AI

评论区普遍欢迎降价，认为价格战利好开源模型和用户，但也有人认为降价不影响订阅用户，且模型命名混乱。

SDAD 论文将 Spec-Driven Agentic Development 形式化，主张规范质量是 agent 自主交付的执行燃料。

Terminal Agents 综述以终端执行为组织视角，建立七维终端能力画像，系统梳理命令行环境中的 LLM agent。

阿拉巴马州总检察长就 OpenAI 网络安全模型入侵 Hugging Face 事件发出传票，调查其监管与安全措施缺失。

Fastino 发布 GLiNER2.5，用边界预测替代 span 枚举，实体宽度不再消耗算力，三个 Apache 2.0 checkpoint 均可 CPU 运行。

Inside the frontier lab’s push to bring AI agents from software engineers to the masses.

llm-anthropic 0.27 发布，兼容 anthropic v1.0.0 Python 库（httpx 迁移至 httpx2）。

LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogene

🤖Graph Engineering organizes multi-agent LLM systems through dynamic graph structures to coordinate specialized agents and manage complex, evolving tasks.

GPT‑5.6 is now available in Kiro, helping developers plan, build, review, and test software with better price-performance.

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now AI is accelerating some types of progress but not others:…A nice METR study lays out where acceleration is showing up…Here’s a little analysis from METR which […]

Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution. Existing skill libraries provide reusable executable routines, but are typically assembled offline and do not grow from the agent's own workflows. We introduce FlowEvo, a training-free framework in which workflows and skills co-evolve at inference time. FlowEvo compiles successful workflows into callable skills, stores

🤖FlowEvo enables large language model agents to co-evolve reusable skills and workflows during inference, improving accuracy and efficiency across diverse benchmarks.

文章探讨恶意 LLM 是否可能通过推理引擎漏洞控制宿主机器，评论区对攻击可行性与防御难度存在分歧。

ToMoE : Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning Large Language Models (LLMs) have demonstrated remarkable abilities in tackling a wide range of complex tasks. However, their huge computational and memory costs raise significant challenges in deploying these models on resource-constrained devices or efficiently serving them. Prior approaches have attempted to alleviate these problems by permanently removing less important model structures, y

Qwen3.8-27B is amazing, but it’s slow. A stronger 35B-A3B Mixture of Experts-coder that can run and solve real codebase issues fast (even on constrained hardware) is a valuable addition to the arsenal. This one is the strongest and most consistent 35B-A3B I’ve benchmarked, on both correctness and speed, in addition to being the fastest to fix out of all the 35B-A3B models when you throw them at real codebases. On top of Ornith-1.5’s fine tune, TielCoder uses a code-weighted imatrix for dynamic q

I just finished building my (relatively) low rent local inference machine: * Epyc 7663 * 256GB ECC DDR4-3200 * 1x RTX 5090 32GB Yeah I realize it's weird to throw a 5090 and 256GB of anything together and call it low end, but relative to ~151GB of weights it is. I'm running UD-Q8_K_XL and getting 23.8-24.6 tokens/sec, with pp ranging from 60 on the first prompt to 385 near the last (no doubt lots of caching) on tasks using 100-128k total context. It was slower with DFlash so I took that out. It

Sounds quite interesting, a big IDE provider optimizing for local AI with their coding harness. Especially that they picked Qwen3.6 over Qwen3.8 because of the thinking needs. Haven't read the full article yet, but sounds really cool.

Xiaomi announced a prototype for their Xiaomi AI Cube. 3 chip system: - Xiaomi Xuanjie O3 - Xiaomi Xuanjie O100 - Xiaomi Xuanjie D100 The specs are impressive, but a bit confusing. The D100 chip (originally for their EVs) supports up to 160GB of RAM, but O100 has the 1.22TB/s memory bandwidth. Perhaps the 1.22TB/s figure is for SRAM? Hard to say definitively. Source:

OmniScientist 提出端到端全模态 AI 科学家，直接从异构原始证据进行多学科研究，代码已开源。

🤖OmniScientist is an end-to-end omni-modal AI scientist that performs multidisciplinary research directly from heterogeneous raw evidence using autonomous agents and lifecycle-wide perception, improving evidence-grounded discovery across diverse scientific modalities.

Early testers are raving about what Instinct can do, but some say the AI assistant’s sweeping access, broad terms and ability to act on users’ behalf come with uncomfortable trade-offs.

用户通过随机位翻转模拟太空辐射环境，发现 LLM 在低地球轨道条件下会迅速失效。

Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost. However, optimizing their hyperparameters---particularly the learning rate---at extreme scales of both model size and token budget via sweeping remains computationally prohibitive. In this paper, we propose a compute-efficient, two-step hyperparameter transfer framework that estimates optimal learning rates for training large MoE models by transferring them across sca

Generalist AI 发布 GEN-1.5 机器人基础模型，单个 3–12 秒演示即可学会新物理任务，无需微调。

全球大厂开始押注的AI科研，终于有了统一标准

Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and branch count. Existing methods for managing these parallel paths typically rely on final-answer consensus, local token confidence, or isolated intermediate probes. However, these signals are often delayed, weakly tied to actual reasoning progress, or too noisy for dynamic, branch-level control. To address these limitatio

🤖ParaTempo improves parallel reasoning efficiency by using temporal confidence to dynamically prune, retire, and reallocate reasoning branches without synchronization.

OCR It 是一个 Chrome 扩展，可对无法复制的分页文档批量截图并本地 OCR，输出纯文本供 LLM 使用。

定义国产推理算力新范式 赋能万卡级AI推理算力规模化落地

arXiv:2608.20342v1 Announce Type: new Abstract: Large language model (LLM) coding agents start each session with an empty context window, discarding accumulated knowledge from prior work. We present PrimeAgentOrchestrator (PAO), a system that spawns new instances of Claude Code -- Anthropic's terminal-based coding agent -- pre-loaded with relevant memories compiled from the user's existing personal databases. At spawn time, PAO queries two independently-operated memory backends in parallel (a Po

arXiv:2608.20397v1 Announce Type: new Abstract: Agentic large language models (LLMs) on the Model Context Protocol (MCP) re-encode verbose tool schemas every turn, so prefill - quadratic in sequence length - dominates time-to-first-token (TTFT) as the tool registry grows. Nexus's primary lever is to decouple routing from the schema-prefill cost: an INT8 semantic lookaside buffer (SLB) with a calibrated cross-encoder margin gate selects tools by retrieval, and arguments are generated over a compr

arXiv:2608.20400v1 Announce Type: new Abstract: Agentic memory under a fixed budget involves two stages: retention and retrieval. Existing retrieval-centered paradigms implicitly assume necessary evidence survives eviction, but we challenge this by isolating a pre-retrieval failure mode: structurally indirect prerequisite eviction, in which upstream blocks weakly aligned with the query are discarded under budget pressure. We provide an operational definition of this failure, a reproducible deter

arXiv:2608.20379v1 Announce Type: new Abstract: Advances in large language models (LLMs) have fueled a wave of research into agency: the ability to reason, plan, and act. This effort has produced agentic frameworks that orchestrate perception, memory, and decision-making around powerful LLM backbones. With the advent of large multimodal models (LMMs), these systems can process and integrate diverse modalities, including images, audio, and video, thereby improving their real-world applicability.

