OpenAI is standing firm on its decision to fire three safety researchers after an investigation found they committed "a significant breach of trust." In a post on X on Friday, the company said Jasmine Wang, Tomek Korbak and Mikita Balesni were dismissed for violating "clear policies on handling sensitive information." It insisted the decision was […]
Johns Hopkins University 的天体物理学家 Brice Ménard 使用 Anthropic's Claude Science 首次绘制了整个天空的紫外线图。AI 智能体从多个太空任务中下载数据，对其进行校准，并使用图像修补技术填补空白。预测结果与实际测量值的平均偏差约为百分之十。Ménard 认为，这个项目体现了一类如果没有 AI 就根本无法完成的研究。文章《Anthropic's Claude Science creates the first complete ultraviolet map of the sky》最先发表于 The Decoder。
Ever since people first seriously contemplated giving machines an intelligence modeled on our own, there has never been any question that they would, like us, be able to say no. The sci-fi canon is full of stories of robotic disobedience. Most of these capers are, of course, cautionary. But recently, the idea that AI shouldn’t…
Google Cloud has introduced the Google Cloud Gemini agent, a single agent for enterprise work. The Gemini agent is a cloud-hosted agent from Google Cloud that answers questions, does knowledge work, creates media, and writes and runs code. It does all of this from 1 prompt box and 1 API. For developers, the agent is […] The post Google Cloud Launches Gemini Agent, One Universal Agent for Enterprise Work appeared first on MarkTechPost .
Underdog Saluki 27B is a 7.89 GB, 2-bit GGUF of Qwen3.8-27B under Apache 2.0. It beats the 54 GB original on tool calling but gives up ground on competition math and reasoning. The post Meet the Underdog Saluki 27B: A 2-bit Qwen3.8-27B That Beats the Original at Tool Calling appeared first on MarkTechPost .
了解 Sophos 如何利用 OpenAI 的 Daybreak 将网络威胁调查时间缩短 96%，并在保留人工监督的同时，实现 52% 的 MDR 案件自动化。
OpenAI揭露了一项俄罗斯影响力行动和一项伊朗影响力行动，并封禁了涉事账号。俄罗斯的“Dark Clark”行动在拉丁美洲各地传播虚假信息，引发政界人士回应，并在OpenAI的报告历史中首次获得6级中的5级评级。伊朗的“Bogus Bylines”行动利用7名虚假记者，在全球各地的网络媒体刊登了近100篇文章。文章《OpenAI揭露了俄罗斯和伊朗在真实新闻媒体中植入虚假报道的影响力行动》最初发表于The Decoder。
I'm a writer who was pondering the problems with getting my work discovered and realized that current search/recommendation engines don't go very deep. So I thought about ways to address this. I was obviously mostly interested in books, so I worked on that first. I'd noticed LLMs are capable of pretty solid textual anaylsis these days, so I wondered if you could use that to generate good recommendations. So I sat down with an LLM- ChatGPT, Luna, for the curious- and spent about two hours explaining, in datail, my literary tastes- going into specifics like what sort of plot structure, characters, and humor I like, not just genre/specific titles. The experiment demonstrated surprisingly strong results (though admittedly with a very small sample size)- the five recommendations I asked for were all ones that I hadn't heard of, as well as books that I would actually read. Further, I provided the LLM with a list of several books I had read and asked it to guess whether I would have liked them and why. The LLM successfully did so. Just proof of concept, but intriguing. The problem with this, of course, is that most people wouldn't sit down with an LLM for an extended period of time to discuss literature. So I decided to build a small prototype of an app that might be able to do that. The prototype is very simple- first, there are two types of information it saves- a persistent preference profile for the user, and user conversations. Then I built conversational functions to call an LLM to have conversations with the user to extract preference information. The interesting thing to me is that this isn't just using an LLM to make recommendations- it's using one to analyze conversations over time, develop a persistent, evolving preference model for a specific user and then use that model to guide an LLM on a search for recommendations. A few things occurred to me. First, the conversations and user profile DO NOT need to live on a central server- they can be stored locally by the user (or in a cloud of their choice). Second, it seems like the user data could potentially be minimized and pseudonymized before being sent to the LLM, though I lack the technical expertise to know what is realistic here. Third, if the LLM is conducting any searches, this would seem to make it possible to provide search services with less information about the user and their preferences (I'm not positive about this, so please do correct me if I'm wrong). Finally, books are just one of the things you could use this for- it could technically be done for just about anything, at least in theory. I experimented with this process several times, and received encouraging results. What I don't know is- Is this architecture novel? Does it work on things other than books? Am I an outlier in how well I articulate my preferences? Am I wrong about the potential benefits to privacy? Would people use this? If the search results are as qualitatively higher for others as it was for me, I think they probably would, but I just don't know. The reason I'm posting this is because I am a writer, not a software engineer. I don't really have a strong interest in developing this and could bring very little to the table on the technical side. But it seems like a neat use of the more advanced LLMs we have these days. I don't really know if the idea is novel (not in each part- every bit of the pieces has been done before- but as a whole). I'm in the process of documenting the prototype and experiments- I'd be curious to hear if I am completely barking up the wrong tree or if this could actually be a cool new way to conduct searches. I'm not posting this as a business proposition- it's just not my jam. If anyone wants to take the idea and run with it, I'd love to see what you do with it. Comments URL: https://news.ycombinator.com/item?id=50017172 Points: 1 # Comments: 1
OldRoll Web brings vintage camera effects and film-inspired filters to your browser. Give everyday photos a nostalgic look without installing an app, with all creative assets free to use. Comments URL: https://news.ycombinator.com/item?id=50016840 Points: 3 # Comments: 1
Explore a comprehensive coding guide to Google Research's RRSI (Regularized Recursive Self-Improvement), detailing how noise bands, cost rules, and leakage screens enable safe, efficient, and self-improving AI agents. The post Google Research RRSI Guide: Mastering Self-Improving AI Agents appeared first on MarkTechPost .
