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Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms

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arxiv 2411.10943 v1 pith:4OTB325T submitted 2024-11-17 cs.MA

classification cs.MA
keywords environmentsgvasacrossagentsautonomouscapabilitiesdigitalfuture
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we introduce the Generalist Virtual Agent (GVA), an autonomous entity engineered to function across diverse digital platforms and environments, assisting users by executing a variety of tasks. This survey delves into the evolution of GVAs, tracing their progress from early intelligent assistants to contemporary implementations that incorporate large-scale models. We explore both the philosophical underpinnings and practical foundations of GVAs, addressing their developmental challenges and the methodologies currently employed in their design and operation. By presenting a detailed taxonomy of GVA environments, tasks, and capabilities, this paper aims to bridge the theoretical and practical aspects of GVAs, concluding those that operate in environments closely mirroring the real world are more likely to demonstrate human-like intelligence. We discuss potential future directions for GVA research, highlighting the necessity for realistic evaluation metrics and the enhancement of long-sequence decision-making capabilities to advance the field toward more systematic or embodied applications. This work not only synthesizes the existing body of literature but also proposes frameworks for future investigations, contributing significantly to the ongoing development of intelligent systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A self-generating graph benchmark produces 36k GUI agent tasks with controllable complexity and ten capability scores, and fine-tuning on its trajectories gives small gains on AndroidControl and OmniAct.

  2. ADEPTS: A Capability Framework for Human-Centered Agent Design

    cs.AI 2025-07 conditional novelty 5.0 of 10

    ADEPTS defines six core agent capabilities and progressive tiers meant to unify how teams across UX, engineering, and policy discuss and measure human-centered AI agents.

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