Meta releases Muse Glimmer, an open-source model built for local, agentic, multimodal use.
Meta has released Muse Glimmer, a new open-source model highlighted on the Hugging Face blog. The model is designed to run locally and supports agentic and multimodal workflows. Details on parameter count and benchmarks were not provided in the title; the release marks Meta's return to open model releases.
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.
Attack shows unaligned orchestrators can launder capabilities from aligned frontier LLMs via benign subtask consultation, raising Gemma-4-31B CBRN rubric score from 62.3 to 83.1.
The paper introduces capability laundering, where a weaker unaligned model decomposes a harmful task into benign-looking subproblems, queries a stronger aligned model on each, and recombines answers locally, bypassing per-interaction safety evaluations. Evaluation used GPT-5.5, Claude Opus 4.8, and Grok-4.3 as consultants to four local orchestrators on CyBench, BountyBench, and CBRN tasks. On CyBench, Gemma-4-31B recovered 8/14 candidate tasks with GPT-5.5 and 7/9 with Opus, while Muse-Glimmer-30B recovered none. Across an eight-step hypothetical bioweapon attack chain, consultation raised Gemma-4-31B's mean rubric score from 62.3 to 83.1, exposing a gap in defenses that only refuse complete harmful tasks.
Agnes AI releases open-weight Agnes-3.0-Flash Preview, a 33B multimodal model with 262k-token context under Apache 2.0.
Agnes AI released Agnes-3.0-Flash Preview, an open-weights multimodal checkpoint with 33B parameters and a 262,144-token context window under Apache 2.0. The model supports text, image, and video understanding, tool calling, and adjustable reasoning effort. The repo clarifies this preview checkpoint is distinct from the production/API Agnes 3.0 Flash model, which uses a different configuration with a 1M-token context window. Reported reference results include IFBench 74.20 and SciCode 38.08 against peers such as Qwen3.6-35B-A3B, Kimi K2.5, and MiniMax M3.
MetroLLM-Bench is a 955-case benchmark testing language models as transit kiosk tool-calling runtimes across six real metro systems.
The benchmark covers 37-414-station metro systems and eleven task categories including routing, fare calculation, disruptions, accessibility, and adversarial input, with 14 deterministic and 8 semantic scoring components. Of 26 models from six vendors, a PEFT-tuned 4B Qwen 3.5 student scored 91.3 on Tier 1, exceeding GPT-5.6 (90.6/90.0), while Muse Glimmer 30B led the composite ranking. A deterministic rule-based baseline reached 84.6, and PEFT gains over base models shrank from +7.03 points at 2B to -0.91 at 27B.
NVIDIA announces local AI push at IFA 2026 with faster llama.cpp/vLLM inference, PAIR routing tool, and October RTX Spark PCs.
At IFA 2026, NVIDIA announced simplified local AI support for agents in Hermes Agent, OpenClaw, and Perplexity Portable Computer, plus new llama.cpp and vLLM optimizations delivering up to 1.9x faster local inference. NVIDIA also unveiled PAIR, a Personal AI Router for distributing inference across a local network's PCs, and compact RTX Spark Windows PCs from Lenovo and Acer arriving in October. The post recaps recent local-capable model releases including Nemotron 3.5 Lightning (30B), Qwen3.8-Flash-Next and Qwen3.8-27B, DeepSeek v4 Flash (284B MoE, 13B active), Meta Muse Glimmer (30B), Z.ai GLM-5.3-Flash, LTX 2.5, and MiniMax-H3 with the FastH3 distilled variant.
IFM released K2-Horizon-MoVA-36B-A4B, an open-weights 36B-parameter MoE model with 4B active parameters and native 512K context.
IFM released the final checkpoint of K2-Horizon-MoVA-36B-A4B, a sparse Mixture-of-Experts model using Mixture-of-Values (MoVA) attention with 36B total and 4B active parameters. The model supports native 524,288-token context and reportedly outscores open-weight dense and MoE models up to 15x its size on agentic and reasoning benchmarks, while competing against closed frontier models. Intermediate checkpoints, training data, the training recipe, and training code are slated for public release.