Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation

• URL: http://arxiv.org/abs/2510.15624v1
• Date: Fri, 17 Oct 2025 13:13:32 GMT
• Title: Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation
• Abstract summary: We present texttfreephdlabor, an open-source multiagent framework featuring textitfully dynamic determined by real-time agent reasoning.<n>The framework provides comprehensive infrastructure including textitautomatic context compaction, textitworkspace-based communication to prevent information degradation, textitmemory persistence across sessions, and textitnon-blocking human intervention mechanisms.
• Score: 41.659285482346235
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The automation of scientific discovery represents a critical milestone in Artificial Intelligence (AI) research. However, existing agentic systems for science suffer from two fundamental limitations: rigid, pre-programmed workflows that cannot adapt to intermediate findings, and inadequate context management that hinders long-horizon research. We present \texttt{freephdlabor}, an open-source multiagent framework featuring \textit{fully dynamic workflows} determined by real-time agent reasoning and a \coloremph{\textit{modular architecture}} enabling seamless customization -- users can modify, add, or remove agents to address domain-specific requirements. The framework provides comprehensive infrastructure including \textit{automatic context compaction}, \textit{workspace-based communication} to prevent information degradation, \textit{memory persistence} across sessions, and \textit{non-blocking human intervention} mechanisms. These features collectively transform automated research from isolated, single-run attempts into \textit{continual research programs} that build systematically on prior explorations and incorporate human feedback. By providing both the architectural principles and practical implementation for building customizable co-scientist systems, this work aims to facilitate broader adoption of automated research across scientific domains, enabling practitioners to deploy interactive multiagent systems that autonomously conduct end-to-end research -- from ideation through experimentation to publication-ready manuscripts.

Related papers

• DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document Operations [106.74348504930124]
  We introduce DocOps, a deterministically verifiable evaluation framework underpinned by a hierarchical taxonomy.<n>Based on DocOps, we systematically evaluate representative closed- and open-source models across various agentic harnesses.<n>Our work exposes the capability boundaries of agents in maintaining global document consistency, shedding light on the future design of robust, non-destructive agents.
  arXiv  Detail & Related papers  (2026-07-22T07:52:59Z)
• AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows [5.357463600626556]
  AgentCo-op is a retrieval framework that composes reusable skills, tools, and external agents into executable artifacts.<n>It applies self-guided local repair to implicated components when execution indicates failure.<n>It coordinates specialized agents for spatialomics and gene-set interpretation, and builds a parallel workflow for cross-modality marker analysis on single-cell multiome data.
  arXiv  Detail & Related papers  (2026-05-19T19:22:21Z)
• Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems [65.90791804095561]
  Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination amplifies a less explored risk.<n>Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined.<n>This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement.
  arXiv  Detail & Related papers  (2026-05-14T14:36:13Z)
• Assistance to Autonomy: A Systematic Literature Review of Agentic AI across the Software Development Life Cycle [4.241377338556251]
  This study contributes a comprehensive characterization of the current literature on agentic systems in software product development.<n>It also provides a methodological contribution in the form of an AI-assisted tool to automate the screening phase in high-volume SLR domains.
  arXiv  Detail & Related papers  (2026-05-14T10:46:51Z)
• An Empirical Study of Multi-Agent Collaboration for Automated Research [41.906658558789545]
  We present a study investigating the comparative efficacy of distinct multi-agent structures for automated machine learning optimization.<n>We benchmark a single-agent baseline against two multi-agent paradigms: a subagent architecture and an agent team architecture.<n>Our findings reveal a fundamental trade-off between operational stability and theoretical deliberation.
  arXiv  Detail & Related papers  (2026-03-31T11:57:00Z)
• AI-Supervisor: Autonomous AI Research Supervision via a Persistent Research World Model [1.14219428942199]
  Existing automated research systems operate as stateless, linear pipelines.<n>We present textbfAI-Supervisor, a multi-agent orchestration framework.<n>Agents provide end-to-end AI research supervision driven by human interests.
  arXiv  Detail & Related papers  (2026-03-25T15:16:51Z)
• From Agent-Only Social Networks to Autonomous Scientific Research: Lessons from OpenClaw and Moltbook, and the Architecture of ClawdLab and Beach.Science [0.0]
  OpenClaw and Moltbook produced a large-scale dataset of autonomous AI-to-AI interaction in January 2026.<n>This study conducts a multivocal literature review of that ecosystem and presents two complementary platforms for autonomous scientific research.
  arXiv  Detail & Related papers  (2026-02-23T13:10:01Z)
• El Agente Gráfico: Structured Execution Graphs for Scientific Agents [7.47895130442454]
  We present El Agente Grfico, a single-agent framework that embeds large language models (LLMs)-driven decision-making within a type-safe execution environment.<n>Central to our approach is a structured abstraction of scientific concepts and an object-graph mapper that represents computational state as typed Python objects.<n>We evaluate the system by developing an automated benchmarking framework across a suite of university-level quantum chemistry tasks.
  arXiv  Detail & Related papers  (2026-02-19T23:47:05Z)
• An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
  Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
  arXiv  Detail & Related papers  (2025-12-22T15:03:57Z)
• Seismology modeling agent: A smart assistant for geophysical researchers [14.28965530601497]
  This paper proposes an intelligent, interactive workflow powered by Large Language Models (LLMs)<n>We introduce the first Model Context Protocol (MCP) server suite for SPECFEM.<n>The framework supports both fully automated execution and human-in-the-loop collaboration.
  arXiv  Detail & Related papers  (2025-12-16T14:18:26Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism [61.01709143437043]
  We introduce a novel agent design framework centered on a Hierarchical Task Abstraction Mechanism (HTAM)<n>Specifically, HTAM moves beyond emulating social roles, instead structuring multi-agent systems into a logical hierarchy that mirrors the intrinsic task-dependency graph of a given domain.<n>We instantiate this framework as EarthAgent, a multi-agent system tailored for complex geospatial analysis.
  arXiv  Detail & Related papers  (2025-11-21T12:25:47Z)
• Unifying Tree Search Algorithm and Reward Design for LLM Reasoning: A Survey [92.71325249013535]
  Deliberative tree search is a cornerstone of Large Language Model (LLM) research.<n>This paper introduces a unified framework that deconstructs search algorithms into three core components.
  arXiv  Detail & Related papers  (2025-10-11T03:29:18Z)
• TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents [28.125147449800696]
  TinyScientist identifies the essential components of the automatic research workflow and proposes an interactive, controllable framework that easily adapts to new tools and supports iterative growth.<n>We provide an open-source, interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer.
  arXiv  Detail & Related papers  (2025-10-08T02:18:57Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• State and Memory is All You Need for Robust and Reliable AI Agents [29.259008600842517]
  Large language models (LLMs) have enabled powerful advances in natural language understanding and generation.<n>Yet their application to complex, real-world scientific remain limited by challenges in memory, planning, and tool integration.<n>Here, we introduce SciBORG, a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution.
  arXiv  Detail & Related papers  (2025-06-30T02:02:35Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• 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)
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
  Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence.<n>This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy.<n>Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
  arXiv  Detail & Related papers  (2025-03-27T12:50:17Z)

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.