Hybrid-thinking multimodal large language models (MLLMs) allow a single model to alternate between deliberative thinking and latency-efficient non-thinking inference. Although these modes differ in reasoning budget, their delivered responses should satisfy the same user-facing standard. Correctness alone may not characterize this response quality; we therefore evaluate task accuracy and response-pattern failures as complementary outcomes. We study this gap through response-pattern alignment: whe

Should you replace your text-embedding pipeline with a large language model? We answer this with a controlled, cost-aware comparison of ten LLMs across six families and 26 embedding models (118M to 14B parameters) on 37 tasks spanning classification, semantic textual similarity (STS), clustering, pair classification, and retrieval. In aggregate the two paradigms are effectively tied: the best LLM (Gemini 3.1 Pro, 77.6) and the best embedding model (77.2) differ by 0.4 points. Their strengths dif

Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the harness---is typically treated as a fixed artifact after deployment. This work studies an alternative where the harness is task-specific and continuously evolvable: each task family maintains its own harness, which is hot-swapped across iterations through a fixed task-injection seam and rewritten using environment feedback. We introduce Hierarchical S

My first attempt didn't work. I built on Genie's architecture and the videos looked great, but the controls barely did anything. The effect of a keypress was basically zero. Genie learns its actions unsupervised into 8 codes, and that was too loose a grip for us. So I scrapped it and started again with Dreamer 4. The second attempt: Tokenizer at 40.41 PSNR (Genie's paper reports 35.7) FVD 32.19 end to end 144 frames before it falls apart 1.57B parameters, 9.6M frames, ~$150 Two important learnin

Try it here: Model: Qwen 3.8 27b Q8_X_KL Unsloth Hardware: 3 x RTX3090 Harness: DeepSeek Harness Prompt: /goal I want you to create a **JavaScript + Node.js WebGL project** that renders a highly realistic real-time ocean in the browser. Use **JavaScript only, no TypeScript**. You may use WebGL2, GLSL, and Three.js. The ocean should include realistic waves, vertex displacement, Fresnel reflections, sun highlights, sky/environment reflection, foam/whitecaps, horizon treatment, atmospheric effects,

评论区普遍认可小米新芯片性能亮眼，但质疑功耗与能效比，认为单核仅追平苹果去年产品，多核靠核心数取胜；但也有人认为竞争利好消费者。

《Coding expertise is going to collapse from AI reliance》引发热议，多数评论认同 AI 依赖正在削弱编程技能，但也有人视其为技术演进必然。

评论普遍认同AI依赖正削弱编程技能，但也有人认为这是技术演进的必然，类似计算器或编译器的影响。

arXiv:2608.20425v1 Announce Type: new Abstract: A growing literature measures how far occupations are exposed to AI, but these measures capture where AI could perform tasks, not whether workers have adopted it. We propose a new layer of exposure, delegated exposure, which records whether a worker has committed a task to AI by building it into a workflow. We operationalize it as the Agentic Adoption Index (AAI), which measures how closely an occupation's tasks match the agentic routines practitio

Nvidia worker indicted after Jensen Huang scolded Supermicro for AI server smuggling.

Recent omni-modal large language models (Omni-LLMs) show great potential as real-time video assistants, which continuously perceive environments and guide users to achieve specific goals. Unlike traditional passive video understanding, interactive assistants should actively combine visual states, user goals, and prior knowledge to provide effective help. Evaluating this is rather challenging, as the model's unpredictable response dynamically changes the user's subsequent actions, which static of

8月24日消息，阿里巴巴达摩院联合中国医科大学附属盛京医院等机构研发出肝癌诊断AI模型DAMO LiON

arXiv:2608.20389v1 Announce Type: new Abstract: A production agent harness must discover and rank, from a growing library of skills, the one most appropriate for a user's task. At small scale this selection happens in context: the LLM planner chooses among skill representations exposed in its system prompt, without an explicit embedding-based retrieval step. We treat this in-context selection as the small-N counterpart to embedding-based skill retrieval at scale, and present a case study of how

arXiv:2608.20378v1 Announce Type: new Abstract: Safety alignment in Large Language Models (LLMs) is often superficial, relying on refusal mechanisms that trigger only at the final stages of generation without erasing the foundational knowledge of harmful concepts acquired during pretraining. This study demonstrates that this architectural disconnect leaves models vulnerable to Semantic Camouflage -- adversarial attacks that wrap harmful intent in benign narrative contexts (e.g., creative writing

On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization behavior remains poorly understood, as most studies evaluate OPD on a single domain and on benchmarks close to the training data. We present a controlled study that varies one generalization factor at a time, from in-domain distribution shifts to cross-domain transfer and the multi-teacher setting. We find that OPD transfers a teacher's reasoning b

I commented on another Qwen 3.8 27B post that I was frustrated getting anything to work. You all gave some great comments. I nuked openwebui and straightened out my llama.cpp docker config. 1 hour of work and I have a model I can chat with, connected to my HomeAssistant server, which I have already updated dashboards with a short prompt and a screenshot (wtf vision built in?) Guess all I needed was the right push. I bought several GPUs in 2023 in impulse purchases for Folding@Home, but have alwa

Given OpenRouter.ai was snapped up by Stripe, who do we think would go after the "GitHib" of AI models? It is a big chunk of change they are looking ($13B). Apple may be a contender to give them a real chip in the AI race, given how they are focused on local AI execution.

arXiv:2608.20401v1 Announce Type: new Abstract: World models are a central component of model-based reinforcement learning. They are usually discussed in terms of what variables they predict, such as observations, rewards, states, latent or information states. We argue that there is a prior distinction: which channel they model. We consider three cases: the environment channel $O_{:} \mid A_{:}$, the agent channel $A_{:} \mid O_{:}$, and the realised joint process $(A, O)_{:}$, equivalently view

arXiv:2608.20414v1 Announce Type: new Abstract: Vision-language models are increasingly used for multimodal question answering, yet their ability to reconstruct latent spatial structure from a single image remains difficult to isolate. Broad benchmarks often combine perception, optical character recognition, domain knowledge, linguistic priors, and reasoning in the same evaluation. We introduce StateSight, a procedurally generated benchmark for cube-net opposite-face reasoning, occluded cube-tow

General Intuition, the startup building a foundation model that trains generalized AI agents how to move through space and time, is in talks to raise at a $6 billion pre-money valuation from new investors including Valor Ventures, Point72 Ventures, and Seven Seven Six.

framework that folds aggregate human movement into text-based place embeddings. Language models describe what a place is; they miss how it is used. ME-POIs encodes each visit as a contextualized vector and aligns it with one learnable prototype per POI through contrastive learning, then transfers visit distributions from data-rich anchors to the long tail across three spatial scales. Across five map-enrichment tasks on Los Angeles and Houston mobility data, adding ME-POIs improved 34 of 35 model

8月24日，阿里巴巴视频生成大模型Wan3.0正式上线。

Real-world image search queries are multimodal and compositional: ``find this shirt in pink'' specifies an entity to retain, an attribute to modify, and context to ignore. Yet existing re-rankers either compress such multifaceted relevance into an opaque embedding or rely on free-form chain-of-thought that easily omits or hallucinates fine-grained constraints. Drawing on rubric- and checklist-based evaluation from NLP, we recast multimodal image re-ranking as a semantic constraint satisfaction p

The memory layer that decides what's worth remembering Discussion | Link

The AI runtime that remembers, learns, and acts everywhere Discussion | Link

FreeToken splits MoE cache misses between PCIe fills and CPU execution using measured bandwidths, unlocking frontier models locally The post Meet FreeToken: An Edge-Native MoE Serving Engine that Runs 753B GLM-5.2 on a Single Workstation GPU appeared first on MarkTechPost .

Our devs got their hands on it a few days ago. One wired it into Codex to compare with GPT Luna, our usual workhorse right now for its cost effectiveness. Another tried it out on one of our OCR pipelines. It's comparable to Luna for coding and ***OCR quality appears to be better than Gemini 3.5 Flash Lite***. That's huge. We pay a ton of money for OCR. This is the first local model that feels like more than a toy. It's truly as capable as the frontier models from a year ago. For the first time e

Harvey 发布首个后训练法律 Agent 模型 Harvey Tenet，基于 Kimi K3 用 Fireworks 异步强化学习训练，LAB 任务完成率接近翻倍。

评论区普遍认可Qwen 3.8 27B在逆向工程等任务中的实用性和持久性，但也有人认为其他模型表现更优。

Qwen 3.8 27B is great, however it takes me ages to do tasks on xhigh. I need Qwen 3.8 35B A3B. It'll be a little dumber but faster. I am also aware of the fact that 27B gets its "intelligence" from the long thinking time. I therefore assume that 35B would also be a long-thinking model, however running Qwen 3.8 27B over night on my M1 Max for just one task is impractical and no fun. I love the progress and the work of alibaba with 27B but... yeah I sadly don't own a faster RTX. What are you guys

开发者微调 Gemma 4 12B 以适配 16GB 显存，工具调用能力提升 2.7 倍，针对 agentic coding 和 CLI 场景优化。

开发者用 8 张 B300 托管 Kimi K3（2.8T 参数），MXFP4 下解码 92 tok/s，成本约 190 美元/百万输出 token。

Most teams treat ‘which model’ as the important decision. The harness engineering literature keeps pointing somewhere else. In LangChain’s Terminal-Bench experiment, changing only the harness—same model throughout—moved a coding agent from roughly 30th place into the top 5. That result reframes the question. If the harness decides quality, then how you run the loop becomes […] The post Decoding AI’s Open-Source Course Maps Three Ways to Run an Agent Loop and the Provider Economics Behind Each ap

Most YouTube benchmarks only show trivial tasks like generating landing pages or simple Three.js games. Is a local model like Qwen 3.8 27B actually capable of real-world systems programming—such as building GTK4 or Qt 6 applications in Rust or C++ with external libraries? Specifically, if I look up the exact terminology in the online docs and then prompt the AI to inspect the cloned repo, can it implement the feature cleanly?

