871986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Quo Vadis? Scientific Discovery in the Age of Artificial Intelligence [0.0]
  It first surveys the rapid rise of AI capabilities, before turning to scientometric evidence of AI's diffusion across the sciences.<n>It then proposes a typology of AI systems used in research, ranging from specialized scientific AI through scientific AI assistants and agents to hybrid experimental systems that combine computation and physical experimentation.
  arXiv  Detail & Related papers  (2026-08-18T16:21:44Z)
• Embodied Science: Closing the Discovery Loop with Agentic Embodied AI [73.54118756665221]
  We argue for embodied science, a paradigm that reframes scientific discovery as a closed loop tightly coupling agentic reasoning with physical execution.<n>We propose a unified Perception-Language-Action-Discovery (PLAD) framework, wherein embodied agents perceive experimental environments, reason over scientific knowledge, execute physical interventions, and internalize outcomes to drive subsequent exploration.
  arXiv  Detail & Related papers  (2026-03-20T09:20:12Z)
• OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists [47.41269933143946]
  We introduce OmniScientist, a framework that encodes the underlying mechanisms of human research into the AI scientific workflow.<n> OmniScientist achieves end-to-end automation across data foundation, literature review, research ideation, experiment automation, scientific writing, and peer review.<n>This infrastructure empowers agents to not only comprehend and leverage human knowledge systems but also to collaborate and co-evolve.
  arXiv  Detail & Related papers  (2025-11-21T03:55:19Z)
• Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics [82.55776608452017]
  Large language models (LLMs) provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics.<n>This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle.<n>We identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents.
  arXiv  Detail & Related papers  (2025-10-10T22:26:26Z)
• Virtuous Machines: Towards Artificial General Science [0.0]
  We show that a domain-agnostic, agentic AI system can independently navigate the scientific workflow.<n>The system autonomously designed and executed three psychological studies on visual working memory, mental rotation, and imagery vividness.
  arXiv  Detail & Related papers  (2025-08-19T00:35:56Z)
• Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI [98.19195693735487]
  We propose the paradigm of Intelligent Science Laboratories (ISLs)<n>ISLs are a multi-layered, closed-loop framework that deeply integrates cognitive and embodied intelligence.<n>We argue that such systems are essential for overcoming the current limitations of scientific discovery.
  arXiv  Detail & Related papers  (2025-06-24T13:31:44Z)
• Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
  An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.<n>AGS aims to significantly reduce the time and resources needed for scientific discovery.<n>As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
  arXiv  Detail & Related papers  (2025-03-28T14:00:27Z)
• Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence [0.9065034043031668]
  We argue that human complex reasoning for scientific discovery remains of vital importance, at least before the advent of artificial general intelligence.<n>Knowing the principles' the AI systems used to make decisions can be a point of contact with domain experts and scientists.<n>We define this field as Explainable AI for Science, where domain experts formulate scientific hypotheses and explanations based on the interpretability of a predictive AI system.
  arXiv  Detail & Related papers  (2024-06-15T08:34:42Z)
• To think inside the box, or to think out of the box? Scientific discovery via the reciprocation of insights and concepts [26.218943558900552]
  We view scientific discovery as an interplay between $thinking out of the box$ that actively seeks insightful solutions. We propose Mindle, a semantic searching game that triggers scientific-discovery-like thinking spontaneously. On this basis, the meta-strategies for insights and the usage of concepts can be investigated reciprocally.
  arXiv  Detail & Related papers  (2022-12-01T03:52:12Z)
• A Computational Inflection for Scientific Discovery [48.176406062568674]
  We stand at the foot of a significant inflection in the trajectory of scientific discovery. As society continues on its fast-paced digital transformation, so does humankind's collective scientific knowledge. Computer science is poised to ignite a revolution in the scientific process itself.
  arXiv  Detail & Related papers  (2022-05-04T11:36:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.