AI 日报

了解 GPT-6 如何通过提高缓存命中率、新增诊断功能、明确的断点以及降低延迟和成本的控制选项来改进提示缓存。

— OpenAI Blog

大家好！我开发 JevBench 是因为 Jev 太厉害了，而这个世界有必要知道那些严肃的开源项目和虚有其表的仿冒项目相比， 实际 表现究竟如何。 Jev 类模型返回的是有界选项和概率，而不是文本；与 LLM 相比，它们的速度更快、成本更低，同时在所处理的文本输入方面具备相近的智能水平。 JevBench 可以同时、以加权方式考察准确率、延迟和价格——你甚至可以自行配置权重。 一次完整运行会提出 534 个英文决策题。v1.3 得分综合了经机会校正的智能度、校准度、速度和成本。 当前排行榜： #1 - Jev 74.4 #2 - SemIf 73.1 #3 - djev 73.0 #4 - Winnow-12B Q8 71.2 #5 reflex 4B 70.3。 MIT 测试框架、公开题目、冻结的制品、评分代码以及公开的逐任务结果： https://github.com/fstandhartinger/jevbench 两个无需注册的演示： https://who-is-right.app.mintapis.com https://is-it-ai-slop.app.mintapis.com 局限性：仅支持英文；延迟数据来自一台德国服务器；本地/演示延迟会进行公开说明的 ×2 调整（在我的服务器上为额外增加 150 毫秒），这属于基于经验的假设；保留测试提示仍会发送到接受评估的服务；约 1 分的差距可能只是噪声。 你怎么看？ 评论地址： https://news.ycombinator.com/item?id=49800574 得分：44 评论数：7

— Hacker News 熱門

Hey HN! We’re Vincent and Jochen from Sitefire ( https://sitefire.ai ). We have been working together for years, with backgrounds in RL/optimization at Stanford and software engineering from Technical University Munich (TUM). With Sitefire (YC W26), we help marketing teams get recommended by AI Search (ChatGPT, Google AI Overviews, AI Mode, Claude, etc.). Our software monitors prompts, sees which web pages get cited, and uses these insights to help marketing teams take action, e.g. create YouTube videos or write the right blog posts. This means we have a commercial stake in AI-generated web content. And for now, high-information, AI-generated content works great to get cited and recommended in AI Search. But after talking to hundreds of marketing teams, it became clear that everyone despises AI-generated content (“AI slop”). And yet, everyone still wants to leverage AI to create content. So we asked ourselves: what characterizes AI slop? Can we train a model to identify it from human-generated web pages? Researchers from the University of Maryland and Google DeepMind already asked this question for fiction. Their paper StoryScope (Russell et al., 2026) showed that you can tell AI-written stories from human ones by their structure alone, without looking at the words. We ported their pipeline to commercial web pages. Using the Wayback Machine, we collected 2,250 blog posts from 268 B2B company websites that were written before ChatGPT existed. For each blog post, five AI models (GPT-5.4, Claude Sonnet 4.6, Gemini 3 Flash, DeepSeek V3.2, Kimi K2.5) wrote their own version. Instead of looking at the words, we looked at how each post is built. We had an AI model answer 214 questions about every post, e.g. how hard it pushes its own product, whether it backs up its claims with sources, or whether it quotes a named expert. Then we trained a classifier on these answers. On blog posts it had never seen before, our classifier told AI-generated and human posts apart with 98% accuracy, getting only 19 of 1,740 wrong. Why does it work so well? Because all five AI models write in a similar shape. Mapping every AI model’s values for these features, we see they cluster together, while the human values sit apart and spread out much more. Of the 1% most unique blog posts in our data set, 149 are human, only 4 are AI. So what characterizes AI slop? It tells you the same thing three times. The title already promises what you'll get ("How to Cut Onboarding Time in Half"), the intro lays out what's coming, and the ending says it all again. 77% of the AI posts end by repeating their main point, compared to only 12% of the human posts. We call it the tidy, self-announcing blog post. Still, each AI model has its own accent. We trained a second classifier to tell which of the five AI models wrote a post, or whether a human did. It picks the right author 79% of the time, where random guessing (1 in 6) would get 17%. Almost all of its mistakes are mix-ups between the AI models, not between human and AI. The cool thing about structural features is that you can't simply reword your way out of it. We had each AI model rewrite its own posts until, on average, 73% of their original 13-word sequences were gone, and the AI slop classifier still worked just as well. We're building this into Sitefire: our agents get a structural understanding of text, so the posts they write go deeper and vary the way human writing does. There's a lot we haven't tested yet, like the myriad of humanizer tools, human rewriting, restructuring a post, or prompting an AI model to explicitly avoid these habits. And our human posts are mostly from 2020 to 2022, while the AI posts were generated in August 2026. Structure can't really tell when a human post was written, but it's still not a same-year comparison. We published the study with all the figures on arXiv: https://arxiv.org/abs/2609.15369 . The code is on GitHub: https://github.com/pulse-energy-eu/slopshape We're pretty sure your own blog isn't AI slop, is it? We built a checker that runs one of your posts through the ten features from the paper, so you can see for yourself (the full report asks for a work email): https://sitefire.ai/slop-checker . Think you can tell AI slop from human writing? We also made a little game to see if you can keep up with our model, which gets all five rounds right: https://sitefire.ai/spot-the-slop . Comments URL: https://news.ycombinator.com/item?id=49800566 Points: 5 # Comments: 0

