Agora-4B

Model Card: Agora-4B

Model Summary

Agora-4B is a 4-billion parameter, BF16-precision transformer language model, designed for ethical, inclusive, and adaptive dialogue in multi-user domestic environments. Inspired by the research paper "Plural Voices, Single Agent: Towards Inclusive AI in Multi-User Domestic Spaces", Agora-4B incorporates principles of fairness, value alignment, and accessibility to better serve diverse household users—including children, elderly, and Neurodivergent individuals.

Repository: JoydeepC/Agora-4B Paper: Plural Voices, Single Agent Model size: 4B parameters Tensor type: BF16 Files: Safetensors format (2 shards, ~8.07 GB), tokenizer files, configs, chat templates, etc.

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Intended Use

Agora-4B is intended for use as a core assistant agent in domestic AI deployments, especially in settings with multiple users and overlapping accessibility needs. Typical scenarios include: Domestic voice assistants which must mediate between adult, child, and elderly users Applications where context-sensitive safety, fairness, or ethical intervention is required Research or development in inclusive, privacy-first AI for multi-agent, multi-user environments

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Model Architecture & Training

Architecture: 4B-parameter transformer, trained with curriculum blending human and synthetic dialogue Objective: Optimized for fairness, multi-value alignment, ethical compliance, and accessibility-aware conversation Training Data: Curated public datasets covering mental health, eldercare, education, and moral reasoning. Enhanced with fairness-aware, multi-user scenarios and privacy-centric synthetic examples. Ethical Safeguards: Includes adaptive safety scaffolds (e.g., age-specific explanations, guidance for Neurodivergent users), autonomy sliders, and safe conflict resolution.

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Key Features

Real-Time Value Alignment: Dynamically identifies and negotiates conflicting user needs, values, and accessibility requirements Inclusive Design: Special handling for overlooked populations (children, elderly, Neurodivergent), including step-by-step instructions, accessible language, and equitable interaction Privacy-Focused: Avoids unnecessary data retention or sharing Adaptivity: Safety, autonomy, and guidance dynamically adjusted per user/context Design Innovations: Video guidance, autonomy sliders, family hubs, adaptive dashboards Performance: Outperforms baselines in compliance, fairness, and safety (see paper for details)

• Compliance: 76% (vs 70% baseline)
• Fairness: 90% (vs 85% baseline)
• Safety violations: 0% (vs 7% baseline)

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Citation

If you use this model, please cite:

@misc{chandra2025pluralvoicessingleagent,
      title={Plural Voices, Single Agent: Towards Inclusive AI in Multi-User Domestic Spaces}, 
      author={Joydeep Chandra and Satyam Kumar Navneet},
      year={2025},
      eprint={2510.19008},
      archivePrefix={arXiv},
      primaryClass={cs.HC},
      url={https://arxiv.org/abs/2510.19008}, 
}

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Further Reading

arXiv:2510.19008 Project repository (HuggingFace)

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This model and codebase are open sourced for reproducibility and collaborative research on inclusive, agentic AI.