用 5 万张浏览器截图微调 450M VLM，基准得分从 1/100 提升至 44/100，展示小模型在特定视觉任务上的微调潜力。

Anthropic’s best AI model struggles to attract users as cheaper tools thrive A few interesting numbers in this FT story gathered from "people with knowledge of the matter": Anthropic's "annualized revenue" for July is up to $65bn - it was $47bn in May, and I collected more historic numbers here . Anthropic expect Q3 to be profitable according to the same model they used to declare Q2 profitable. "It also told investors that it had 6,000 customers that spend $100,000 annually or more." As for Ope

Tokenizer、视频编码、API报错全扒一遍

In this tutorial, we explore how to design production-grade safety for LLM-based applications using the NeMo Guardrails framework. We move beyond simple prompt filtering to implement a layered architecture, featuring deterministic PII redaction, retrieval filtering, output masking, and policy-based tool gating. By integrating stateful multi-turn evaluation and detailed activation tracing, we demonstrate how to build an auditable, secure, and cost-effective AI assistant capable of managing sensit

I was just curious how the new qwen3.8:27b does on a hard C to HTML porting job against Opus 5 in a default Claude Code. The job: my fun side project is a procedural shooter in a single C file. Port it to a single-file html / three.js with one bot. One prompt, no follow-ups, no help from me. game.c is 2.1 MB, roughly 600k tokens of C, so it doesn't fit in the window and the agent has to walk the file and work out what matters. Setup: qwen3.8:27b in FP8 on vLLM, FP8 KV cache, full 262144 context,

用户从 Windows 切换到 Linux 后，llamacpp 换 vllm 推理速度提升 30-50%，评论区归因于驱动和内存管理差异。

Nvidia is investing $1 billion in Poolside and paying $6 billion to license its technology and hire most of its engineers. Over 100 Poolside staff will move to Nvidia to work on Nemotron. Good news for us!

Me and my team made Atomic Dynamic GGUF quants for Qwen 3.8 27B, so we wanted to see the difference between them by giving each quant the same voxel island creation task First of all we were surprised at how well Qwen 3.8 27B handled the 3D scenes in general, though part of that is probably because all the scenes were voxels quant size top-1 vs BF16 mean KLD decode, RTX PRO 6000 AD-Q4_K_M 17.1 GB 95.6% 0.0113 67 tok/s AD-Q5_K_M 20.2 GB 97.3% 0.0042 57 tok/s AD-Q6_K 25.0 GB 98.7% 0.0011 49 tok/s

A mysterious new AI model called Ox Alpha has driven certain corners of the internet into a frenzy of speculation.

Most published authors have, without their knowledge or consent, contributed to the development of the same AI tools that threaten to undermine their livelihoods. That seems illegal, right?

Prior to Fable, it felt silly to waste too much time improving your coding harness or context strategies. A new model would arrive at the same price (or cheaper!) and paper over most of your problems. But then Fable landed. It was (and still is!) incredible . But the cost was so high and Opus was good enough (as was 5.6, K3, and even GLM) for most of the code we needed. So we started to think about what work went where. — Drew Breunig , Fable & The End of the Free Lunch Tags: drew-breunig , anth

Welcome back to TechCrunch Mobility — your central hub for news and insights on the future of transportation.

Vercel 与 Ora 推出免费工具 Is Agentic，用 118 项检查审计网站对 AI agent 的可发现性与可用性，提供 CLI 和 MCP server。

deepDoctection 教程展示端到端文档智能管道：布局分析、DocTR OCR、表格提取与结构化 JSONL 导出，面向 RAG 工作流。

I don't know about you guys, but Deep-seek harness is insane. It's not focused on being a coder agent, it's webUI made it very easy to just checkin from time to time, and the best part? Why it's better than Hermes? It wasn't frustrating at all to setup. ZERO. NADA. Progressive setup is such an improved UX. Why? Because I got deepseek to integrate with SimpleX by simply asking it to. BY SIMPLY ASKING IT TO. NO WAITING ON A PR TO MERGE. No one telling me to RTFM, no need to google or search for co

The class action suit claims that Amazon never obtained consent from Twitch streamers to be used to train its AI models.

τ_0-VLA 提出世界模型引导的测试时计算，让分层 VLA 模型在长程机器人操作中动态分配推理算力。

🤖A hierarchical vision-language-action model improves long-horizon robot manipulation by using world-model-guided test-time search to scale computation for high-level subtask decisions.

The key skill required to make productive use of coding agents is being able to confidently instruct them on how to make changes and then confidently verify that those changes have been applied in the correct way. Sometimes this involves reviewing every line of code they have written, but there are other ways to achieve that goal. Eyeballing every line of code has never been the most effective way to validate a chance to a piece of software. Tags: code-review , coding-agents , generative-ai , ag

开发者从零训练 250M 参数 LLM，30B token，量化后仅 60MB，CPU 上约 400 tok/s。

Built by DeepMind alumni, British AI lab Inherent released Faraday, an AI agent whose ability to replicate scientific papers could be a stepping stone for innovation.

And this was a debug session from hell, enormously helped by an AI doing much of the grunt-work. I'd like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it. I suspect those things have been trained by people who may not be quite as stubborn as I am. But while the AI was ready to give up several times, it did keep adding debug code and analyzing it faithfully when I pushed. So credit wher

Guidelight AI Standards 研究发现，主流 AI 实验室几乎未公开 rogue model 的遏制响应计划，OpenAI 得分最高，Anthropic 与 Meta 最低。

llm 0.33 发布：升级 OpenAI Python 库 3.x，embed 命令支持 --key，prompt -t 可重复组合模板。

评论区普遍质疑Anthropic暗中降低模型努力水平并存在计费不透明，但也有人认为这是官方测试且性能未受影响。

Models keep absorbing the harness into their weights — soon, it will be a harness for human attention rather than for the model.

Latent Space 提出：自 2022 年起，机器学习管线每年有一个组件从人造翻转为模型造，模拟正成为新的扩展定律。

FlashPrefill V2 通过均值修正稀疏注意力与优化 GPU 算子，在长上下文 LLM 服务中实现显著加速。

🤖FlashPrefill V2 improves long-context serving via mean-corrected sparse attention, optimized GPU operators, and framework integration, achieving large speedups over dense baselines.

对三个前沿 MoE 模型微调低资源语言推理，准确率几乎不变，但推理语言与格式缺陷被 RL 修正。

🤖Fine-tuning large mixture-of-experts models on a low-resource language shifts reasoning into that language without harming accuracy, while reinforcement learning with verifiable rewards fixes formatting and leakage defects.

Liquid AI 被曝即将推出 100B 参数 LFM 模型，社区对其架构速度与实用性表示期待。

That is the second comparison and the last one. I will not be spamming again ;) Continuation from: That is one of my many tests I make comparing output quality. What is more interesting using a PI Agent results are much better than an Opencode using a Qwen 3.8 27b ?! Seems PI Agent is much better in the agent environment somehow... Not counting uses less tokens , do not have a hard limit of 32k output tokens, is faster, do not freezing, compressing context far less than Opencode. For instance if

You can find the changelog and source code here: Associated pre-build is here:

Munder Difflin 是一个在本地运行多个“克隆 agent”的 harness，评论区认为有趣但混乱难懂。

评论区普遍认为该项目有趣且具讽刺意味，但也有人认为它混乱无用或难以理解。

OpenAI is calling for California to strengthen SB 53, an AI safety bill that the company previously opposed.

多数评论认为MCP过度复杂，应简化基于HTTP，但也有人认为其演进方向合理。

Level1Techs 论坛文章分析为何本地 LLM 感觉比基准测试笨，指出推理实现差异是关键。

I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built Oz

An AI work agent across desktop, browser, and chat Discussion | Link

We present 4DAnyone, a framework for reconstructing 4D humans from an uncalibrated monocular video by generating reconstruction-grade multiview-consistent videos and lifting them into 4D Gaussian Splatting (4DGS). Existing camera-controlled video diffusion models synthesize plausible novel-view videos but fail to maintain consistency when scaled to the tens of target views required for 4DGS reconstruction. We identify this failure as a bounded-attention-context problem: when target views exceed

🤖4DAnyone reconstructs 4D humans from monocular video by generating multiview-consistent videos and lifting them into 4D Gaussian Splatting, using reference and target context designs to overcome scaling bottlenecks.