— Hacker News 熱門

OpenAI 概述了对前沿模型及安全保障措施开展严格、安全且独立的第三方 AI 安全评估的优先事项和原则。

— OpenAI Blog

文章网址： https://openai.com/index/introducing-gpt-6-sol-and-luna/ 评论网址： https://news.ycombinator.com/item?id=49805509 积分：25 评论数：3

— Hacker News 熱門

GPT‑6 Astra 使 Parallel 的智能体能够以一半的时间和一半的成本研究并综合劳动力市场数据，相较于此前的模型。

— OpenAI Blog

Hey HN, Guanming here, cofounder of General Instinct. We just released InstinctFlash, a high-performance serving framework for robotics models. It’s licensed under AGPL-3.0. On Jetson Thor, we see speedups about 1.2x to 7.9x from runtime optimizations alone and up to 33.78x for LingBot-VA when we combine those runtime optimizations with a distilled few-step diffusion scheduler, going from the original 25 visual / 50 action steps to 2 / 4 steps. Across 50 Robotwin2.0 tasks, we evaluated 1,153 episodes per configuration, LingBot-VA with InstinctFlash at 2 visual / 4 action steps achieved a 90.5% success rate, compared with 92.1% for the baseline at 25 visual / 50 action steps. Here’s an optimized 5B world action model, running in real time on a Jetson Thor: https://youtu.be/nku65iyL5Fw InstinctFlash currently supports 8 VLA / world-action model families, including pi0.5 and NVIDIA Cosmos Policy, across RTX 4090 / 5090 and Jetson Thor. Just give it your fine-tuned checkpoint and InstinctFlash handles the rest, exposing the accelerated model through a Python runtime or an OpenPI-compatible WebSocket server. We started working on this because we kept running into the same problem while deploying robot policies, the models were getting much better, but inference was often way too slow for the control loop we actually wanted. For pi0.5, mixed-precision GEMMs and CUDA graphs speed up computation and reduce launch overhead. For Cosmos, caching avoids redundant computation across diffusion steps. World-action models’ diffusion denoising step depends on the previous one which motivated our work on few-step distillation. Right now, InstinctFlash contains 6 aspects of optimization. - Graph: CUDA graph capture, memory planning and separating prefill from repeated execution. - Cache: Reusing KV and conditioning state across diffusion steps and prediction calls. - Attention: Specialized attention paths for different model architectures. - Kernels: Fused operations and kernels tailored to specific backends and tensor layouts. - Precision: FP8 and mixed-precision execution. - Model: Few-step distillation for diffusion and action generation. Teams at Samsung, Siemens, and other robotics startups have used InstinctFlash for model acceleration on VLAs, WAMs, and diffusion-based world models. Now we are opening up access to you. Feel free to try it here: https://github.com/General-Instinct/InstinctFlash More implementation details and benchmarks: https://general-instinct.com/blog/instinctflash-edge-inferen... Would love to hear your feedback! Comments URL: https://news.ycombinator.com/item?id=49802789 Points: 4 # Comments: 0

— Hacker News 熱門

大家好，我构建了 ai·rete·rag，因为我不断看到一些团队让 LLM 负责那些需要可审计的决策（贷款、欺诈、临床分诊），然后事后再加上“护栏”。 这个项目改为让两者串联运行： 1. 纯 Python 实现的 Rete 引擎根据事实评估 YAML 规则。判定结果只来自这里。相同的事实，每次都得到相同的判定，并通过基于显著性的冲突解决机制处理冲突。 2. RAG 从你自己的政策文档中检索段落，然后由 LLM 撰写通俗易懂的决策解释，引用这些段落。它无法改变判定结果。 有几件事的发展超出了我的预期： - 规则是一个图，而不是扁平列表：支持嵌套的 all/any/not，并且规则可以断言供其他规则使用的事实（前向链接）。决策追踪会展示因果链。 - 审计模式会记录评估过的每一条规则，包括那些未触发的规则，逐条件记录，并保存规则集快照以便重放。 - 规则可以引导检索（触发的规则会缩小要搜索的文档范围），检索到的文本也可以转换为供引擎使用的事实。 - 非技术人员可以在可视化编辑器中构建规则，也可以粘贴政策文档，让 LLM 起草带引用的规则。未经审核，草稿绝不会被保存。工程师仍然可以使用 YAML。 落地页提供无需注册的在线演示（涵盖 8 个演示领域：贷款、欺诈、临床、保险、法律、运营、电子商务、区块链）。此外还有一个 MCP 服务器，因此 Claude 和其他代理可以将 /decide 作为工具调用：`uvx ai-rete-rag-mcp`。 坦率地说：这是一个提供免费套餐的托管产品。MCP 客户端采用 MIT 许可证并已开源（github.com/zaharajabeen13-create/ai-rete-rag-mcp）；引擎和平台目前并未开源。 我尤其想听听曾经需要向监管机构或审计人员解释自动化决策的人分享经验：他们实际要求了什么？ 评论链接： https://news.ycombinator.com/item?id=49803683 得分：6 评论数：0

— Hacker News 熱門