Apodex scales reasoning verification over parameters

Scaling verification and orchestration rather than raw model size is a compelling path forward for agent reliability. By separating generation from verification, Apodex addresses the cascading failure problem common in long-horizon tasks.

• –Flagship API uses up to 150 sub-agents to construct an auditable evidence graph, minimizing hallucinations in critical research domains.
• –Open-weight Smol models (0.8B, 2B, and 4B parameters) enable developers to run local verification loops efficiently without massive infrastructure costs.
• –AgentHarness open-source framework offers reproducible benchmarking for research agents, preventing uncontrolled step-count loops.
• –The 35B MoE 'mini' model offers a self-hosted alternative for deep-research pipelines requiring high local reasoning capacity.
• –This architecture shifts the focus of AI development from monolithic model scaling to agent-orchestration patterns.