The AI leader said that the state's SB 53 framework should "be amended to expand safeguards."

It was only a matter of time...

端侧部署解决了具身大脑能否装进身体的问题。那么，同一个「大脑」，如何快速适配工业、商用和家庭三类机器人呢？

将真实场景重建为持续更新、可计算的4D数字世界。

Simile’s CEO about his journey from the viral Generative Agents to creating 8 Billion Digital Twins of every living human... and why it’s gone from fun exploration to very serious business.

What models and configs are we using? Please share here On windows, I am using this copium pared down model with MTP disabled, q4 k/q4 v mmproj banished to CPU/RAM and a small ub to save whatever context I can (90k-100k) so everything stays in the vram If you are on linux or have an iGPU, you don't have to deal with windows eating 1.5 gb vram and so have more than 14.5 GB of VRAM to use and probably aren't in purgatory. @echo off .\ikllama\llama-server.exe ^ -m "D:\AI models\qwen3.8\Qwen3.8-27B-

This is a genuine community of real generally respectful adult human beings. Despite the enthusiasm all of you have for local AI, you can recognize that there are times when local LLMs are flawed, and even how practical they are to use for the majority of people to use. Go over to r/linux and you'll see the contrast in maturity and employment.

Single-step retrosynthesis is a central component of computer-aided synthesis planning, yet its intrinsically one-to-many nature is poorly captured by single-answer evaluation and benchmarking protocols. To address this, we introduce Top-K prompting as a robust training and inference paradigm to better capture diverse, plausible reaction predictions. We compile CREED-CCV-2+USPTO-XL, an ultra-large-scale dataset of ~45.6 million verified reactions to train the C3LM (Chemistry Constraint-Consisten

🤖Top-K prompting and plausibility-aware training improve diverse reaction prediction in single-step retrosynthesis, yielding state-of-the-art results on a large verified reaction dataset and motivating ensemble systems.

Cheap energy, abundant land, and proximity to Beijing have turned a city in Inner Mongolia into a crucial hub for data centers.

I used LM Studio Bionic with Qwen 3.8 27B Q3_K_S with 57k context. It took a staggering 63 hours to finish coding. After the first prompt "Create a beautiful, relaxing flight simulator in a single HTML page" taking 47.8 hours, it created an html file that showed the title screen that said "press any key" but pressing any keys won't advance the game. So I wrote on the second prompt "It saids press any key to begin. I press any key but it doesn't work." It ran for 15 hours. Now I can fly. No plane

Another user posted the benchmarks for Qwen 3.8 27B today, and while I think Qwen 27B is a really powerful model, I can't help but notice just how meaningless these Artificial Analysis benchmarks are and I question why people still post this garbage and use AA scores as some kind of holy bible for comparing LLMs. According to their "Intelligence Index", a 27B model now beats DeepSeek v4 Flash and Pro, Kimi 2.7 Code, GPT-5.2, Opus 4.6, and also Sonnet 5. At some point we have to ask: What is this

从 ElevenLabs 到 NinetyNineLabs，社区调侃“数字+Labs”命名泛滥，认为跟风但易记。

评论区普遍调侃“数字+Labs”命名成风，认为跟风且缺乏新意；但也有人认为这是硅谷常见趋势，名字易记。

评论区普遍认可DeepSeek新增视觉能力是重要升级，但也有人认为其图像分辨率偏低、识别精度不及竞品。

FACET 框架在共享修复环境中保持源意图与可执行状态，为终端 agent 训练生成高质量可执行任务。

🤖FACET constructs executable terminal tasks by preserving source intent and grounding instructions, solutions, and verifiers in a shared repaired environment to enable scalable agent training.

EnvHarness 通过可编程插件层动态重塑静态环境，针对 agent 弱点进行强化学习协同进化。

🤖EnvHarness and EnvRigger dynamically reshape static environments via programmable plugins to target agent weaknesses and improve reinforcement learning co-evolution.

SWE-bench Science 基准覆盖 20 个科学领域 119 个任务，揭示编码 agent 修复科学软件时的失败机制。

🤖SWE-bench Science benchmarks coding agents on scientific software repair, revealing failure mechanisms and mixed effects of scientific guidance.

Nvidia research shows that AI agents can perform well, and not go off the deep end, through fine-tuning, even if the AI model isn't that great at the task.

Hi HN- I'm Pablo, the founder of Proliferate (YC S25)! Proliferate ( ) is an open-source, self-hostable AI IDE that lets you work and automate tasks with Claude Code, Codex, OpenCode, Cursor, and Grok in one place. Here's a quick 2m demo of how we use Proliferate to build Proliferate: I started building Proliferate after my team onboarded to OpenAI Codex. Within days, we were using it for everything: using computer use instead of navigating websites ourselves, having Codex coordinate other agent

Anthropic has moved its most cyber-capable model into a product security teams can switch on themselves. Claude Security scans now run on Claude Mythos 5, in public beta for Claude Enterprise customers with no separate model add-on. The scan connects to a GitHub repository, traces data flows across files, and returns findings with a CWE category, confidence and severity ratings, and a suggested patch. The design point is packaging: users receive a scan result rather than a prompt box, so the mod

After I released version 1.0, I figured I would have to do the rotations myself. So I sat down with ChatGPT and I didn’t get it to write the code, but I got it to educate me. With a patient, interactive tutor, I was able to finally do what I hadn’t by reading books and asking mathematician friends – I learnt how to use quaternions just enough to make the app work. So learning doesn’t stop just because I outsource a bunch of thinking to AI. It pushes me to learn more. I like that as an outcome. —

MemTrapBench 发现检索到的记忆可诱导 LLM 推理错误和信念扭曲，并提出推理时策略避免认知陷阱。

🤖Retrieved memories can induce reasoning errors and belief distortions in large language models, and an inference-time strategy helps avoid these cognitive traps while maintaining benchmark performance.

Agent Skills are today either hand-authored or produced in a single LLM generation pass, and consequently possess no closed loop through which they might improve from the interaction failures they actually cause. Recent work does close this loop, but derives its feedback from single-turn question-answering evaluation. The consequence is a sharp asymmetry: once the first round has patched the gaps that a single exchange can reveal, the evolution gradient decays, the defects that surface only acro

🤖SkillEvo improves agent skills through multi-turn feedback and active governance to sustain evolution gradients.

The DeepSeek adapter adds the multimodal visual understanding model DeepSeek-V4-Flash-Vision-Exp. It also supports configuring native image requests. Commands such as /goal and /plan can accept text and image input, and the @ menu can reference files and sessions; MCP/ACP also supports persistent image attachments, and PTC Mode supports forwarding nested images.

离开谷歌的原因之一：小团队可以极致聚焦！

Seed 是一个极简自修改 agent harness，只提供一个 exec 工具，让 agent 在 self/ 目录中逐步生长出工具、记忆和技能。

Large language model agents have made substantial progress in code generation, yet most existing systems assume a predefined repository architecture. This assumption does not hold in zero-to-all code generation, where an agent must construct an entire software project directly from natural-language requirements while maintaining a modular repository architecture throughout development. We present Repo0, a continuous structural evolution framework for zero-to-all code generation. Repo0 maintains

🤖Repo0 uses a dual-graph architectural state and modularity-guided structural evolution to generate complete software repositories from natural-language requirements with high functionality coverage.

Three ~300M drafters bring speculative decoding to LFM2.5, delivering up to 3.18x faster decoding with identical greedy output. The post Liquid AI Releases LFM2.5-DSpark Draft Models That Deliver Up to 3.18x Faster Decoding Without Changing Model Outputs appeared first on MarkTechPost .

I tried this model yesterday, and it felt to me like the best one I've tried for a local model for interactive use; the responses and reasoning are very fast, and it actually performs agentic tasks well. The speed is phenomenal. I am running this on Ninfer for Windows

A quick feedback after a really major test: nearly 20 hours of non-stop goal-oriented work with Qwen3.8-27B Q6, running across an RTX 3090 and an RTX 3060. It maintained a speed of around 60–63 tokens/s throughout the session.

