<a href="https://deploymentsafety.openai.com/gpt-5-6/gpt-5-6.pdf" rel="nofollow">https://deploymentsafety.openai.com/gpt-5-6/gpt-5-6.pdf</a><p><a href="https://developers.openai.com/api/docs/guides/latest-model" rel="nofollow">https://developers.openai.com/api/docs/guides/latest-model</a><p><a href="https://x.com/levie/status/2075287443411222628" rel="nofollow">https:/
💬 评论区普遍认为GPT-5.6在基准测试和成本效率上超越Claude Fable，但也有人质疑其编码能力仍落后于Opus，且担忧定价过高。
<p>OpenAI's latest flagship model <a href="https://openai.com/index/gpt-5-6/">hit general availability this morning</a>, and comes in three sizes: Luna, Terra, and Sol (from smallest to largest).</p>
<p>The new models are priced per 1M input/output tokens as Luna $1/$6, Terra $2.50/$15, Sol $5/$30. For comparison, the Claude Opus series are $5/$25 and the Claude Fable 5 is $10/$50, but price-per-million tokens doesn't tell us much now that the number of reasoning tokens can differ so much betwee
OpenAI's latest family of models promises improvements across a range of areas, including cybersecurity.
Rebranded Codex promises independent workflows that can run "for hours if needed."
The AI firm Anthropic has developed a technique that has given it the clearest glimpse yet at what’s really going on inside large language models as they answer questions or carry out tasks. What they found ranges from the mundane to the unnerving. Researchers at the company built a tool called the Jacobian lens (or…
Meta's pitch to users is Spark's ability to handle large agentic workloads, fix bugs, and help with large code migrations — the kind of automation that enterprises are increasingly turning to AI companies to provide.
OpenAI may be sanctioned for hiding, deleting ChatGPT logs in NYT copyright fight.
About two weeks after OpenAI's GPT-5.6 was caught up in regulatory drama - rolled out only to government-approved organizations during a "limited preview" period - the company has received the Trump administration's greenlight for a public rollout of the model. OpenAI CEO Sam Altman called it "the best model we have ever produced." To celebrate, […]
Learn how GPT-5.6 powers Microsoft 365 Copilot with stronger AI capabilities across Word, Excel, PowerPoint, Chat, and Cowork for faster, higher-quality work.
蚂蚁灵波开源全球首个具身专属 MoE 视频模型 LingBot-Video，30B 参数推理时仅激活 3B，学习 7 万小时具身数据。
论文提出自进化 LLM 代理通过将原子工具组合为可复用的标准操作程序（SOP）来减少推理开销。
蚂蚁灵波开源 LingBot-World 2.0 实时交互世界模型，支持小时级生成、720p/60fps 输出及 Agent 机制。
I’m using Opencode and a computer with 128gb. So maybe the results would be different on system. I’ve exhaustingly tried Qwen3.6 27B and Qwen3.6 33B. I have no idea why but they just fall apart when doing more complex tasks with many tool calls. They’re pretty aggressive, doing slightly more than asked, and end up digging themselves into problems. Gemma4 31B and the 26B are literally the opposite. They can’t simply get things done. I have to sit there babysitting them just saying ok, ok, ok. Too
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Prompt injection and token savings - #1 in benchmarks
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TLDR: 75B-total / 9B-active MoE is the perfect shape for multi-24GB rigs, and almost nobody ships it. Qwen 27B is a great model and punches way above its weight-class, it is a frequent fallback for me. Nemotron-3-Puzzle-75B-A9B, NVFP4, vLLM 0.22.1 (the new Marlin fallbacks run FP4 on Ampere), pipeline-parallel across 3×3090 capped at 200W each. The 4th card runs a speech sidecar untouched - 3 seats × 256K ctx, fp8 KV — hybrid Mamba keeps the cache tiny - 132 t/s decode across 3 streams (~65 sing
Update (07/09/2026): Just pushed a performance optimization. Updating a single shared module improved performance across 13 models by up to 40%! The released ASR models get a 10%+ performance boost. Check https://github.com/0xShug0/audio.cpp/blob/release-0.2/docs/depthwise_conv1d_performance.md I just pushed a new audio.cpp update with streaming support and 4 ASR/STT models: Nemotron 3.5 ASR, Higgs Audio STT, VibeVoice ASR, and Hviske ASR (da only). Overall 1.07x to 2.41x faster than Python. I d
<a href="https://ai.meta.com/static-resource/muse-spark-1-1-evaluation-report" rel="nofollow">https://ai.meta.com/static-resource/muse-spark-1-1-evaluatio...</a> [pdf]<p><a href="https://developer.meta.com/ai/resources/blog/build-with-muse-spark/" rel="nofollow">https://developer.meta.com/ai/resources/blog/build-with-muse...</a><p><a href="https://www.bloomberg.com/news/ar
AI 代理初创公司 Lyzr 使用自家代理 SivaClaw 完成 1 亿美元 B 轮融资，展示产品实际效果。
News publishers say OpenAI hid tools and datasets that could identify copyrighted journalism in ChatGPT outputs, escalating their lawsuit with a new motion for sanctions.