I have been an opensource terminal tool for understanding AI-generated code. A git-diff shows what changed, but the context behind it lives somewhere within the agent's session context, and often goes missing in long running agent execution. Wy helps reduce that understanding gap, and helps engineers build judgement. This is especially useful for unfamiliar codebases, where agents are the primary interface for the developers. It is meant to be used alongside the With wy, you can: - Browse changes, and relevant agent notes in a terminal UI - Request explanation and ask follow up questions and changes - Open code references and citations - Distinguish recorded justifications agents derived to get a better understanding of the changes. If you try, and find it useful consider supporting it with a github star or bug report or contribution. Comments URL: https://news.ycombinator.com/item?id=50016298 Points: 1 # Comments: 0
Release: ttok 1.0 I released ttok 0.4 , ran uv tool upgrade ttok , piped a file into the new version... and realized that it was defaulting to the GPT-4 tokenizer when it should very clearly default to GPT-5/GPT-6 instead! I figured switching the default was a reasonable excuse to finally ship a 1.0. OpenAI haven't actually confirmed that GPT-6 uses the same tokenizer as the GPT-5 family yet - there's an angry issue about it - but I found this commit by William Liu which reports on an experiment he ran confirming that the tokenizers are likely the same: All seven GPT models (5.5, 5.6 Sol/Terra/Luna, 6 Astra/Sol/Luna) report 44,794 tokens and match each other on every one of the 31 fixtures. GPT-6 introduces no input-count change on this corpus. Tags: projects , ai , openai , generative-ai , llms , tokenization
arXiv:2610.06513v2 公告类型：replace-cross 摘要：评估文本生成需要衡量生成分布与数据分布的匹配程度。对于自回归模型，这通过困惑度实现。扩散语言模型和基于流的语言模型只能提供似然下界，而不同模型系列的下界紧致性有所不同。带熵的生成困惑度等基于样本的替代方法没有考虑分布拟合程度。我们提出了 SOL，一种用于衡量文本分布之间距离的方法。每个序列都表示为固定 Transformer 下其隐藏状态的经验测度，并通过双切片 Wasserstein 距离比较这些测度的分布。我们证明，当 Transformer 是单射时，SOL 是一种度量。实验表明，SOL 能够检测分布失效，重现预期的模型趋势，并提供稳定的基于样本的估计。我们提出 SOL，旨在弥补当前用于非自回归模型的评估流程中的空缺。作为第一步，我们使用 SOL 重新评估了在 OpenWebText 上训练的多种模型。
arXiv:2610.03827v2 公告类型：替换交叉版本 摘要：相似度分数常被用作证据，证明某个模型与大脑或不同语言之间共享结构。我们在两种情境下追问：当这种结构被移除，或测量它的工具无法正常工作时，这样的分数究竟反映了什么。在十七种语言中，语法性探针在语言距离越远的语言之间迁移效果越差（r = -0.66）。但探针自身的准确率也沿着同一轴下降，并且在四种语言中处于随机水平，而这四种语言恰好是距离最远的五种语言中的四种。剔除这些语言后，可解释方差减半；由于语言距离的取值范围也随之缩小，该设计无法说明这一梯度效应中有多少来自测量工具本身。将272个语言对视为相互独立的样本，会得到引导效应的 p = 0.0006；而以十七种语言为统计单位时，该效应为零（p = 0.155）。在脑对齐方面，一项训练目标使语言模型与 fMRI 反应的相似度从0.10提高到0.34（可靠性上限为0.54），然而，即使破坏了目标中的脑对应关系，模型仍能达到0.31。对于包含 n 个句子的真实目标，经过打乱的目标得分约为 k/n，其中 k 为其排名；这一基线可在训练任何模型之前计算出来。两种干预都有效，但我们没有检测到随距离变化的引导效应，也没有发现对齐带来的句法收益。在解读对应性分数之前，先测量在没有对应关系的情况下，该分数有多少仍能保留下来。