Strategic Innovation Management in the Age of Large Language Models Market Intelligence, Adaptive R&D, and Ethical Governance

• URL: http://arxiv.org/abs/2511.14709v2
• Date: Mon, 24 Nov 2025 16:42:33 GMT
• Title: Strategic Innovation Management in the Age of Large Language Models Market Intelligence, Adaptive R&D, and Ethical Governance
• Authors: Raha Aghaei, Ali A. Kiaei, Mahnaz Boush, Mahan Rofoosheh, Mohammad Zavvar,
• Abstract summary: This study analyzes the multiple functions of Large Language Models (LLMs) in transforming research and development (R&D) processes.<n>By automating knowledge discovery, boosting hypothesis creation, integrating transdisciplinary insights, and enabling cooperation within innovation ecosystems, LLMs dramatically improve the efficiency and effectiveness of research processes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study analyzes the multiple functions of Large Language Models (LLMs) in transforming research and development (R&D) processes. By automating knowledge discovery, boosting hypothesis creation, integrating transdisciplinary insights, and enabling cooperation within innovation ecosystems, LLMs dramatically improve the efficiency and effectiveness of research processes. Through extensive analysis of scientific literature, patent databases, and experimental data, these models enable more flexible and informed R&D workflows, ultimately accelerating innovation cycles and lowering time-to-market for breakthrough ideas.

Related papers

• Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team [53.38438460574943]
  IDVSCI is a multi-agent framework built on large language models (LLMs)<n>It incorporates two key innovations: a Dynamic Knowledge Exchange mechanism and a Dual-Diversity Review paradigm.<n>Results show that IDVSCI consistently achieves the best performance across two datasets.
  arXiv  Detail & Related papers  (2025-06-23T07:12:08Z)
• AI-Researcher: Autonomous Scientific Innovation [13.58669328864436]
  We introduce AI-Researcher, a fully autonomous research system that transforms how AI-driven scientific discovery is conducted and evaluated.<n>Our framework seamlessly orchestrates the complete research pipeline--from literature review and hypothesis generation to algorithm implementation and publication-ready manuscript preparation.
  arXiv  Detail & Related papers  (2025-05-24T13:54:38Z)
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research [58.944125758758936]
  The Science of Science (SoS) explores the mechanisms underlying scientific discovery.<n>The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS.<n>We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them.
  arXiv  Detail & Related papers  (2025-05-17T15:01:33Z)
• 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models [58.98176123850354]
  The recent release of DeepSeek-R1 has generated widespread social impact and sparked enthusiasm in the research community for exploring the explicit reasoning paradigm of language models.<n>The implementation details of the released models have not been fully open-sourced by DeepSeek, including DeepSeek-R1-Zero, DeepSeek-R1, and the distilled small models.<n>Many replication studies have emerged aiming to reproduce the strong performance achieved by DeepSeek-R1, reaching comparable performance through similar training procedures and fully open-source data resources.
  arXiv  Detail & Related papers  (2025-05-01T14:28:35Z)
• A Vision for Auto Research with LLM Agents [46.95148319863236]
  This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research.<n>The system spans all major research phases, including literature review, ideation, methodology, experimentation, paper writing, peer review response, and dissemination.
  arXiv  Detail & Related papers  (2025-04-26T02:06:10Z)
• IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery [27.218896203253987]
  IRIS is an open-source platform designed for researchers to leverage large language models (LLMs)-assisted scientific ideation.<n>IRIS incorporates innovative features to enhance ideation, including adaptive test-time compute expansion via Monte Carlo Tree Search (MCTS), fine-grained feedback mechanism, and query-based literature synthesis.<n>We conduct a user study with researchers across diverse disciplines, validating the effectiveness of our system in enhancing ideation.
  arXiv  Detail & Related papers  (2025-04-23T14:01:36Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process [15.471884798655063]
  The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR) This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages. The synergy between AI and OR is poised to drive significant advancements and novel solutions in a multitude of domains.
  arXiv  Detail & Related papers  (2024-01-06T15:55:14Z)
• SciOps: Achieving Productivity and Reliability in Data-Intensive Research [0.8414742293641504]
  Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals. Various scientific disciplines, including neuroscience, have adopted key technologies to enhance collaboration, inspiration and automation. We introduce a five-level Capability Maturity Model describing the principles of rigorous scientific operations.
  arXiv  Detail & Related papers  (2023-12-29T21:37:22Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)

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.