Four fraud detectors and three fine-tuned game policies on Hugging Face. The results, the limits, and the same-row comparison with stock Laya.
What a reasoning language model is, how RLVR and GRPO train one, what the evidence says RL adds and what it doesn't, the open-weight landscape from DeepSeek-R1 to Qwen thinking, GLM-5 and Kimi K2 Thinking, and what long chain-of-thought costs to serve — the measurements an enterprise evaluation should run before procurement.
CID's own controls trained a recipe advisor from the platform's outcome ledger and promoted it twice — the second beating the first — alongside two rejections the gate kept honest. A pre-registered fairness experiment met both its criteria.
What recursive self-improvement is, where AI labs stand in 2026, the levels between a static model and a system that improves how it improves, and why the controls come first.
How to fine-tune a Jev-class System One decision model on CID in four steps, keep it current with a fine-tuning loop, and what it scored on fraud decisions, public benchmarks and real-time games.
Jev proved software wants typed, calibrated decisions, not prose. What the independent numbers say, why you cannot fine-tune Jev, and how to serve, fine-tune or train a System One model on your own hardware with CID.
Why an AI agent's tools, context and checks can matter as much as its model, with recent research on harness engineering and its limits.
AI releases include more than code. How SDLC practices extend to models, prompts, retrieval and runtime—and how CID connects execution, policy and evidence.
Quantization went from compression detail to model-governance problem. The history in four squeezes, the KV-cache math nobody runs, and how CID gates quantized checkpoints like the new models they are.
Data poisoning is a supply-chain attack that ships in your weights. What it does, the modern defense stack, and how CID makes it somebody else's problem.
The AI stack is closing below the model line — chips, racks, clouds — and opening above it. The model layer is the only layer an enterprise can own. Here's how to own it properly.
Fine-tuning turned every enterprise into a model builder — and almost none of them keep a build record. Why the fine-tuning boom is a lineage problem, and how STEAV solves it.
Sovereign AI means more than data residency. What real AI sovereignty requires — owning the model, the build record, and the exit — and how STEAV builds it.
Centralized training wins whenever the data can move. Federated learning is what you reach for when it can’t — and most of it doesn’t work yet. An honest cost sheet for both, and what closing the gap actually takes.
Model-build lineage is a signed, verifiable record of how an AI model was built — an AI bill of materials. What it proves, and how STEAV produces it.
Book a demo to watch STEAV run on a real workflow.