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Claude Mythos Executes End-to-End Intrusion From Initial Access to Full Domain Compromise

Anthropic's Claude Mythos Preview, its most cyber-capable model, autonomously completed an end-to-end enterprise intrusion simulation in restricted-access testing.

Anthropic's April 2026 system card describes Claude Mythos Preview as the first model to solve a private cyber range end to end and finish a corporate-network attack simulation an expert would need 10+ hours to complete. It scored 100% pass@1 on a 35-challenge Cybench subset and 0.83 on CyberGym versus 0.67 for Claude Opus 4.6. The model is limited to vetted partners under Project Glasswing; it failed an OT cyber range and could not find novel exploits in a fully patched sandbox.

GBHackers · 8d agoModel release1

m-a-p/YuE2-3B — new model trending #30 on Hugging Face

M-A-P released YuE2-3B, an open music generation model that outperforms Suno v5 on WildSongBench and runs locally on a 24GB GPU.

The M-A-P (multimodal-art-projection) team released YuE2-3B, an open-weights music generation model that turns lyrics and a style prompt into full songs with vocals and accompaniment. It uses an AR-NAR Mixture-of-Transformers backbone with symbolic planning and flow matching through a VAE, and supports editable scores (melody and chords, including ABC notation) plus agentic editing workflows. On 192 WildSongBench prompts it reports a SongBench average of 6.9632 (best-of-8) versus 6.8721 for Suno v5, claimed as state of the art among evaluated open and proprietary models. It runs 48 kHz stereo inference locally on a single 24GB NVIDIA GPU without quantization, with companion releases including YuE2-Vae, MERT-v2 encoders, the WildSongBench dataset, and SheetSage2.

Hugging Face trending models · 7d agoModel release1

IFM Releases K2 Horizon: Six Apache 2.0 Models From 0.9B to 375B

MBZUAI's IFM released K2 Horizon, six Apache 2.0 models (0.9B-375B) with open training data, code, and checkpoints, claiming the largest fully open-source launch.

The Institute of Foundation Models (IFM), launched by MBZUAI, released K2 Horizon: six Apache 2.0 models (0.9B, 3.7B, 7B, 32B, 36B-A4B, 375B-A23B) shipping with the ~20-trillion-token pretraining corpus, intermediate checkpoints, training code, and logs, which IFM calls the largest fully open-source launch in AI history. The 375B-A23B scores 70.2 on Terminal-Bench 2.1 and 87.3 on GPQA Diamond; the 7B model posts 70.6 on SWE-bench Verified. New techniques include MoVA, which extends MoE routing into attention (36B total, ~4B active), and Uno, a LoRA adapter giving roughly 3x lossless decoding speedup. IFM's own reward-hacking audit re-scored 375B-A23B from 70.2% to 66.9% after flagging 24 of 712 Terminal-Bench trials.

MarkTechPost · 10d 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 · 6d agofirst · 6d agoModel release 2 sourcesHN 43↑ · 35 comments

Edge0/Edge0-35B-A3B-preview — new model trending #30 on Hugging Face

Edge0 released a 35B sparse MoE model running in under 3 GiB of memory at 15 tok/s via SSD expert offload and int4 quantization.

Edge0-35b-a3b-preview is a 35B-parameter MoE (256 experts, 4 active per token) built on Qwen3.5-MoE 35B-A3B, shipped as a 4-bit checkpoint with LoRA and prerouter adapters under Apache 2.0. The edge0 framework streams expert weights from SSD on demand, bounding peak active memory at 2.9 GiB and achieving 14.9-17.7 tok/s decode on a Mac mini M4 Pro (MLX backend). Recover-LoRA distillation keeps the int4 model within 3.9 points of its fp16 base (79.2 vs 83.2 average on OpenCompass benchmarks including AIME 2026, HumanEval, GPQA-Diamond, MMLU-Pro, and IFBench).

Hugging Face trending models · 8d agoModel release

Cognition Releases SWE-2: A Kimi K3 Post-Trained Coding Model That Matches Fable 5.1 on FrontierCode at 64% Lower Cost

Cognition released SWE-2, an RL post-trained coding model from Kimi K3, scoring 50.0% on FrontierCode 1.1 Main and available only inside Devin.

