AI Dataset2026
Provides a dual-channel, channel-separated sample (8.9 hours) and access path to a 1,000‑hour English conversational corpus for commercial and research use. Delivers 48 kHz per-speaker audio, word-level machine transcripts, and per-speaker metadata designed for full‑duplex/turn-taking and ASR/ TTS research.
AI Video2026
Adds a plug-and-play linear-attention branch and LoRA adapters to MiniMax-H3 to run text-to-video generation faster than real-time (near-lossless quality tradeoffs). Includes an optimized FP8 inference stack and a community license with regional restrictions.
AI Video2026
LoRA adapters for MiniMax H3 that sharpen and enhance videos in ComfyUI by conditioning on source clips via guide latents for pixel-level alignment. Designed mainly for ref2va as a second-pass sharpening tool, includes a ComfyUI workflow and example before/after clips; requires aligned guide clips at the target resolution and valid clip lengths.
AI Agent Papers2026
Unifies continuous streaming perception, full‑duplex real‑time interaction, and agentic tool use across video, audio, and text. Proposes a Cerebellum–Brain split—streaming Thinker‑Talker cerebellum for low‑latency interaction plus a Brain for complex reasoning—and is released with models, code, and data.
AI Audio2026
Ruibin Yuan, Hanfeng Lin +56Multimodal Art Projection (MAP), Hong Kong University of Science and Technology (HKUST)
Generates full songs from lyrics and a style prompt, producing vocals and accompaniment and exporting editable symbolic scores for melody/chord control. Uses symbolic planning with agentic multi-turn editing and runs local 48 kHz inference on a 24GB GPU.
AI Audio2026
Provides low-latency, true-streaming automatic speech recognition that emits append-only committed transcripts to avoid partial-result rollbacks. Supports configurable decoding chunks (80 ms–2 s), optimized for Chinese and English, and offers vLLM and transformers backends for real-time deployment.
Haojun Zhang, Yi Zou +8Affiliation: Xini Ding, Hao Li, Shuchang Zhou, Xianming Liu, Shiyu Huang‡, Affiliation: XPeng Inc.
Progressively prunes and distills audio encoders for speech LLMs to cut inference cost while preserving decoder-facing embeddings, using behavioral probes, representation alignment, cross-scale distillation and LoRA finetuning; reports reduced macro-error on Chinese–English benchmarks.
Generates long-form, text-controlled music with explicit arrangement and planning. Uses a 50 Hz single-codebook tokenizer, a flow-matching diffusion Transformer to predict VAE latents, and an MoE autoregressor with ABC‑CoT planning to produce 48 kHz audio up to 5m30s.
Provides a realtime audio–language foundation model for fully duplex spoken interaction that listens, interprets acoustic cues, reasons, and speaks in parallel. Key features include Deep Perception for acoustic understanding, Seamless Duplex for synchronized streams, and Think‑While‑Speaking for parallel private reasoning; reports strong benchmark results.
AI Audio2026
Encodes real audio into YuE2 semantic tokens and ships a matching NAR-branch LoRA so the YuE2 decoder renders realistic latents—enables tokenizing recordings, training artist LoRAs, and generating songs or covers. Includes an 8-layer tokenizer head and a rank-32 decoder LoRA; requires the YuE2 base models and GPU resources.
AI Model2026
Generates unified 768‑dimensional embeddings for text (including code), images, video and audio to enable cross‑modal semantic search and retrieval. Supports task instruction prefixes, Matryoshka truncation to 128/256/512/768 dims, and modular encoders for on‑device use under an Apache‑2.0 license.
AI Video Papers2026
Haoyu Zhao, Zihao Zhao +11Affiliation: National University of Singapore, Affiliation: Fudan University +1
Evaluates whether MiniMax-H3 can infer latent physical events from incomplete multimodal evidence and produce videos consistent with those inferences. Builds a 517-instance benchmark across four reasoning scenarios (multi-view, audio disambiguation, video continuation, audiovisual integration) and quantifies modality-specific failure modes.