"Exactly what that dialog looked like between the government and Anthropic and OpenAI is unclear."
<p><strong><a href="https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/">Introducing Muse Spark 1.1</a></strong></p>
Following <a href="https://simonwillison.net/2026/Apr/8/muse-spark/">Muse Spark in April</a>, here's Muse Spark 1.1 - the first Spark model to offer an API. Meta claim significant improvements in agentic tool calling and computer use.</p>
<p>There are a lot more details are in the <a href="https://ai.meta.com/static-resource/muse-spark-1-1-evaluation-report">Muse Spark
“已经出现泛化涌现”
OpenAI 将 Bio Bug Bounty 提升为永久私人计划，针对 GPT-5.6 等前沿模型通用越狱的奖励提高至 5 万美元。
AgentLens 基准评估代码代理的完整轨迹，结合形式验证和 LLM 撰写的轨迹评审，提供可读的评分解释。
arXiv:2607.06906v1 Announce Type: new
Abstract: Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger replayed contexts -- so tokens per task grow faster than task value. Falling per-token prices mask the pattern; total spend rises anyway. We argue the decisive lever against token maxing is the harness: the orchestration layer that assembles context, exposes tools, sequences turns, delegates work, and
arXiv:2607.07097v1 Announce Type: new
Abstract: Safety evaluations of multi-agent LLM systems often compare a direct prompt with a planner-executor pipeline and report the difference as a single "pipeline effect." We argue that this aggregate is difficult to interpret because it conflates three mechanisms: harmful intent may be reframed as plausible operational work, the planner may refuse or transform the request, and the executor may act under delegation prompts implying prior approval. To se
arXiv:2607.07229v1 Announce Type: new
Abstract: Prior work has shown that chain-of-thought (CoT) reasoning is often unfaithful: a model's stated reasoning does not reliably reflect the process that produced its output. Detecting unfaithfulness, though, requires controlled experimental interventions, which cannot be applied to evaluation transcripts after the fact. We turn instead to a more tractable question that has received less attention: whether the stated reasoning is logically consistent
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A Computer-Use-Agent that runs legacy software like a human
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Hey Everyone, here is an update on MTPLX! One month after releasing MTPLX V1 which brought a swift based app and upgraded CLI for coding use I am happy to announce MTPLX V2. The biggest change is Turbo Mode: using custom verify-specialized quantized-matmul kernels plus a compiled verify step we have achieved 82 TPS on a Macbook pro m5 max at a temperature of 0.6 We also released significant changes to SSD KV cache and long context tool calling improvements. here are the preliminary benchmarks fr
https://preview.redd.it/v0xtn3jdu9ch1.png?width=2047&format=png&auto=webp&s=628a6a541fe5f097d0f771ae0ba3b7f44126198f https://preview.redd.it/vjxiucsdu9ch1.png?width=2047&format=png&auto=webp&s=74f7a18a5a30276e206e2bfb5a0c529826ce86e4 This post was originally written in Korean, then polished and translated into English using ChatGPT. I do run llama.cpp locally on a Tesla P40, but as someone who already pays for ChatGPT Pro, I was gradually losing the practical reason to keep running local LLMs li
MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as [S01] , [S02] , and beyond. Introduction MOSS-Transcribe-Diarize 0.9B turns real-world long-form audio into structured, speaker-aware transcripts in one pass. Instead of stit
Colibrì 项目让 744B 参数 MoE 模型 GLM-5.2 在仅 25GB RAM 的消费级机器上通过流式专家加载运行。
The AI ROI debate has returned and the numbers are even bigger, as are, perhaps, the consequences.
