sreejanjalagam/lead-architect-compliance

TEXT GENERATIONConcurrency Cost:1Model Size:0.5BQuant:BF16Ctx Length:32kPublished:Apr 25, 2026Architecture:Transformer Cold

The sreejanjalagam/lead-architect-compliance model is a 0.5 billion parameter agent, based on Qwen/Qwen2.5-0.5B-Instruct, specifically fine-tuned for multi-agent compliance arbitration in board-room consensus scenarios. It utilizes Supervised Fine-Tuning (SFT) and Goal-Oriented Reinforcement Learning (GRPO) to prioritize compliance directives over other proposals. This model excels at navigating complex regulatory frameworks like HIPAA, GDPR, and SOC2, aiming to achieve consensus while ensuring strict adherence to compliance rules within a 32768 token context window.

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Lead Architect Compliance Agent

The sreejanjalagam/lead-architect-compliance model is a specialized 0.5 billion parameter agent built upon the Qwen/Qwen2.5-0.5B-Instruct base, enhanced with LoRA adapters. It is designed to act as a compliance arbiter in multi-agent board-room simulations, with a primary mission to ensure compliance overrides all other proposals.

Key Capabilities

  • Compliance-Driven Decision Making: Trained with Supervised Fine-Tuning (SFT) on expert trajectories and Goal-Oriented Reinforcement Learning (GRPO) with reward shaping, the model prioritizes compliance in consensus-seeking scenarios.
  • Regulatory Knowledge: Incorporates knowledge bases for critical regulations including HIPAA (45 CFR Part 164), GDPR (Article 32), and SOC2 (CC6).
  • Action Space: Can submit_position with compliance reasoning, cross_examine other agents, cast_vote, adapt_api_schema with citations, and inject_preference (compliance directives).
  • Performance: Achieves a consensus rate of 0.59 (target 0.47) and demonstrates specific compliance performance across HIPAA (0.407), GDPR (0.659), and SOC2 (0.656) scenarios.
  • Robustness: Designed to guard against failure modes such as ignoring preference injection, citing incorrect regulations, and failing to form coalitions.

Good For

  • Simulating compliance-focused decision-making processes in corporate governance.
  • Developing agents that can enforce regulatory adherence in complex, multi-stakeholder environments.
  • Research into goal-oriented reinforcement learning for policy and compliance applications.

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