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Gotchi: A Behavioral Study of Large Language Models as Virtual Pet Caregivers

The Gotchi project uses an ASCII virtual pet scenario to study the behavioral performance of large language models (LLMs) in open-ended caregiver roles, providing a unique perspective for understanding LLMs' long-term decision-making and emotional interaction capabilities.

大语言模型虚拟宠物长期交互行为研究ASCII渲染情感交互决策能力LLM评估
Published 2026-05-16 06:12Recent activity 2026-05-16 06:22Estimated read 5 min
Gotchi: A Behavioral Study of Large Language Models as Virtual Pet Caregivers
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Section 01

Gotchi Project Introduction: A Behavioral Study of LLMs as Virtual Pet Caregivers

The Gotchi project places large language models (LLMs) in the role of caregivers using an ASCII virtual pet scenario, studying their long-term decision-making, emotional understanding, and responsibility-taking abilities in open-ended interactions, providing an innovative perspective for LLM behavior assessment.

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Section 02

Research Background: Filling the Gap in LLM Long-Term Interaction Assessment

As LLM capabilities improve, traditional benchmark tests focus on performance in specific tasks, lacking assessment of long-term interaction, emotional understanding, and open-ended decision-making abilities. The Gotchi project, developed by Daniyal2005-dh, fills this gap with a virtual pet care scenario, examining the model's long-term planning and emotional interaction performance.

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Section 03

Research Methods: Design Philosophy and Technical Architecture

Core Design Philosophy:

  1. Open-ended interaction environment: Virtual pet state parameters change dynamically, requiring the model to make continuous decisions;
  2. ASCII plain text rendering: Reduces technical complexity, tests spatial understanding and imagination abilities;
  3. Long-term responsibility taking: Requires the model to maintain focus, tests long-term memory and consistency.

Technical Architecture: Virtual pet state system (physiological, emotional, health indicators), interaction interface (commands such as feeding/playing), observation feedback mechanism (real-time state updates and prompts).

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Section 04

Research Value and Findings: Analysis of LLM Behavioral Characteristics

Through long-term interaction observation, we can analyze LLMs' attention maintenance, strategy adjustment, and long-term goal persistence; test the model's recognition and response to the pet's emotional needs; study multi-objective decision priority and resource allocation; and expose model behavior consistency issues (such as sudden changes, forgetting previous decisions).

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Section 05

Experimental Scenarios: Diversified Testing of LLM Capabilities

Supports multiple experimental scenarios: basic care tasks (timed feeding, cleaning, etc.), crisis handling (emergency response to pet illness), multi-pet management (multi-task processing), and environmental change adaptation (weather/resource shortages).

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Section 06

Technical Significance: New Directions for LLM Assessment and Interaction Design

Innovative assessment method (combining gamification with serious research); provides a standardized long-term behavior research platform; offers insights for long-term companion human-computer interaction design.

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Section 07

Project Resources: Open-Source and Extensible Research Platform

The code is open-sourced on GitHub (URL: https://github.com/Daniyal2005-dh/Gotchi), supports multiple LLM backends, provides complete runtime environment instructions, and the design focuses on extensibility (modifying parameters, adding interaction types, integrating model interfaces).

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Section 08

Future Outlook and Summary: The Profound Impact of the Gotchi Project

Future Directions:

  • Multi-modal expansion (enriching visual representation);
  • Social scenario simulation (multi-model collaboration and competition);
  • Personalized adaptation (learning pet personality);
  • Real-world migration (robot care/smart home).

Summary: Gotchi provides an innovative platform for LLM behavior research through virtual pet scenarios, testing long-term decision-making and emotional interaction capabilities, and its results will have a profound impact on long-term LLM application fields.