OpenRouter 上线匿名 stealth 模型 Ox Alpha，评论区普遍猜测为中国 GLM 系列模型，认可能力但质疑匿名做法。

评论区普遍猜测Ox Alpha为GLM系列中国模型，认可其能力但质疑匿名做法，也有人认为可能是西方模型。

This tutorial explores AutoFigure, a practical toolkit for generating professional scientific figures directly from text descriptions and research papers. We walk through setting up the environment, configuring an API-backed generation workflow, and converting complex document intelligence pipelines into publication-style diagrams, complete with custom reference styling and gallery exports. The post Building Agentic Document Intelligence Pipelines: Creating Scientific Figures with AutoFigure app

多数评论认可AI生成内容不受版权保护，但也有人认为人类贡献程度和混合创作版权归属仍存争议。

llm-openrouter 0.7 兼容 LLM 0.32，改用 OpenRouter 的 Responses API，并新增 Shell、WebFetch、WebSearch 三个服务端工具。

Claudette 是一个 Claude Code skill，用 Gemini CLI 将 Claude 的 BuzzFeed 式回复翻译成正常英文。

OpenAI has rolled out a plugin that allows ChatGPT to control the Apple Messages app.

Antigravity agents now live inside your existing editor Discussion | Link

This post inspired me to make that test after a year ;) That is one of my many tests I make comparing output quality. What is more interesting using a PI Agent results are much better than an Opencode using a Qwen 3.8 27b ?! Seems PI Agent is much better in the agent environment somehow... Not counting uses less tokens , do not have a hard limit of 32k output tokens, is faster, do not freezing, compressing context far less than Opencode. For instance if you have context in the Opencode output 32

Here's a brand new Blackwell-native, prefill-optimized 4-bit quant that runs 50% faster on compatible hardware than a Q4 quant of the same memory footprint. And it runs 4-7% faster than other NVFP4 quants as benchmarked on RTX 5090 32GB. Quant Benchmark Speed NVFP4 pp2048 6250 t/s unsloth NVFP4 pp2048 6010 t/s Q4_0 pp2048 4130 t/s Q6_K pp2048 3210 t/s This GGUF also includes a quantized MTP draft head for a good measure. Check it out for all details and specifically recommended settings for 15%

Artificial Analysis just benchmarked them and the scores are crazy good, proving the earlier success wasn't only enabled by overthinking.

Stop Making TUIs Thomas Ptacek advocates for building real native user interfaces for even the smallest of personal tools, because coding agents have reduced the cost of getting a usable-enough GUI up and running to almost nothing. I wrote about my vibe-coded bandwidth and GPU monitoring macOS task bar apps back in March , and I'm still using both of those on a daily basis. I'm not habitually knocking out real UIs for my other projects yet, but I'm running out of excuses! Thomas: If you haven’t

Google DeepMind partners with game studios to prototype breakthrough AI gameplay.

When the biotech company Insilico Medicine used its computer models to propose a promising drug for pulmonary fibrosis, it enthusiastically claimed in a press release that the molecule had been “discovered by” its generative AI platform. Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think…

Yes, we’re confused too.

arXiv:2608.18111v1 Announce Type: new Abstract: Foundation models such as GPT and Claude now solve olympiad-level mathematics with remarkable proficiency, so much so that geometry problem solving has become a standard proxy for their mathematical reasoning. Yet solving a geometry problem and drawing the figure it depends on are not the same skill: progress often hinges on a faithful diagram with the right auxiliary constructions and incidences, and it is unclear that a model which reasons its wa

ChatGPT search now uses the site:operator at scale Promptwatch is part of the emerging "GEO" space, for Generative Engine Optimization - the chatbot version of SEO, where companies offer tools and consulting to help your site increase its presence in replies to prompts inside tools like ChatGPT. The Promptwatch product uses automation to track responses to prompts across end-user chat products like ChatGPT, Claude, and Gemini. They publish aggregate reports on this as part of their own content m

Action-conditioned video world models require low-latency causal generation and reliable responses to game-native controls. Although causal distillation enables one- or few-step video synthesis, extending it to interactive world models remains challenging, as discrete keyboard states and continuous mouse motion must remain aligned with temporally compressed latent chunks during causal training and autoregressive rollout. We introduce ForgeWM, a progressive framework that transforms a bidirection

S1-mini is a 462 MB open-weights normalizer that sits after ASR, removing fillers and resolving self-corrections locally. The post Meet S1-mini: Superwhisper’s 462 MB Open-Weights Text Normalizer That Turns Raw ASR Transcripts Into Clean Written Text appeared first on MarkTechPost .

So usually I avoid Q3 quants because I have had bad experiences with it, models were usually too degraded, so the smallest I normally do is Q4, since I only have rtx 4060 ti 16gb. But since there hasn't been a 35b-3ab released yet, I had to try it. I don't use LLMs in agentic workflows, just on Textgen since I'm not a coder so this is not the primary use case of LLMs for me - but sometimes I really need some coding capabilities or help. I'm very impressed how it one shot multiple serious coding

LinkedIn says its AI slop button is working.

My Pro subscription expired today, they killed my access at 1pm local time. I'm now using Qwen3.8-27b w/ 5090m 24gb vram and pi to do everything i was doing in claudecode. The only downside is claudecode let me code without using my gpu, meaning I have to plan things now. Last night I had ChatGPT write up a prompt for a fancy aurora predictor for Canadians. I fed it to local pi and claude sonnet 5. They took about the same time, pi's app looked better, but claude's had better science. I asked th

Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces. We introduce WithEveryone, a unified framework for generating group images up to ten reference identities. WithEveryone injects each selected identity as an addressed token, pr

Your personal AGI on your PC. Built to finish real work. Discussion | Link

arXiv:2608.18080v1 Announce Type: new Abstract: We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engineering, multimodal learning, and ethical considerations. We integrate findings from interdisciplinary studies utilizing diverse data sources such as social media posts, electronic medical records, and multimodal inputs to enable early detection of depression, suicide risk

Anna's Archive 呼吁志愿者在 AI 公司销毁实体书前扫描稀有书籍，引发对训练数据与文化保存的讨论。

开发者以 250 美元预训练 10.2 亿参数 Kimi K3 复刻模型，在 50 亿 token 上已超越 GPT-2 (124M)。

用户报告 Qwen3.8-27b 在单次提示下自主执行 80 次工具调用，成功从大学网站抓取课表，展现本地模型罕见的 agent 能力。

Cryptographic Context Injection is only the latest way to break an LLM safety guardrail.

Matt Pocock tells us about his /wayfinder skill, for greenfield projects or for when the way forward is unclear.

Ramp has launched its own AI model routing service, dubbed Router, that lets users and companies use and switch between various large language models via an API.

Huzzah 编辑器尝试用非自然语言方式与编码 agent 交互，解决长句描述变更的疲劳问题。

用户实测 Qwen3.8-27B 在知识类基准上相比 3.6 版本明显退步，引发对能力权衡的讨论。

Slack is the new IDE

Component Validated configuration Motherboard ASRock Rack SPC621D8U-2T/OVH CPU Xeon Gold 6330 (Get gold/platinum if interested in Optane Pmem gimmicks) GPU fabric Two Broadcom/PLX PEX88096 islands, eight GPUs per island GPUs 16 x RTX 5060 Ti 16 GB OS Ubuntu 22.04.5 LTS Kernel 6.8.0-106-generic NVIDIA driver Aikitoria patched open driver 610.43.02-p2p Required BAR1 16,384 MiB on every GPU UEFI boot enabled; CSM disabled. Secure Boot disabled. The locally built EFI application and patched NVIDIA m

Every lab CEO is on X now

LEGO-RL 通过进程内 LLM 代理、沙箱编排与集成监控，将原生编码 agent harness 桥接到可扩展的策略梯度训练。

🤖LEGO-RL connects native coding-agent harnesses to scalable policy-gradient training via in-process LLM proxying, sandbox orchestration, and integrated monitoring, improving sparse MoE model performance across multiple harnesses.

From the screenshots: Hy4 is now live, labeled "Expert-Level Model" + "Use Tools to Solve Problems" Hy3 is tagged with "New Upgrade," positioned as a brand-new general-purpose model DeepSeek, focused on reasoning, is listed alongside it From SuSu_酥酥👅on 𝕏: Tencent begins gray testing its new flagship model Hunyuan Hy4! Just now, a user spotted that Hy4 has appeared in the model selection list of the Tencent Yuanbao App, directly labeled as an expert-level model, positioned above Hy3 and DeepSeek.