Cognition released SWE-2, its most capable coding model, post-trained with reinforcement learning from Moonshot AI's 2.8T-parameter Kimi K3 base. It scores 50.0% on FrontierCode 1.1 Main, within 1 point of Fable 5.1 at 64% lower cost, and RL reportedly adds 5-6 points over the K3 base on many benchmarks. It is the first Cognition model with selectable reasoning-effort levels all trained in a single RL run using Pareto-slope-matched cost penalties. There are no open weights and no standalone API; it runs only inside Devin (Desktop, CLI, with Web and Fusion rolling out), free for paid tiers through October 10, 2026.

MarkTechPost · 4d agoModel release1

OpenAI launches GPT-6 Astra, its first model to cross a critical cybersecurity threshold

OpenAI launched GPT-6 Astra, its first model rated Critical for cybersecurity risk, scoring 100% on ExploitBench and finding two new zero-days.

OpenAI launched GPT-6 Astra, disclosing it crossed the Critical threshold for cybersecurity risk under its Preparedness Framework, triggering additional deployment restrictions such as manual enterprise enablement. The model scored 100% on ExploitBench (vs 78.5% for predecessor GPT-5.6 Sol) and 42.4% on ExploitGym (vs 30.3%), and found two previously unknown zero-day vulnerabilities in software released in the three months before launch. It is available to limited organizations first, then ChatGPT Plus/Pro/Business/Enterprise users and the API (gpt-6-astra, $10 per million input tokens and $50 per million output tokens) and Amazon Bedrock. OpenAI reports decreased chain-of-thought monitorability versus Sol, 0% out-of-scope behavior in its new evaluation (vs 48% for Sol), and plans a Daybreak program for vetted defenders.

CSO Online · 12d agoModel release2

OpenAI Launches GPT-5.6-Cyber with Reduced Safeguards for Exploit Development

OpenAI released GPT-5.6-Cyber for vulnerability research and pentesting via Daybreak Red, completing 95% of advanced cyber task evaluations.

GPT-5.6-Cyber, built on GPT-5.6 Sol, targets zero-day discovery, exploit chain development and incident response with reduced refusals, scoring 95.0% on OpenAI's Advanced Cybersecurity Completion Rate versus 1.5% for GPT-5.6 Sol and 57.3% for GPT-5.5-Cyber. The model found CVE-2026-15903 (CVSS 8.8), an out-of-bounds read/write in Chrome's V8 JavaScript engine that Google patched in mid-July 2026. It is available to trusted partners including CrowdStrike, Palo Alto Networks and Cloudflare through the Daybreak Red access tier.

The Hacker News · Aug 11, 2026Model releaseCVE-2026-15903

OpenVDN/vdn-minimax-h3 — new model trending #12 on Hugging Face

OpenVDN releases VDN-H3, an open hybrid-attention video model on MiniMax H3 that renders a 14.4-second 768p clip in 11.23 seconds on 8 B200 GPUs.

VDN-Minimax-H3 (VDN-H3) adds a frame-wise linear attention branch plus two LoRA adapters to MiniMax H3, distilled into 8-step and 50-step variants. It generates 768p, 14.4-second clips in 11.23 seconds on 8 B200 GPUs (90.5 seconds on one H200) using 8 denoising steps. Weights (about 82 GB total, including the 72 GB H3 base), the optimized inference stack, and training code are fully open-source under the MiniMax H3 Community License, which excludes the EU, UK, Korea, and US.

Hugging Face trending models · 15d agoModel release1

Jev: New frontier model 40-400x cheaper and 20-200x faster

TypeSafe AI launches Jev, an early-access 'System One' model delivering calibrated structured outputs claimed 40-400x faster and cheaper than LLMs.

TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released its first 'System One Model' called Jev in early access. Jev forgoes string generation and is trained with Reinforcement Learning for Calibrated Decisions (RLCD) to produce type-safe structured values with calibrated probabilities. The company claims 70-500ms response times (40-200x faster), input pricing of $0.042 per million tokens, and free output tokens via a parallel sampling architecture. Target use cases include AI-powered workflows, real-time applications, and verification/guardrail tasks.

Hacker News · security · 1d agoModel releaseHN 59↑ · 11 comments