Typhoon-S-4B NitiBench-CCL Legal Agent (Research Preview)

This 4 billion parameter model is a research artifact from FILM6912, specifically designed to explore domain-specific sovereignty in AI. It is not a general-purpose instruction model or intended for production legal use.

Key Capabilities & Innovations

• Agentic Reinforcement Fine-Tuning (RFT): The model is trained as a multi-step agent, operating within a controlled RAG environment with search and read tools.
• InK-GRPO (Injected Knowledge GRPO): Augments GRPO with a stochastic auxiliary next-token prediction objective on in-domain Thai legal text, injecting domain knowledge during RFT.
• Specialized Legal Reasoning: Post-trained on NitiBench (CCL) and aligned Thai legal corpora, focusing on question-answer tasks.
• Benchmark Performance: Achieves 78.02% accuracy on the NitiBench (Thai Legal Reasoning, Agentic) benchmark in its specified agentic setup, outperforming larger models like GPT-5 in this specific context.

Intended Use & Limitations

This model is research-only and primarily for studying Agentic RFT and InK-GRPO behavior. It is only meaningful when evaluated using its official agentic setup (agent + RAG environment) and is not suitable for real-world legal advice or general-purpose tasks. Performance is highly environment-dependent and benchmark-specific, with no guarantees for safety, bias, or robustness.