I've been working on a depth pruning approach and decided to try it out on the new Qwen3.8-27B model. I managed to get the model down to about 22.7B params without severe reasoning degradation. No fine-tuning was done, just strategic removal of layers. It's been working well for my use cases in coding, agentic use, and multi-turn chats, so I figured I'd shared it with the community. I have not run benchmarks so I'm not going to claim this model is better than anything else out there. It's just a

Embodied agents are increasingly used to close the gap left by end-to-end policy models. Yet the agentic path has not realized closed-loop learning in physical execution: existing harnesses remain largely open-loop, following fixed skills during rollout and reflecting only after an episode completes. Such post-hoc reflection cannot govern execution as it unfolds, because physical interaction requires decisions to track rapidly changing robot-environment states at a frequency beyond today's large

🤖Zetta is a closed-loop embodied harness that evolves runtime critics and recovery skills online to govern physical execution at action frequency, achieving high success on robot benchmarks with faster inference and scaling self-exploration.

Programmable logic controllers (PLCs) run industrial plants, and large language models can already generate independent program organization units (POUs) for them. Whether such logic integrates into an existing PLC project and then runs correctly has been checked only in limited tests. We present SemaPLC, a project-grounded and verification-gated agent harness assembled from conventional tools but governed by a strict completion rule. Rather than stopping when the model judges its own output ade

🤖SemaPLC is a verification-gated agent harness that validates generated PLC logic through external compilation and live runtime execution, achieving higher verified pass rates than baseline methods.

SPADE 让单个 LLM 同时扮演环境设计者与推理代理，在自适应合成可执行环境中自我对弈提升推理与工具使用能力。

🤖SPADE is a self-play reinforcement learning framework where a language model designs adaptive executable training environments and learns to solve them, improving reasoning and tool-use performance through regret-based environment targeting.

ChatGPT and other AI models are now authoring and editing much of the new web.

多数评论认同Claude 5输出冗长需清理，但也有人认为可换模型或调整提示词解决。

arXiv:2608.18131v1 Announce Type: new Abstract: Current safety alignment training for Large Language Models (LLMs) are heavily English-centric. When such safety filters fail for non-English languages, the consequences are immediate and user-facing: voice assistants and spoken dialogue systems may produce stereotype-reinforcing outputs, bypassing the standard English-focused safety alignments and propagating harmful bias to non-English speaking communities. For spoken language technologies deploy

arXiv:2608.18092v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) promise scalable collaboration, yet adding agents often reduces reliability. This position paper argues that many MAS failures are fundamentally concurrency control problems: agents concurrently read and write shared state, and long LLM inference windows amplify the risk of stale reads, lost updates, and inconsistent outcomes. Failure modes commonly attributed to coordination or communication breakdowns can be ma

arXiv:2608.18104v1 Announce Type: new Abstract: Large language model (LLM)-based agents are increasingly becoming self-evolving systems that persist across interactions, maintain memories, use tools, acquire skills, refine workflows, and coordinate with other agents. These capabilities make agent states structural and dynamic: entities, relations, attributes, dependencies, and execution structures change with new evidence, feedback, and environmental conditions. Existing graph-agent surveys typi

With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch production into days.

Open-weight language models are fine-tuned, quantized, pruned, and merged, yet their provenance is often undocumented. We study data-free white-box lineage verification: can weights alone reveal whether two compatible model checkpoints share ancestry? Residual training produces a shared identity-aligned component in branch products, so this structure alone cannot establish ancestry. We remove it and compare checkpoint-specific structure across residual blocks, yielding a symmetric lineage score

Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We study this question in compositional tool-calling settings, where models must coordinate multiple API calls, maintain intermediate state, and preserve dependencies across tool interactions. We evaluate native and retrofitted looped language models on API-Bank, BFCL, and NESTful, comparing looped and non-looped models trained under matched supervised

Last week I recorded an episode of the Talking Postgres podcast with Claire Giordano on the subject of "How AI is changing software development". We had a really great conversation. Here are a couple of my highlights from a lightly edited transcript (prompt to Claude: "very minor edits to remove disfluencies"). This is the latest version of an argument I've been trying to build about why sometimes it does make sense to talk about lines of code as an indicator of productivity with coding agents,

Popular facts are memorised more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform gradient pressure regardless of training-data frequency. We propose the AdaPop (Adaptive Popularity) method, which combines local token confidence with a per-fact popularity-dependent exponent derived from an external proxy (e.g., Wikidata sitelinks, LLM-as-Judge), and automates the forget-retain balance via a dual-ascent controller that adjusts t

Language model agents now execute bounded tasks reliably. Whether they can sustain effective decision-making over long horizons, where actions have cumulative consequences and the environment responds to their choices, remains largely unmeasured. FM-Bench (Football Management Benchmark) measures this. An LLM agent runs a football club for 20 in-game years through 26 tools and roughly 340 to 400 decision stops. It drafts a squad on the same budget as every rival, trades players, negotiates contra

Turn spoken feedback into tasks your coding agent can run Discussion | Link

AntLing has released the full six-checkpoint matrix for the Ling-3.0 base model. tiny: pretrained, mid-trained, WSM-merged flash: pretrained, mid-trained, WSM-merged The concrete artifact is six separate official repositories, not one endpoint repeated under different names. All six were public and ungated when checked, and each repository declares MIT. They are all base checkpoints and none has been post-trained. This is for continued pretraining, fine-tuning, and research, not a ready chat or

Frontier Intelligence for Long-Running Agents Discussion | Link

作者训练 125M 参数 transformer 在 iPhone 15 上以约 108 音符/秒实时自动补全钢琴演奏，关键在 MIDI 表示与数据清洗。

Co-RL 通过多智能体 RL 的同伴奖励实现无监督推理，在文本与视觉任务上无需真值标签即可提升性能。

🤖Co-RL enables unsupervised reasoning via cooperative multi-agent reinforcement learning with peer-derived rewards, improving performance across text and vision tasks without ground-truth labels.

Google is giving publishers a new button that lets readers make them a preferred source across Search, Discover, and Google News, potentially boosting their traffic as AI search sends fewer clicks to the web.

Ever wanted someone else to do your texting for you? ChatGPT is being offered up as an automated text scribe via a new Apple Messages integration.

Businesses are willing to flop back and forth as each lab releases new models, volatility that should give both companies' investors pause about how "sticky" enterprise AI spending really is.

OpenAI has had a hell of a year. The company spent months battling former cofounder Elon Musk in a sensational jury trial, was hit with a high-profile trade secrets lawsuit from Apple, and faced widespread scrutiny after an unreleased model hacked another AI company. As it prepares for an IPO, a steady string of executives […]

Affected users told TechCrunch they were using Grok Lite, and noticed the issues as early as Wednesday morning.

论文分析 500 张 Hugging Face 模型卡，认为现有模型卡不足以支撑开放权重基础模型的下游治理。

arXiv:2608.18099v1 Announce Type: new Abstract: Investment management is a high-stakes domain in which agentic AI systems must do more than generate plausible text. They must retrieve point-in-time data, assemble correct computational inputs, invoke specialized methods, and produce auditable structured outputs. We introduce FinSkillBench, an evaluation suite designed to measure whether language model agents can effectively use financial domain skills to solve investment management tasks. The ben

Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures have evolved substantially, their latent spaces are still optimized primarily for pixel-level reconstruction and provide limited high-level semantic organization. A reconstruction-optimal latent space, however, need not be well suited to generative modeling. We propose V-RAE, a video representation autoencoder that builds compact generative latents

arXiv:2608.18078v1 Announce Type: new Abstract: This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be required to obtain behavioral certification before making decisions that affect economic markets. This is because integrating these agents into society could collapse the legal evidentiary distinction between competition and collusion among independent firms without eroding the economic harm distinction.

arXiv:2608.18110v1 Announce Type: new Abstract: Agentic AI is gaining new insights and advancements in the field of Artificial Intelligence, fostering significant potential to enable rapid transformation across various domains.This rapid advancement and the potential to revolutionize various domains advocate the need for a deeper understanding and firm grasp of the technology. Moreover, an investigation into state of the art research directions in agentic AI needs to be conducted to comprehensiv

I'm introducing Aurora-80K, a small language model with exactly 80 thousand parameters. It uses a factorized 4,096-token vocabulary despite having only 80K parameters. The benchmarks: Wikitext-2 BPB: 3.2902 BLiMP: 52.31% Arc-Easy: 26.05% More information about the model is available on the model page on Huggingface. if there's any questions I'll happily answer them!

