Model-First Reasoning — explicit problem modeling cuts hallucinations in LLM agents
A May 2026 arXiv preprint introduces Model-First Reasoning (MFR): a paradigm where an LLM agent is required to construct an explicit problem model before proposing a solution. The reported effect is a sharp drop in hallucinated steps and a more inspectable trace.
The technique sits between chain-of-thought prompting and formal program synthesis. Where CoT asks the model to "think step by step" but lets the model choose its own representation, MFR forces a structured intermediate object: entities, relations, constraints, goal — all named before any action is proposed. The authors argue this is what makes agent behavior auditable rather than improvisational.
Why it matters now: as agent runtimes (Claude Code, Codex subagents, Cursor 3 Agents Window) ship in production, the cost of hallucinated steps grows quadratically — they aren't just wrong, they propagate into multi-agent contexts. An explicit problem model is a checkpoint a supervisor agent can verify.