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AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Open-source speech foundation model AuK unifies generation and editing, trained on 1.95 million hours, with distilled AuK-Flash achieving 4.5x speedup.

AuK is an open-source foundational model that unifies speech generation and editing through natural-language instructions and audio context, trained on approximately 3.03 billion instruction-audio instances and 1.95 million hours of supervision across five task families including generation, content editing, and acoustic editing. It combines a multimodal LLM for semantic conditioning, a VAE jointly trained on speech, general audio, and music, and a hybrid rectified-flow Transformer using dual-stream MMDiT blocks followed by unified single-stream DiT blocks. Post-training applies human-feedback preference optimization for editing and reward-based reinforcement learning for generation, and the distilled AuK-Flash performs 4-step inference without classifier-free guidance at a 4.5x wall-clock speedup. Source code and model weights are released.

Hugging Face daily papers · 9d agoModel release2

unsloth/Qwen3.8-Flash-Next-GGUF — new model trending #21 on Hugging Face

Qwen released Qwen3.8-Flash-Next, an experimental 125B-parameter open-weight MoE previewing the Qwen4 architecture, with Unsloth shipping optimized GGUF quants.

Qwen released Qwen3.8-Flash-Next, an experimental open-weight preview of the architecture planned to underpin Qwen4. The model has 125B parameters with 6B activated, 512 experts (10 routed plus 1 shared), Qwen Sparse Attention (QSA), Gated DeltaNet, Gated Residual, and n-gram embeddings, with 262,144-token native context extendable to 1,000,000 tokens. Unsloth provides Dynamic 3.0 GGUF quantizations, and multi-token prediction (MTP) delivers 1.3-1.7x faster inference via llama.cpp or Unsloth Desktop.

Hugging Face trending models · 21d agoModel release1

Show HN: MultiMatte, a Promptable Image Background Removal Model

Feyn releases MultiMatte, a promptable background-removal model fine-tuned from Meta's SAM 3 via LoRA, outputting alpha mattes that beat SAM 3 on segmentation benchmarks.

Feyn introduced MultiMatte, a promptable image background-removal model built on Meta's SAM 3 (860M parameters). It modifies only 19.49M parameters (2.27%) using a rank-16 LoRA adapter and replaces binary masks with alpha mattes to handle fuzzy boundaries like hair. On the DIS-VD benchmark it scores 0.901 S-measure versus SAM 3's 0.667, and it improves on SAM 3 across all twelve evaluated splits. Training used 19,953 images for 14,000 steps with focal and Dice loss, and the merged weights are available via the nobg library and a web demo.

Hacker News · security · 6d agoModel releaseHN 21↑ · 4 comments1

Sakana AI Launches Fugu Max and Fugu Ultra v2 for Cheaper, Stronger Multi-Agent Orchestration

Sakana AI released Fugu Max and Fugu Ultra v2, API-only orchestrator models that route tasks across model pools to cut costs and boost multi-step reasoning.

Sakana AI released Fugu Max and Fugu Ultra v2, two orchestrator models that route queries across a pool of third-party and open-weights models, including the NVIDIA Nemotron family. Fugu Max is priced at $2 per million input and $6 per million output tokens, 40-60% cheaper per output token than Sonnet 5, GPT 5.6 Terra, and Kimi K3, and reportedly wins 6 benchmarks including Terminal Bench 2.1 and GPQA Diamond. Fugu Ultra v2 targets complex multi-step reasoning, scoring 48.3 on Chartography and 74.3 on DeepSWE. Both are live through Sakana's OpenAI-compatible API only, with no open weights and no EU/EEA availability.

MarkTechPost · 5d agoModel release

Cohere Releases North Small Translate: A 218B MoE Translation Model That Scores 83.6 on WMT26 Across 50 Languages

Cohere released North Small Translate, an open-weight 218B MoE (25B active) translation model scoring 83.6 on WMT26 across 50 languages.

Cohere and Cohere Labs released North Small Translate, a decoder-only sparse Mixture-of-Experts translation model with 218B total and 25B active parameters, 128 experts with 8 activated per token plus shared experts, and 16K-token input and output context. In Cohere's vendor-reported WMT26 evaluation, judged by GPT-5.6-Sol, it scores 83.6 averaged across 50 languages (84.36 in an agentic multi-pass mode), ahead of DeepL NextGen (81.37), Qwen 3.5 397B A17B (81.56), GLM 5.2 (76.50), and Google Translate (68.20). The model was built with RWS's Language Weaver team, post-trained specifically for translation, and reports 112 output tokens per second versus 81 for Gemma 4 31B, with long-document xCOMET-XL scores of 48.9 versus 21.3 for Google Translate. It is available free on Cohere's Chat V2 API until rate limits, with three self-hosting checkpoints including a 4-bit NVFP4 variant running on 1x B200 or 2x H100.

MarkTechPost · 5d agoModel release

[AINews] GPT-6 Astra: OpenAI’s biggest LLM launch of all time

OpenAI launched GPT-6 Astra, its new flagship model, claiming state-of-the-art computer use, software engineering, math, and cybersecurity capabilities.

OpenAI launched GPT-6 Astra as its new flagship model, describing it as its most intelligent and aligned model with state-of-the-art computer use, software engineering, and math/science capabilities. Pricing is $10/$50 per 1M input/output tokens standard ($20/$100 fast tier), rolling out first to limited organizations, then ChatGPT Plus/Pro/Business/Enterprise, the API, and AWS. OpenAI claims 99.9% on ARC-AGI-3, 98% on FrontierMath Tier 4, and 100% on ExploitBench. Artificial Analysis scored Astra 67 on the Coding Agent Index and 61 on the Intelligence Index, behind Claude Fable 5.1, and the system card drew attention for reporting decreased chain-of-thought monitorability despite alignment gains.

