Klear: Unified audio–video generation, finally in sync

Kari Jaaskelainen

08 Jan 2026 — 1 min read

Meet Klear, an AI model that creates video and audio together—so lip movements match words, sounds land on time, and visuals stay coherent. It targets the sync and quality gaps seen in earlier open systems.

What makes it work

Single‑tower design with unified transformer blocks and an "Omni‑Full Attention" mechanism keeps audio and video tightly aligned and scales smoothly.
Progressive multi‑task training—random modality masking to joint optimization, plus a staged curriculum—builds robust, balanced A/V skills and avoids unimodal collapse.
New large‑scale dataset with dense captions; an automated pipeline filters and tightly aligns millions of audio‑video‑caption triplets.

Why it matters

High‑fidelity, instruction‑following clips in joint or single‑modality modes.
Strong generalization, including out‑of‑distribution cases.
Big gains over prior methods; performance comparable to Veo 3 (per authors).

Paper: https://arxiv.org/abs/2601.04151v1

Paper: https://arxiv.org/abs/2601.04151v1

Register: https://www.AiFeta.com

AI GenerativeAI Video Audio Multimodal DeepLearning Research AVSync

Meet GRACE: a moral governor for safer, more transparent AI

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing? Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making. * Moral Module: uses deontic logic and explicit reasons

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added