ResearStudio: A Human-Intervenable Framework for Building Controllable Deep-Research Agents

• URL: http://arxiv.org/abs/2510.12194v1
• Date: Tue, 14 Oct 2025 06:40:11 GMT
• Title: ResearStudio: A Human-Intervenable Framework for Building Controllable Deep-Research Agents
• Abstract summary: We present ResearStudio, the first open-source framework that places real-time human control at its core.<n>A hierarchical Planner-Executor writes every step to a live ''plan-as-document''<n>At any moment, the user can pause the run, edit the plan or code, run custom commands, and resume.
• Score: 29.584029036072863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current deep-research agents run in a ''fire-and-forget'' mode: once started, they give users no way to fix errors or add expert knowledge during execution. We present ResearStudio, the first open-source framework that places real-time human control at its core. The system follows a Collaborative Workshop design. A hierarchical Planner-Executor writes every step to a live ''plan-as-document,'' a fast communication layer streams each action, file change, and tool call to a web interface. At any moment, the user can pause the run, edit the plan or code, run custom commands, and resume -- switching smoothly between AI-led, human-assisted and human-led, AI-assisted modes. In fully autonomous mode, ResearStudio achieves state-of-the-art results on the GAIA benchmark, surpassing systems like OpenAI's DeepResearch and Manus. These results show that strong automated performance and fine-grained human control can coexist. The full code, protocol, and evaluation scripts are available at https://github.com/ResearAI/ResearStudio. We will continue to update the repository to encourage further work on safe and controllable research agents. Our live demo is publicly accessible at http://ai-researcher.net:3000/. We support the development of DeepScientist, which can be accessed at https://github.com/ResearAI/DeepScientist.

Related papers

• OpenAI4S: Code as Action, Science as Sessions [57.428071188574]
  We present OpenAI4S, an open-source scientific research agent built around the principle of emphCode as Action, Science as Sessions.<n>OpenAI4S combines a persistent computing runtime with research-session management orchestration is handled through structured tool calls.<n>We evaluate OpenAI4S on 36 research scenarios spanning retrosynthesis, molecular protein binder design, protein mutation, catalyst screening, and mineral spectroscopy.
  arXiv  Detail & Related papers  (2026-09-14T06:16:59Z)
• Dr. Claw: An AI Scientist Workspace for Vibe Research [64.18459471289147]
  Dr. Claw is an open-source workspace that wraps existing coding-agent executors in a controllable human-in-the-loop workflow.<n> Persistent state objects, a reusable skill library, and multi-executor coordination link human decisions to AI execution.<n>Dr. Claw scores higher on research completeness while persisting an auditable, recoverable process trail.
  arXiv  Detail & Related papers  (2026-08-31T20:58:47Z)
• ETA: A New Agentic Paradigm for Embodied Tasks [76.25893120982523]
  We introduce the Embodied Task Agent (ETA), a new paradigm for extending digital agents into the physical world.<n>ETA centers the robot around a Planner that chooses one Tool call at a time, an Interface that controls execution, and a World that returns the result and a fresh observation.<n>ETA provides replaceable Planners, composable Tools and Skills, auditable memory, replayable trajectories, and common interfaces for simulation and real robots.
  arXiv  Detail & Related papers  (2026-08-04T16:56:09Z)
• 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)
• Workstream: A Local-First Developer Command Center for the AI-Augmented Engineering Workflow [0.0]
  We present Workstream, an open-source, local-first developer command center that aggregates pull requests, task management, calendar, AI-powered code review, historical review intelligence, repository AI-readiness scoring, and agent observability into a single interface.<n>We describe the system architecture, a novel 5-category AI readiness scoring algorithm, a review intelligence pipeline that mines historical PR reviews for team-specific patterns, and an agent observability layer implementing the Model Context Protocol (MCP), Agent-to-Agent (A2A), and Agent Observability Protocol (AOP)
  arXiv  Detail & Related papers  (2026-04-18T16:24:29Z)
• Towards a Humanized Social-Media Ecosystem: AI-Augmented HCI Design Patterns for Safety, Agency & Well-Being [0.0]
  Social platforms connect billions of people, yet their engagement-first algorithms often work on users rather than with them.<n>We propose Human-Layer AI (HL-AI)--user-owned, explainable intermediaries that sit in the browser between platform logic and the interface.<n>HL-AI gives people practical, moment-to-moment control without requiring platform cooperation.
  arXiv  Detail & Related papers  (2025-11-08T06:22:15Z)
• Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training [67.895981259683]
  General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence.<n>Current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools.<n>We present Cognitive Kernel-Pro, a fully open-source and (to the maximum extent) free multi-module agent framework.
  arXiv  Detail & Related papers  (2025-08-01T08:11:31Z)
• Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery [5.326072982491534]
  cmbagent is a system for automation of scientific research tasks.<n>The system is formed by about 30 Large Language Model (LLM) agents.<n>The system is deployed on HuggingFace and will be available on the cloud.
  arXiv  Detail & Related papers  (2025-07-09T20:03:30Z)
• SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam? [51.112225746095746]
  We introduce X-Master, a tool-augmented reasoning agent designed to emulate human researchers.<n>X-Masters sets a new state-of-the-art record on Humanity's Last Exam with a score of 32.1%.
  arXiv  Detail & Related papers  (2025-07-07T17:50:52Z)
• From Reproduction to Replication: Evaluating Research Agents with Progressive Code Masking [48.90371827091671]
  AutoExperiment is a benchmark that evaluates AI agents' ability to implement and run machine learning experiments.<n>We evaluate state-of-the-art agents and find that performance degrades rapidly as $n$ increases.<n>Our findings highlight critical challenges in long-horizon code generation, context retrieval, and autonomous experiment execution.
  arXiv  Detail & Related papers  (2025-06-24T15:39:20Z)
• CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation [70.3224918173672]
  CowPilot is a framework supporting autonomous as well as human-agent collaborative web navigation.<n>It reduces the number of steps humans need to perform by allowing agents to propose next steps, while users are able to pause, reject, or take alternative actions.<n>CowPilot can serve as a useful tool for data collection and agent evaluation across websites.
  arXiv  Detail & Related papers  (2025-01-28T00:56:53Z)
• OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
  We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer.<n>We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T17:50:43Z)
• Open-Source Skull Reconstruction with MONAI [3.245541722525715]
  We present a deep learning-based approach for skull reconstruction for MONAI, which has been pre-trained on the MUG500+ skull dataset. The primary goal of this paper lies in the investigation of open-sourcing codes and pre-trained deep learning models under the MONAI framework.
  arXiv  Detail & Related papers  (2022-11-25T12:12:23Z)
• Human-AI Shared Control via Frequency-based Policy Dissection [34.0399894373716]
  Human-AI shared control allows human to interact and collaborate with AI to accomplish control tasks in complex environments. Previous Reinforcement Learning (RL) methods attempt the goal-conditioned design to achieve human-controllable policies. We develop a simple yet effective frequency-based approach called textitPolicy Dissection to align the intermediate representation of the learned neural controller with the kinematic attributes of the agent behavior.
  arXiv  Detail & Related papers  (2022-05-31T23:57:55Z)

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.