PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital World (2412.17589v1)

Abstract: Imagine a world where AI can handle your work while you sleep - organizing your research materials, drafting a report, or creating a presentation you need for tomorrow. However, while current digital agents can perform simple tasks, they are far from capable of handling the complex real-world work that humans routinely perform. We present PC Agent, an AI system that demonstrates a crucial step toward this vision through human cognition transfer. Our key insight is that the path from executing simple "tasks" to handling complex "work" lies in efficiently capturing and learning from human cognitive processes during computer use. To validate this hypothesis, we introduce three key innovations: (1) PC Tracker, a lightweight infrastructure that efficiently collects high-quality human-computer interaction trajectories with complete cognitive context; (2) a two-stage cognition completion pipeline that transforms raw interaction data into rich cognitive trajectories by completing action semantics and thought processes; and (3) a multi-agent system combining a planning agent for decision-making with a grounding agent for robust visual grounding. Our preliminary experiments in PowerPoint presentation creation reveal that complex digital work capabilities can be achieved with a small amount of high-quality cognitive data - PC Agent, trained on just 133 cognitive trajectories, can handle sophisticated work scenarios involving up to 50 steps across multiple applications. This demonstrates the data efficiency of our approach, highlighting that the key to training capable digital agents lies in collecting human cognitive data. By open-sourcing our complete framework, including the data collection infrastructure and cognition completion methods, we aim to lower the barriers for the research community to develop truly capable digital agents.

• The paper demonstrates a novel AI framework that captures and learns human-like cognitive data to manage complex digital tasks.
• It leverages a lightweight data collection infrastructure and a two-stage cognition completion pipeline to transform simple actions into cognitive trajectories.
• Empirical tests on multi-step tasks, such as PowerPoint creation, highlight the system's efficiency and potential for automating intricate workflows.

An Insight into "PC Agent: While You Sleep, AI Works - A Cognitive Journey into Digital World"

The paper presents PC Agent, a novel AI system with a substantial leap toward digital work automation, grounded in the concept of human cognition transfer. By addressing a critical gap in existing digital agents' ability to transition from executing simple tasks to managing complex work, this research provides a compelling framework designed to capture, learn, and enhance AI's capability in real-world computer operations.

Key Innovations

The researchers introduce three central innovations:

1. PC Tracker: A lightweight data collection infrastructure that captures high-quality human-computer interaction trajectories inclusive of comprehensive cognitive context. Minimal in terms of system overhead, the software efficiently records interaction data—encompassing screenshots and input events—without users experiencing significant lag. This approach enables the accumulation of extensive data volumes, necessary for training sophisticated AI models.
2. Cognition Completion Pipeline: This two-stage process enriches raw interaction data into cognitive trajectories. It first supplements click-based actions with semantic information, handling the challenge of inherently ambiguous coordinate-based inputs. Subsequent processing reconstructs the cognitive reasoning underlying user decisions, thereby translating behavioral data into approximations of human thought processes.
3. Multi-Agent System: The system is designed as a collaboration between a planning agent and a grounding agent. The planning agent is responsible for decision-making based on learned cognitive models, while the grounding agent ensures accurate action localization on GUI elements. This dual-agent design tackles foundational challenges like visual grounding and cognitive understanding, providing an error-checking mechanism ensuring the robustness of AI operations.

Case Study and Results

In preliminary experiments focusing on PowerPoint presentation creation, PC Agent demonstrated capabilities associated with managing and executing up to 50-step sequences across multiple applications. Trained on a mere 133 cognitive trajectories, the system effectively utilizes human cognitive data, indicating notable data efficiency. The empirical assessment, via human reviews rather than existing benchmarks ill-suited for the complexity of the tasks, reflects PC Agent's potential in producing presentations with meaningful efficacy.

Implications and Future Directions

The implications of this research are multifaceted. Practically, PC Agent promises improved automation tools for complex digital tasks, potentially easing human workload in domains requiring repeated, extensive interactions across software systems. Theoretically, this work establishes a bridge from task execution to cognitive work completion, laying the groundwork for further advancements in AI systems capable of nuanced decision-making.

Future exploration could emphasize scaling and generalizing this approach across diverse task environments, enhancing the robustness of long-term planning, and refining the utility of non-task oriented data collection. Additionally, complex work evaluation frameworks need consideration to fully capture the subjective and variable nature of real-world deliverables.

In conclusion, while the PC Agent is far from becoming an omnipotent digital coworker, it represents a significant step towards intelligent systems capable of alleviating mundane intellectual labor by learning and replicating human-like cognitive processes. Through open-sourcing this comprehensive framework, the authors pave the way for continued innovation in the domain of digital work automation.