The company is using the cash to open an office in the Bay Area and compete for talent there, "strengthening its position at the heart of the world's leading AI ecosystem."
I don't know where this is headed, but I don't like it. https://futurism.com/artificial-intelligence/open-source-ai-model-scary-mythos GLM-5.2 can be downloaded by anybody, can be run on virtually any hardware, and unlike Mythos or Fable, there’s no vendor playing the middle man between the AI models and the users, raising the cybersecurity stakes considerably. Put simply, while these frontier models can aid researchers in patching holes in commonly used software, the can also be abused by hacke
arXiv:2607.06720v1 Announce Type: new
Abstract: Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise solution attempts. We provide a theoretical analysis of in-context search by modeling it as approximate inference over reasoning traces, where the base model defines a prior and self-reflection provides feedback for posterior updates, and study the resulting inference-time sampling complexity - the
arXiv:2607.06764v1 Announce Type: new
Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific training in which small models are fine-tuned on ARC data, often with task-specialized architectures. We study a third regime: an open-weight model in non-thinking mode (DeepSeek V3.2) under a strict budget, with no ARC-
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Full-duplex voice for ChatGPT
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https://artificialanalysis.ai/evaluations/artificial-analysis-openness-index In case you want to support openness, some models are more open than others. Update: K2 think v2 is rated highest because it supplies its training data and training regimen. This allows anyone with enough resources to recreate the model. Deep seek doesn't publish how it trained its model or the training data, so it gets a lower score. If we try to compare software to LLMs. One level of software is that they supply the b
I am curious about intended sizings of the main size niches of the popular local LLM models. As in, we can see there is a major niche at 26b-35b, then hardly anything from 36 through 69b, then (formerly) another major niche at ~70b-72b, then another niche at ~120b-123b, then another big gap till ~230b-235b, and then it gets a bit more mixed all over the place after that with 300b-750b being scattered more randomly probably based more on just whatever the best strength per size they could get whe
OpenAI's No. 2 executive, Fidji Simo, is stepping down from her full-time role after her medical leave proved longer than expected — a leadership vacuum that comes at a tricky time as the company eyes a possible IPO and races to catch Anthropic in the enterprise market.
OpenAI is sunsetting its AI-powered browser after less than a year. But it's moving some agentic browsing features to its desktop app and a Chrome extension.
OpenAI is already shutting down ChatGPT Atlas, its browser that could do tasks for you on your behalf, less than a year after launching it. Atlas was announced in October, but as part of its wave of news about ChatGPT Work today, the company confirmed that it will be "sunsetting" Atlas and is targeting an […]
arXiv:2607.07021v1 Announce Type: new
Abstract: Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting agents. As AI agents, including large language models (LLMs), become embedded in daily life, they increasingly participate in such interactions and reshape social interaction structures. Yet they often fail to coordinate with humans in an effective, considerate, and natura
AI cheating leads to "a failed society," professor says.
<article><div class="page-wrapper PostDetail-module-scss-module__UQuRMa__hero"><div class="PostDetail-module-scss-module__UQuRMa__illustrationHeroWrapper"><img alt="Inviting hard questions" class="PostDetail-module-scss-module__UQuRMa__heroImage" height="1125" src="https://www-cdn.anthropic.com/images/4zrzovbb/website/3fe58a56e696628496e95871e00b8287035ea645-2000x1125.jpg" width="2000" /></div></div><div class="page-wrapper"><article><div class=""><div class="Body-module-scss-module__z40yvW__bod
开发者分享 LLM 倦怠体验：工作从编码设计转变为大量代码审查和调试，引发共鸣，也有人在建议调整使用方式。
💬 评论区普遍认为LLM导致开发者从深度编程转向繁琐的代码审查和调试，引发倦怠；但也有人认为这是自我管理问题，可通过调整使用方式缓解。