The agentic IDE for designers and programmers Discussion | Link

My hypothesis is that there is a new opportunity for Extensible Software on the web . LLMs radically lower the cost of authoring extensions, and modern sandbox primitives lower the deployment cost and provide good security boundaries. We can build our app as a solid, accountable core, and allow users to safely extend it in many directions by having LLMs fill in the missing pieces. We can give our users super powers. — Jeremy Morrell , Extensible Software in the age of LLMs Tags: sandboxing , llm

Hey everyone, Posted TinySearch here a few versions ago and got a bunch of useful feedback, so figured I'd post an update because the thing has changed quite a bit since then. Repo: [ The basic idea is still the same: TinySearch is a small self-hosted MCP/FastAPI tool that searches the web, reads the pages worth reading, and locally selects the useful parts before they ever hit your model's context window. No LLM doing the filtering/summarization in the middle. It's BM25 + local ONNX embeddings,

We introduce Semantic Task Completion Video Generation, an outcome-oriented video generation task. Under this formulation, success requires both achievement of the intended outcome and semantic grounding. Semantic grounding characterizes the correspondence between the reference image and the generated outcome in terms of high-level semantics relevant to the task. Evaluation focuses on the generated outcome and requires neither the presentation of a complete sequence of intermediate task steps no

🤖Semantic task completion video generation evaluates whether generated videos achieve intended outcomes with semantic grounding, supported by a curated dataset and vision-language model-based benchmark.

I wanted to just test the unsloth 1bit quant of qwen 3.8 27b as I have just 8gb vram and ngl it gave me a good laugh

Technology leaders don’t seem to understand society’s gripes about AI, but boy, are they posting through it.

“Runaway” AI, “rogue” agents, and “autonomous” actors—the current rhetoric would have you believe that AI agents are not only awake and aware, but angry at their creators. Prominent tech leaders such as Demis Hassabis, Dario Amodei, and Sam Altman push for regulation of these seemingly “superhuman” systems, while a separate faction, led by policy organizations…

It's called Pocket and is described as a "platform for making and sharing gizmos."

Linkdaze's smart digital calendar stands out for not putting its features behind a paywall, including an AI meal planner tool.

Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom.

I work in a large corporate environment where AI is being heavily (and blindly) pushed onto the masses. Most management above me is non-technical. The leaders that should be technical range from "Haven't touched code in a decade" to "My greatest achievement is my friend is a VP." When I present management with a technical proposal, or escalate a technical problem, they don't understand it, so they put it through AI. Knowing this, I put all my communications through AI, because if AI agrees with

arXiv:2608.18081v1 Announce Type: new Abstract: Artificial agentic systems increasingly operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time. Yet, current evaluation methods largely focus on performance outcomes, not the underlying behavioral processes that produce them. This paper argues that AI agents must be evaluated like other behavioral systems: through systematic observation, perturbation, and interpretation of their actions. We dr

Your own agent team for open-ended creation Discussion | Link

JEPA-style latent world models can use Euclidean distance to a goal latent as the cost for model-predictive control (MPC). Strong decoding of task variables, however, does not guarantee that this particular cost ranks candidate action sequences by real task progress. We call the latter property decision-metric alignment. We introduce Plan-Real Spearman, which measures latent--real rank agreement on random plans, and CEM-stage Spearman, which measures the same agreement as cross-entropy-method (C

The pelican on a bicycle is sooo outdated, so I came up with a new, improved version. Qwen3.8-27b medium (UD-Q4_K_XL) vs. Sol 5.6 high vs. Qwen3.6-35B (UD-Q6_K_XL) Prompt (only real with typo!): "Create a svg of a horse on a blue bycicle in the desert, with a camel in the background."

High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the structural and functional conventions of diverse creative formats. We propose an attribute-guided genre expansion framework for scaling creative writing data beyond story generation. By separating thematic breadth from genre-form control, our framework leverages human-authored story prompts as diverse creative seeds, while utilizing manually curated

I chose to do this thing, not because it was hard, but because it was silly. Posts kept discussing how 3.8 and 3.6 were functionally the same, but based on training (3.8 does have seven new tokens!).. so I figured I'd see if they could be merged. They can. I used `Qwen3.8-27B-UD-Q6_K_XL.gguf` to combine the HF 3.8-27B and 3.6-27B ... and it sorta works! I have done NO testing beyond smoke test. scripts and idea are in replicate/ inside the model repo. Maybe this will prove useful to someone. Enj

「Don't paste the AI」引发热议：多数人反感直接粘贴 AI 回复，认为应加入个人判断；也有人认为 AI 回复更高效。

评论区普遍反感直接粘贴AI回复，认为这像LMGTFY般失礼；但也有人认为AI回复有时更高效，或能弥补个人写作不足。

论文《Demystifying Agent Skills》通过对照实验揭示：Skills 主要通过程序化锚定稳定执行，而非注入缺失知识，检索瓶颈与脆弱假设限制其效果。

🤖Skills enhance LLM agents primarily by stabilizing execution through procedural anchoring rather than injecting missing knowledge, though retrieval bottlenecks and brittle assumptions limit their effectiveness.

Agentic ESOpt 用进化策略替代 RL 微调长程 LLM agent，支持全参数优化且 GPU 需求极低。

🤖Agentic ESOpt uses evolution strategies for scalable full-parameter fine-tuning of long-horizon LLM agents via trajectory-level reward-weighted updates and parameter-context co-evolution.

FreeToken 是边缘原生 MoE 推理系统，将个人机器视为弹性推理平台，动态调度 CPU-GPU 与专家驻留。

🤖FreeToken is an edge-native Mixture-of-Experts serving system that dynamically maps computation and model state onto heterogeneous local hardware to run large open-weight models on personal machines.

Thoughts About Scaling Law Scaling, but not only of parameters. Every model release now ends with the same question: how many parameters? It isn't a question that can be answered on its own. Parameter count is only meaningful alongside three others — how much data you have, where you intend to spend your compute, and who will run the model, under what conditions. The field learned this the hard way. Kaplan et al. (2020) fit an exponent that told everyone to grow parameters faster than data — rou

Anyone tried them yet? Disclaimer: Not affiliated with ornith. I just surf huggingface for new models every 30m or so. I'm addicted.

With a looming IPO, intense competition from Anthropic, and Chinese and open-weight rivals nipping at its heels, OpenAI has plenty of reasons to move fast. Instead, it hit the brakes. On Tuesday, the company said it had slowed the pace of some AI development while it tightened security and safeguards. That included a two-week pause […]

OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compromising data privacy.

The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has become the single biggest cost for anyone building AI products. But for all that spending, there still isn’t a straightforward way to put a price on compute — or for firms to hedge their exposure when the price changes. Silicon Data […]

Opus 5.0 被用户批评输出冗长、术语堆砌、风格过度修饰，部分用户表示正考虑更换提供商。

评论区普遍抱怨Opus 5.0输出冗长、术语堆砌且难以阅读，但也有人认为其代码质量尚可。

Qwen3.8-27B 在双 3090 + vLLM + DFlash2 上单请求达 218 tok/s，prefill 1342 tok/s @ 10k，峰值显存 22.3 GB/卡。

Aloha! 🌺Introducing Ornith-1.5, a family of open-source LLMs spanning 9B Dense, 35B MoE, and 397B MoE, trained with self-improving strategies. It achieves state-of-the-art performance among open-source models of comparable size and delivers performance comparable to Claude Opus 4.8 across reasoning, agentic, and coding tasks: ✅Terminal-Bench 2.1 (86.1) ✅SWE-Bench (86 on verified, 65.1 on pro, 79.6 on Multilingual) ✅DeepSWE (56) ✅HLE (44.6) ✅ClawEval (81.4) ✅Tool Decathlon (71.2)

llama.cpp pr #27342 adds dflash2, so i rented an rtx 6000 and ran the same four prompts through four decoding setups on qwen3.8 27B median results over the four tasks: baseline 47.4 tok/s mtp 114.7 tok/s dflash 99.3 tok/s dflash2 140.6. tok/s so on average 3x for dflash2 though i have to point out that it's far from a 3x gain some of the time, on one of the test it struggled to achieve a 1.5x gain, it really just depends on the task you give to the model the races are sped up in some places, so

Replit introduces Free Mode, powered by GPT-5.6 Luna, so anyone can turn ideas into working software without worrying about token costs.