Latent Space · 12d agoModel release3

Build more natural voice experiences with GPT‑Live‑1 in the API

OpenAI releases GPT-Live-1 in the API, a full-duplex voice model that handles interruptions natively and delegates reasoning to backend models.

OpenAI launched GPT-Live-1 in the API, a single-model full-duplex voice system that listens and speaks simultaneously, replacing chained STT-LLM-TTS architectures. It improves Full Duplex Bench performance by 30 percentage points over GPT-Realtime-2.1 and ranks #1 on Tau3 when paired with GPT-6 Astra at medium reasoning effort. Early partner Speak reported nearly 80% fewer interruptions in language tutoring. The API release costs $0.05 per minute for the front-end voice layer and supports telephony, native ASR transcripts, keyword biasing, and expanded voice and language options.

OpenAI Newsupdated · 6d agofirst · 6d agoModel release 2 sources1

Reducto Releases r-1: A Single Pass Document Parsing Model That Cuts Errors 20% at 1 Cent Per Page

Reducto launched r-1, a single-pass document parsing model claiming 20% error reduction over its legacy agentic pipeline, priced at 1 cent per page.

Reducto announced r-1, the first model in a new parsing family that replaces multi-stage agentic OCR with one full-page pass handling text, tables, figures, layout, formatting, and grounding with page-relative bounding boxes. The company reports a 20% error reduction measured against its own legacy agentic pipelines, plus vendor-run wins over Amazon Textract and Azure Document Intelligence on complex documents. Pricing is a flat 1 cent per page versus 3-6 cents for legacy models; r-1 is available in preview via the V3 Parse API with no open weights.

MarkTechPost · 8d agoModel release1

[AINews] NVIDIA buys HuggingFace for $13B, as OpenAI publishes their HF incident retro

Z.ai released open-weight GLM-5.3-Flash (320B/18B active, 1M context, MIT) while Nvidia confirmed buying Hugging Face for $13B.

Z.ai formally launched GLM-5.3-Flash, the model previously previewed as Ox Alpha: 320B total parameters with 18B active, a 1M-token context window, natively multimodal, MIT-licensed, and claimed on par with Claude Opus 4.8 on coding. Artificial Analysis scored it 57 on its Intelligence Index at $0.09 per task, roughly 7.5x cheaper than GLM-5.3, and it scored 84.3% on Terminal-Bench 2.1. Nvidia's $13B acquisition of Hugging Face (~80x its $150M ARR) was confirmed, nearly double its initial $7B January offer. The roundup also notes Qwen shipping an impressive Flash model on Chinese chips as part of a broader open-model narrative.

Latent Space · 20d agoModel release1

YuE2 · Frontier Music with Symbolic Planning

YuE2, a 3.59B-parameter music generation model, scores 6.9632 on SongBench, beating Suno v5 via symbolic planning.

YuE2 is a music generation model of roughly 3.59B parameters and 28 layers supporting song creation, covering, and agentic editing through editable ABC symbolic scores. Its best-of-8 setting reaches 6.9632 on SongBench, the highest mean among 15 evaluated settings on WildSongBench (192 prompts), ahead of Suno v5 at 6.8721. The project also introduces MERT2, whose 632M-parameter encoders achieve state of the art on 14 of 15 MARBLE metrics, and SheetSage2, which transcribes beats, downbeats, key, chords, structure, and melody with SOTA on 10 of 13 benchmark metrics.

Hacker News · AIupdated · 5d agofirst · 5d agoModel release 2 sourcesHN 43↑ · 35 comments

[AINews] Muse Spark 1.3 matches GPT-5.6-Sol, confirming Meta Superintelligence as the newest Frontier Lab, >90% discount for training

Meta's Muse Spark 1.3 reportedly ranks as the world's #3 model, matching frontier models from OpenAI and Anthropic with planned open weights.

The Latent Space AI News roundup leads with Muse Spark 1.3, promised in Zuckerberg's letter, which ranks #3 worldwide per AAII, is slated for open weights, and uses a pricing model over 90% cheaper when users opt in to training. The issue also covers the rumored Gemini 3.8 Flash launch and analysis arguing OpenAI's rumored looped-transformer 'Astra' architecture is a modest tweak rather than a breakthrough. Additional coverage includes ByteDance Seed's HarnessDev harness-evaluation benchmark, a retrieval-invoked actual-use evaluation method, Stanford's revamped agent engineering curricula, and Photon 2.1 adding TTS models and NVIDIA B200 support.

Latent Space · 13d agoModel release1

Cognition launches new SWE-2 model, Rivaling Fable 5.1 and GPT-Astra

Cognition released SWE-2, a coding model post-trained from Kimi K3 that scores 50.0% on FrontierCode 1.1 Main, near Fable 5.1 at 64% lower cost.

Cognition introduced SWE-2, its most advanced coding model, post-trained from the 2.8T-parameter Kimi K3 base model. It achieves 50.0% on FrontierCode 1.1 Main, 73.0% on DeepSWE 1.1, and 92.8% on Terminal-Bench 2.1, beating Grok 4.6 and SWE-1.7 while matching Fable 5.1 and GPT-5.6 Sol at a fraction of the price. The company says it scaled reinforcement learning to the multi-trillion-parameter regime for the first time, using Pareto-informed cost penalties that train all reasoning-effort levels in a single run, tripled RL environments, and NVFP4/FP8 quantization-aware training. SWE-2 is available today in Devin Desktop and CLI, with rollout on Devin Web and Fusion.

Hacker News · AIupdated · 4d agofirst · 6d agoModel release 10 sourcesHN 58↑ · 15 comments1

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent Space · 4d agoModel release 4 sources1