mlsanityData to 7 Oct 2026

OrchVis: Hierarchical Multi-Agent Orchestration for Human Oversight

arXiv 2510.24937 ↗First posted 28 Oct 2025cs.HCcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

We introduce OrchVis, a multi-agent orchestration framework that visualizes, verifies, and coordinates goal-driven collaboration among LLM-based agents. Through hierarchical goal alignment, task assignment, and conflict resolution, OrchVis enables humans to supervise complex multi-agent workflows without micromanaging each step. The system parses user intent into structured goals, monitors execution via automated verification, and exposes inter-agent dependencies through an interactive planning panel. When conflicts arise, users can explore system-proposed alternatives and selectively replan. OrchVis advances human-centered design for multi-agent systems by combining transparent visualization with adaptive autonomy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 12 of its 35 references that are in the map. First posted on arXiv 28 Oct 2025.

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12 of 35 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks18 Dec
  2. 2024Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks7 Nov

Other references · 10

  1. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  2. 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 616
  3. 2024DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?cited by 119
  4. 2024SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulationcited by 100
  5. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  6. 2024Cocoa: Co-Planning and Co-Execution with AI Agentscited by 38
  7. 2024Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decompositioncited by 29
  8. 2023VAL: Interactive Task Learning with GPT Dialog Parsingcited by 14
  9. 2025OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Modelscited by 7
  10. 2024One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AIcited by 4

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  6. 2026HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation
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