AutoSci: A Memory-Centric Agentic System for the Full Scientific Research Lifecycle

• URL: http://arxiv.org/abs/2605.31468v1
• Date: Fri, 29 May 2026 16:00:04 GMT
• Title: AutoSci: A Memory-Centric Agentic System for the Full Scientific Research Lifecycle
• Abstract summary: AutoSci is a memory-centric agentic system for the full scientific research lifecycle.<n>It is organized around four modules: SciMem, SciFlow, SciDAG, and SciEvolve.
• Score: 24.50819107720795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scientific research has traditionally been human-intensive, requiring researchers to coordinate literature, ideas, experiments, manuscripts, and review responses across long project cycles. The rise of LLM-based scientific agents creates an opportunity to automate this process. Such a system must support the full research lifecycle, maintain structured persistent memory across projects, and improve its own research procedures over time. However, existing systems either partially satisfy or fail to satisfy these requirements, leaving a gap for a unified automated scientific research system. As a result, we present AutoSci, a memory-centric agentic system for the full scientific research lifecycle. AutoSci is organized around four modules. SciMem provides schema-governed research memory, separating Long-Term Knowledge Memory for reusable scientific knowledge from Active Research Memory for project-level artifacts such as ideas, experiments, manuscripts, and reviews. SciFlow executes a five-stage lifecycle from literature understanding to rebuttal through a harness that controls state, context, verification, feedback, and orchestration. SciDAG augments difficult skills with DAG-shaped multi-agent operators and reusable stage-specific templates. SciEvolve converts feedback signals from users, experiments, reviews, and external environments into versioned updates to SciMem organization, SciFlow skills, and SciDAG templates. Together, these modules make AutoSci a persistent research environment that can execute, remember, and evolve across research projects. The code repository is available at https://github.com/skyllwt/AutoSci.

Related papers

• SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery [48.99744520105086]
  SciForge is a multimodal research-native AI workbench.<n>It reserves the graphical interface for human judgment while search, parsing, model routing, workflow execution, plotting, writing, and presentation generation run as modular agent-accessible services.
  arXiv  Detail & Related papers  (2026-07-17T15:13:03Z)
• Rethinking Scientific Discovery in the Agentic Era [69.85050591628048]
  SCION is an agentic scientific operating system that acts as an textbf organizational nexus.<n>At its core is the textbfResearch Execution Plan (REP), which compiles high-level scientific intent into staged objectives, dependencies, verification checkpoints, tool requirements, expected artifacts, and fallback conditions.<n>We formulate discovery under SCION as textbfTarget-conditioned Inverse Search and extend it to hidden-target settings through batch active search under finite experimental budgets.
  arXiv  Detail & Related papers  (2026-07-04T13:09:33Z)
• SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models [63.357710580101894]
  Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice.<n>We propose a new framework named SciCustom to address the problem.<n>It enables the custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
  arXiv  Detail & Related papers  (2026-05-19T04:41:43Z)
• AI for Auto-Research: Roadmap & User Guide [107.0834449839233]
  We present an end-to-end analysis of AI across the complete research lifecycle.<n>We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy.<n>We show that greater automation can obscure rather than eliminate failure modes.
  arXiv  Detail & Related papers  (2026-05-18T17:08:26Z)
• NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation [59.19858972597063]
  LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automation for whom?<n>We propose NanoResearch, a multi-agent framework that addresses these gaps through tri-level co-evolution.<n>A skill bank distills recurring operations into compact procedural rules reusable across projects.<n>A memory module maintains user- and project-specific experience that grounds planning decisions in each user's research history.<n>A label-free policy learning converts free-form feedback into persistent parameter updates of the planner, reshaping subsequent coordination.
  arXiv  Detail & Related papers  (2026-05-11T16:33:47Z)
• SciDER: Scientific Data-centric End-to-end Researcher [9.796056249900785]
  SciDER is a data-centric end-to-end system that automates the research lifecycle.<n>Unlike traditional frameworks, our specialized agents collaboratively parse and analyze raw scientific data.<n>We also provide easy-to-use PyPI packages with a lightweight web interface to accelerate autonomous, data-driven research.
  arXiv  Detail & Related papers  (2026-03-02T03:53:20Z)
• From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery [67.07598263346591]
  Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery.<n>This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating capabilities of LLMs in science.
  arXiv  Detail & Related papers  (2025-05-19T15:41:32Z)
• The AI Cosmologist I: An Agentic System for Automated Data Analysis [0.0]
  The AI Cosmologist implements a complete pipeline from idea generation to experimental evaluation and research dissemination.<n>Unlike traditional auto machine-learning systems, the AI Cosmologist generates diverse implementation strategies.<n>Results indicate that agentic systems can automate portions of the research process, potentially accelerating scientific discovery.
  arXiv  Detail & Related papers  (2025-04-04T13:12:08Z)

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.