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OpenCode has launched a mysterious stealth model, Pulse Alpha, offering 1M context, 131K max output, multimodal input, and three reasoning tiers — free for a limited time. Speculation points to GLM 5.5 or the Qwen series under the hood.
The paper proposes Latent Sparse Attention (LSA), which hierarchically clusters Keys in latent space so that Queries only need to score against cluster centers. This achieves a 72.4x speedup in Prefill attention and a 42.5x speedup in Decode for a single attention layer of Qwen3.8-27B under 10M ultra-long context, reduces end-to-end first-token latency to about 1/9, and keeps accuracy essentially unchanged. The method includes two variants: distillable D-LSA and natively trained N-LSA, with code and distilled weights open-sourced.
The team shares Latent Sparse Attention (LSA), an open research release enabling subquadratic attention that matches full-attention accuracy at ultra-long context, making sparse attention up to 22.7× faster and full attention up to 72.4× faster.
Moonshot AI announced Kimi K3.1 Pro, its most capable 3T-class open-weight MoE model with 1M-token context, and Modal will offer day 0 support for it this week.
Unsloth published benchmark results for its DeepSeek Dynamic V3.1 GGUF quants, claiming >10–20% gains over GSQ-RCO on agentic benchmarks like Terminal Bench and DeepSWE at 512K context, and statistically disputing GSQ-RCO's quantization recovery claims.
LongCat-2.5 is introduced as a natively multimodal model designed for agentic tasks, combining vision-language capabilities with tool-use and long-horizon task performance.
The author tested three local open-source models — DeepSeek-V4.1-Flash-1017, Qwen-3.8-Flash-Next, and Qwen-3.8-27b — on an RTX 5090, an RTX 4090, and dual V100s, comparing the real-world experience gap against cloud models like GPT-5.5 and Opus-4.8 through a Core-300 blind test and 200K+ long-context stress tests.
A non-developer from Rome shares Briglia, an open-source AI agent alternative to Hermes that uses a curated chronological conversation history instead of retrieval-based memory, reporting 43/48 fact recall on a month-long benchmark versus Hermes+Hindsight's 39 and Codex's 6.
A Zyphra Research paper introduces Zyphra Hierarchical Attention (ZHA), a subquadratic sparse attention method using a hierarchical tree-structured indexer over the KV cache, which can be adapted to pretrained models via short KL distillation and scales dramatically better beyond 1M context.
论文提出 GraphNIAH，一个基于真实引文关系自动构建的长上下文多跳推理基准，用于评估 GPT-5.6、Gemini-3、Qwen3.5 等八个大模型，发现随着推理路径加深，模型准确率显著下降甚至接近随机猜测。
A research paper formulating long-context diffusion costs as dual budgets—attention graph edges and activated experts—presenting the DualBudget sparse-expert configuration with graph/compute bounds and evaluations showing gains through 16K context and a 20% cost reduction over DiffuSeq.
A developer shares open-source vLLM patches and Triton kernels that run 8 coding agents with 250K-token contexts concurrently on two RTX 3090s by keeping the full int4 KV cache in system RAM and fetching only ~1% of tokens per attention step via a rank-64 key sketch, reporting ~545-790 tok/s total and 31/32 recall at 250K.
The author tested Liquid AI's LFM3-1.7B model (Q8_0 GGUF via llama.cpp/ROCm) for compression and summarization, reporting strong prefill/decode throughput on Strix Halo and tensor-split 7900 XTX+XT setups, plus unexpectedly coherent performance at 32k-64k context lengths for a 1.7B model.
A practitioner compares Qwen3 27B running INT8 vs INT4 AWQ over two weeks of heavy agent logs, finding INT4 dramatically increases reasoning-loop rates (1.9 vs 4.7 loops/1k calls), cuts task success from 92% to 78%, and the gap widens at long context above 100k tokens. INT8 and even BF16 also loop, so quantization amplifies rather than causes the issue.
A community discussion asking for real-world user experiences with Nanbeige4.3-33B, a 33B parameter open model featuring a 512K context window and strong benchmark results in reasoning, coding, and agentic tasks, despite concerns about high KV cache memory usage.
SenseNova releases SenseNova-Loop-9B-Preview, a looped multimodal model built on a shared looped block and mHC residual stream architecture, targeting long-context and agentic workflows with capabilities spanning motion design, PPT generation, and data analysis.
DAMI 提出一种双轴记忆初始化方法，通过将 A_log 按几何级数分布于各头、dt_bias 沿深度呈低-高-低剖面，在 GDN、KDA、GDN2、Mamba-2、RWKV-7 五种线性注意力架构上将 RULER 检索准确率相对随机初始化提升 5.29–16.71，同时不引入额外参数或改变推理开销。
Mistral 发布 Mistral Large 4（Le Chonk）：一个 1 万亿参数、49B 激活参数的原生多模态 Mixture-of-Experts 模型，具备 1M 上下文窗口、可配置推理力度、160+ 语言支持，并以 Mistral Ecosystem Open-Weight License 开源权重发布。
Hugging Face 的博客介绍 Mistral Large 4（代号 Le Chonk），这是 Mistral 推出的多模态、多语言、混合专家（MoE）模型，具备约 1T 总参数（49B 激活参数）和 1M token 上下文窗口，在网络安全、编码代理和多模态任务上表现出色。
Google unveils Gemini 4 Carbon just two weeks after Argon, jumping to #1 on the Artificial Analysis Index (59) ahead of Opus 5.5 and GPT-6 Astra, with a major long-context leap of 96.2% on GraphWalks at 1M tokens while still lagging in coding benchmarks.