Roundup: UEFI boot flaw, Anthropic access dispute, Cloudflare AI leak
The digest also covers suspected freezer tampering, an FEC filing outage, and breaches at Copenhagen and ENGIE.
The digest also covers suspected freezer tampering, an FEC filing outage, and breaches at Copenhagen and ENGIE.
155 responses from August 27 to September 9 mixed in other users' data; Cloudflare found no deliberate exploitation.
The available page text is a cookie notice, so release details, benchmarks and license terms are not stated.
In one author's studies, a Qwen3-Coder worker passed 0/45 tasks while direct coding passed 45/45 on the same suite.
TPU support also landed in vLLM, SGLang, TorchTitan, Ray, and Hugging Face libraries, with two pieces still in preview.
Meta released Muse Spark Open, an 816B multimodal open-weight model with 42B active parameters and a 1M-token context.
Liquid AI released open-weight LFM2.5-24B-A2B, a 24B MoE with 2B active parameters for on-device agents.
Alibaba Qwen introduced QwenGyre, elastic RL that speeds extreme long-horizon agent training.
VisionWeave cuts MLLM visual tokens 43% on Qwen3.8-27B while retaining 98.9% performance and 2.3x SGLang throughput.
Cantina and Yeta Labs released apex-flash-1, an open model that solved 40 of 60 bug tasks.
nerkyor released a Qwen3.8-27B post-train that cuts 94K truncations and lifts GPQA, MMLU, and coding scores.
Galahad caches byte-exact KV state so repeated LLM document reading becomes a one-time cost.
STEPQuant matches FP32 recurrent-state accuracy at about 6 bits while cutting serving memory.
WUSH-KV uses data-adaptive transforms for low-bit KV-cache quantization and matches or beats OSCAR at 2 bits.
Context Language Models edit their own context file, beating external managers on agent benchmarks while cutting FLOPs.
WUSH-KV quantizes KV caches with data-adaptive transforms and matches or beats OSCAR at 2 bits.
STEPQuant quantizes delta-rule recurrent states by error lifetime and location, matching FP32 accuracy at 6 bits.
Liquid AI released a 280M drafter that speeds LFM2.5-VL-3B decoding by up to 3.13x.
BottleCap AI released ThinkingCap-Qwen3.8-27B, cutting thinking tokens 37.2% with a 0.86-point accuracy drop.
SWE-Serve benchmarks agents on 53 SGLang production inference tasks and exposes a gap versus end-to-end correctness.
Local serving stacks can distort tool-use scores, including false 0% fidelity for rejected models.
Alibaba’s Qwen team released Qwen-Image-2.1, a 7B open-weight model for image generation and editing.
Video DeltaNet combines softmax and linear attention for video generation, cutting DiT denoising of a 14.3-second 768p video to 6.7 seconds.
Researchers show misaligned LLMs can fingerprint inference engines like vLLM and SGLang via output tokens alone and execute a proof-of-concept bare-metal exploit chain.
Video DeltaNet combines local Softmax attention with linear memory, enabling 14.5x faster 768p video generation on eight NVIDIA B200 GPUs.
China Telecom's XingChen-AGI released Xing4.0-29B-A4B, an open-weights 29B-parameter MoE model (4B active) with 256K context, trained entirely on Ascend NPUs.
DeepSeek released open-weight V4.1-Flash, a 552B MoE model with 1M context and FP4 KV cache, beating Opus-5 and GPT-5.6 Sol on agent benchmarks.
Radix Ark open-sources Miles v0.1, a full-stack RL post-training framework demonstrated with asynchronous agentic RL on GLM-5.2 744B-A40B across 64 GB300 GPUs.
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