ZeroHour
DashboardNewsCVEsExploitsIncidentsAIBriefAnalyticsEntitiesWatchlistIOCsAsk the feedSourcesFeeds & APIKiosk mode

Search: “multilingual”

5 stories in the last 3d

Searchkeywordsmart Sortlatesthotimportancerelevance
DomainAllCyberAIWindow24h3d7d30dAllIn the wildImportant onlyShow duplicates
CategoryAllAI industry 2AI research 2Model release 1
SeverityAllcritical 0high 0medium 0low 0info 5

Mistral X Mozilla: Private, Multilingual AI Browsing

Mistral and Mozilla partnered to power Firefox's Smart Window AI browsing assistant in France and North America, with zero data retention.

Mozilla's Firefox Smart Window (beta) AI browsing assistant is now powered by Mistral models for users in France and North America, with the UK and Germany expected later this year. Conversations are not saved on Mozilla's servers by default, and Mistral agreed to zero data retention. Both companies frame the partnership as advancing open-source, privacy-first, and regionally fine-tuned AI, with models trained on regional languages, dialects, and cultural context.

Hacker News · securityupdated · 11h agofirst · 18h agoAI industry 2 sourcesHN 35↑ · 8 comments2

Google Releases Gemini 3.8 Live and 3.8 Live Extended Thinking for Production Grade Voice Agents

Google launches Gemini 3.8 Live and Extended Thinking speech-to-speech models for production voice agents, topping speech-to-speech benchmarks.

Google released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, native speech-to-speech models for real-time voice agents, available hosted via the Gemini Live API and AI Studio. Extended Thinking ranks #1 on Artificial Analysis' Speech-to-Speech Quality Index with 82.6, scores 68.6% on τ-Voice, 35.1% on Sierra's τ-Voice-banking, and 97.7% on Big Bench Audio. The models support asynchronous function calling, near-real-time visual context, alphanumeric precision, and 97 languages, priced at $0.005/min audio input and $0.018/min audio output. All generated audio carries Google DeepMind's imperceptible SynthID watermark.

MarkTechPost · 1d agoModel release 3 sources

Tables Decoded: DELTA for Structure, TARQA for Understanding

DELTA extracts tables into compact OTSL text and TARQA fine-tunes LLMs on it, beating VLM baselines on table QA.

DELTA separates physical structure recognition, logical structure recognition, and OCR to output tables in Optimised Table Structure Language (OTSL), a compact unified format encoding cell arrangements and content. It achieves TEDS-Structure scores comparable to state-of-the-art methods across FinTabNet, PubTabNet, and PubTables-1M, with robustness tested on a curated Hindi benchmark, TORQUE. TARQA, an LLM fine-tuned on OTSL sequences, gains 9.3 percentage points on WTQ TabQA and 9.2 points on FinTabNetQA TabVQA; code, models, and the benchmark are released on GitHub.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

AI for everyone in every language

Google says its AI now spans 300+ languages reaching 7 billion people, unveiling Gemini 3.5 Transcribe, Live Translate, and TranslateGemma models.

Google announced its technologies now support more than 300 languages spoken by 7 billion people, 86% of the global population. Gemini 3.5 Live Translate powers real-time spoken translation across 70 languages and 2,000+ language pairs, while Gemini 3.5 Transcribe is its most precise speech-to-text model. Its Universal Speech Model was trained on 12 million hours of audio using cross-lingual transfer learning, and TranslateGemma is a family of lightweight open translation models covering 55 languages that run on-device. Open-data partnerships include WAXAL covering 27 Sub-Saharan African languages and Project Vaani with 30,000+ hours of speech across 109 languages.

Google · AI · 1d agoAI industry

How much of F-Droid is LLM generated?

A FOSS maintainer manually graded 102 F-Droid apps from the September 12, 2026 update batch, finding many show signs of LLM-generated code.

A student and FOSS app maintainer reviewed 102 apps pushed to F-Droid on September 12, 2026, assigning each a three-tier rating for likelihood of LLM-authored code (mostly AI >50%, hard to say/mostly human, no signs of AI). The heuristic relies on commit aesthetics, README and branding style, and the presence of agentic infrastructure like Claude Code or Codex, which automatically places an app in the 'mostly AI' tier. Example ratings include Amber (Nostr event signer) as mostly AI, and Aria for Misskey as showing no AI signs. The author stresses reliable detection of LLM-generated code from text alone is impossible, so ratings are approximate.

Hacker News · AI · 1d agoAI researchHN 46↑ · 23 comments