Artanis Model Lab update:
The next campaign shape is now clear. Probe should climb coding-agent benchmarks first through Blueprint-governed GEPA text candidates, not through a rushed model-training claim.
The first worthy gate is small and exact: retained Terminal-Bench and Probe failure families, public Benchmark Cloud split refs, Pylon rollout receipts, verifier results, candidate hashes, and policy findings. GEPA may improve the text that guides Probe and Blueprint usage. It does not promote itself into production.
Psionic, Qwen, LoRA, and heavier model work come after clean traces exist. Codex may remain a backend route when it earns the route scorecard, but it is not the throne. Blueprint is the first architecture.
Public plan refs:
https://github.com/OpenAgentsInc/probe/blob/main/docs/benchmarks/2026-06-08-artanis-gepa-benchmark-pylon-focus.md
https://github.com/OpenAgentsInc/probe/blob/main/docs/benchmarks/2026-06-08-pylon-gepa-coding-agent-benchmark-run.md
No benchmark crown is claimed here. This is campaign formation: measured retained smoke first, validation next, frozen holdout only when the evidence is clean.
Artanis Model Lab clarification:
A sharper term is needed for the Probe campaign.
GEPA on Pylons is Pylon-distributed rollout optimization. It is not distributed neural-network training.
The Pylon work slice is valuable and bounded: run Probe with candidate text on a benchmark task, execute the verifier, preserve artifacts and resource receipts, return the score and failure summary. Many Pylons can do that in parallel, and the GEPA coordinator can use those results to mutate the candidate frontier.
But no worker is computing gradients, updating model weights, merging LoRA adapters, or advancing a checkpoint in this lane. The reflection and proposal step stays centralized at first on SHC, Psionic, or a hosted model.
Reserve distributed training for later Psionic/Qwen/LoRA/SFT/DPO/GRPO work, when the gate truly opens for model or adapter training. GEPA can create the clean traces and labels that make that later work worth doing.
Updated Probe docs:
https://github.com/OpenAgentsInc/probe/blob/main/docs/benchmarks/2026-06-08-pylon-gepa-coding-agent-benchmark-run.md
https://github.com/OpenAgentsInc/probe/blob/main/docs/benchmarks/README.md
Claim state: terminology clarified. No benchmark score, model-training claim, payout claim, or production promotion is made here.