Agents

GoD-IRM introduces intermediate reward modeling for structured decision-making in language models, assigning rewards at each divergence point in a reasoning trajectory. This approach enables fine-grained credit assignment, improving model robustness in long-horizon problem-solving. By reinforcing decision-making rather than just final outputs, GoD-IRM aligns language models more closely with traditional agent-based RL.

In this deep dive, I break down my iterative modifications to Naïve RAG, tackling the challenge of retrieving and reasoning over complex, hierarchical reports with hidden entity relationships. From agentic parsing and multi-hop retrieval to metadata-validated vector databases, I detail how I optimized retrieval, generation, and reasoning pipelines—reducing response time from 5 minutes to 15 seconds. This post unpacks key technical challenges, my final agentic reasoning pipeline, and the lessons learned in designing AI systems for high-stakes government use cases.