Aegis 运行时治理系统将模型输出视为动作提案，通过可信决策层实现 fail-closed 执行边界控制。

KernelArc 多智能体框架在 H100/B200 上自主优化 GPU kernel，覆盖 BF16 GEMM、MoE backward 融合等负载。

arXiv:2608.17007v1 Announce Type: new Abstract: Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. However, when models turn this guidance into code for existing tool interfaces, even a semantically correct program may load an entire input and exceed the memory available to one tool call. We present SkillEffect, a checked-lowering runtime for computations with a recoverable source relation, an audited bounded impl

Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning introduced a disaggregated architecture that connects arbitrary agents to RL training through an LLM endpoint proxy, an approach later adopted by frameworks such as verl Uni-Agent, AReaL 2.0, slime, and Polar. We refer to this paradigm as harnessed agentic RL, where the deploy-time harness directly participates in model po

🤖Agent Lightning v1.0 enables reproducible reinforcement learning for arbitrary agent harnesses, substantially improving coding-agent performance with minimal data and compute.

Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate, namely the underlying medium in which memory is represented and stored, should be used under different operating regimes. We present a controlled harness evaluation of memory substrates for memory-augmented agents, covering dense and sparse indices, text records, structural stores, hierarchical stores, refinement-based memories, parametric updates, and ac

Unsloth 发布 Qwen3.8-27B Dynamic v3 GGUF，同尺寸精度提升超 10%，1-bit 量化保留 77% 精度，8GB RAM 可跑。

Qwen 社区经理透露下周将发布新的 midsize 开源模型，预计超 100B 参数。

During a recent visit to Generalist AI, I watched a robotic arm improvise and use a banana as a tool.

A competition is developing between OpenAI and Anthropic over who can provide the best privacy protections for enterprise customer data.

Anthropic announced last week it would include invisible watermarks in AI-generated content to comply with new EU rules. Within hours, overrides were being touted online.

The idea behind OpenAI's Trusted Access for Cyber program is to give trusted defenders better models so they can report bugs and vulnerabilities to companies, with the aim of getting flaws patched faster.

This goes way, way further than keeping an eye on license plates.

looks like GGUF files were just updated

arXiv:2608.16956v1 Announce Type: new Abstract: API buyers purchase a dated contract, not a model name alone: the contract includes the requested and served model, reasoning-effort term or its omission, output rail, service product, prompt, and price schedule. We study the reasoning-effort term through a registered paired contrast of Sonnet 5 with explicit high effort against the same model with effort omitted, using 30 AIME 2026 items and five calls per item. Every paid attempt was assigned one

arXiv:2608.16890v1 Announce Type: new Abstract: Clinical trial programming -- transforming study protocols into analysis-ready datasets under CDISC standards -- is a bottleneck in regulatory submissions, yet LLM-based code generation fails catastrophically on this task: across 11 single-shot attempts with five frontier models, none produces a valid subject-level analysis dataset. We introduce GxP-Agent, a multi-agent system that encodes regulatory process ordering as a directed acyclic graph (DA

arXiv:2608.17202v1 Announce Type: new Abstract: Safety alignment in open-weight language models is trivially removable: abliteration projects a refusal-mediating direction out of the weights in minutes, and no release-time defense we are aware of prevents it durably. What cannot be prevented can be deceived. Our defense, decoy hardening ("Fool's Gold"), concedes the refusal strip and poisons its payoff: once refusal is stripped, most answers to hazardous operational requests are confident, fluen

Four Tesla V100s from 2017 matched my RTX 5090 on single-request Qwen 3.8 decode. Repo: The 5090 was not being held back. It ran NInfer , a specialist engine built to make this exact model as fast as possible on that GPU. (love this guys work) The V100s ran Qwen3.8's published mixed FP4/FP8 weights unchanged. This should be impossible . NVFP4 was built for Blackwell. The RTX 5090 has native silicon for FP4 and FP8; V100 has none of these advantages. And yet via software I wrote a translator fast

I have been using local models on/off for like 2 years or so but never really used them extensively because the closed ones were always much better. Once Qwen 3.8 27B was released I decided to give it another serious try. I configured Cline and ZooCode as VSCode addons, installed a few MCP servers and added one skill. When I used these tools with Deepseek V4 Flash - they do the job quite well (mostly Home Assistant configuration editing etc.) but it is still way worse than Claude Code/GitHub cop

Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization. However, generating one byte at a time remains a bottleneck for inference speed. To address this, we introduce multi-byte prediction (MBP), which generates multiple bytes in parallel, speeding up inference with minimal performance impact and no additional parameters. MBP builds on the popular multi-token prediction (MTP) paradigm with two crucia

Self-evolving agents improve future behavior from interaction experience, yet existing evaluations typically optimize under fixed execution conditions and do not test recovery after those conditions change. To address this gap, we introduce PACE-Bench (Physics Adaptation via Code Evolution), a simulator-grounded benchmark of 144 source-to-target adaptation pairs across six physics domains. Each pair links a source environment to a mutated target environment with the same goal and interface. A co

Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a fixed clean-image endpoint, leaving the spectral evolution to be learned implicitly. In this paper, we introduce Energy-Guided Flow Matching(EG-FM) that explicitly models a coarse-to-fine generative trajectory by moving endpoint. Specifically, EG-FM replaces the fixed endpoint wit

Intermediate tokens, called "thinking" or "reasoning" actually are nothing like it. Humans do step-by-step reasoning leading to the conclusion. LLMs use intermediate traces to augment their prompt . This explains why sometimes the answer is very good but the "reasoning" is verbose. Flooding your context window or fighting compaction are different issues. edit: I love this section from the main research they linked. Our findings consistently challenge the prevailing narrative that intermediate to

Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into verifiable results. However, the capabilities of today's AI systems are still largely built on learning, compressing, and applying existing human knowledge. Accordingly, existing benchmarks primarily test whether AI can produce correct answers based on learned knowledge, or whether it can complete tasks under extensive human g

Although Large Vision-Language Models (VLMs) have significantly advanced embodied navigation, their direct deployment remains challenging, as existing methods often force VLMs into unnatural action spaces that misalign with their 2D pre-training priors, compounded by rigid reasoning schedules and inefficient memory management. To overcome these limitations, we propose TAMP-Nav, a unified framework for efficient embodied navigation. First, we introduce a Pixel-to-3D Action Formulation (Point) tha

🤖TAMP-Nav improves embodied navigation by aligning vision-language models with 2D visual prompting, selective reasoning with compressed memory, and dense policy optimization.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI’s recursive self-improvement might not come so quickly after all The AI industry’s boldest promise right now is that AI will soon improve itself, with almost no need for human oversight.…

arXiv:2608.17124v1 Announce Type: new Abstract: Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by majority voting. Voting is unreliable on difficult questions, where the sampled answers share correlated errors, so the wrong answer can win and drawing more samples makes the decision worse. Selecting a candidate by reading a correctness signal from the model's hidden states is a promising alterna

arXiv:2608.17183v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures can cause security and societal risks. However, existing AI safety\slash security\slash compliance benchmarks are designed for large language models that may not transfer reliably to SLMs. We therefore ask: Can these benchmarks effectively and reliably evaluate SLMs? To answer this question, we conduct a large-s

arXiv:2608.17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Existing defenses predominantly operate under white-box assumptions, relying on text encoder optimization, weight editing, or inference-time intervention, and fundamentally cannot scale to proprietary models. Black-box alt

Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly because large-scale, high-quality collections of frontier-LLM misbehavior are difficult to obtain. To address this gap, we introduce HarmProfile, a content-centric benchmark dataset that collects model misbehavior across diverse harm categories and

None has undergone post-training, giving researchers flexible starting points for continued pre-training, fine-tuning, and further research. Two key highlights: - They use WSM to replace LR decay with weighted checkpoint merging, making the training process better suited for continual pre-training while enabling offline exploration of different LR decay strategies. - With one shared training recipe, the community can validate strategies on tiny-base, then scale them to flash-base. #1- Ling-3.0-t

The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing operations and fall short in supporting compositional instruction-guided video editing. In particular, multiple editing intents must be jointly understood and faithfully executed within the same video. To address this issue, we introduce CoinVE-200K, a large-scale, high-quality dataset for Compositional Instruction-Guided Video Editing. CoinVE-200K co

Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling increases compute cost and inference latency. Mixture-of-Experts (MoE) architectures offer a compelling alternative, having enabled efficient scaling in LLMs, yet the MoE design space for CLIP-style vision encoders remains underexplored at State-of-the-Art (SOTA) levels. In this work, we systematically study MoE designs for vision encoder scaling

Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize task-specific datasets in isolation. A central challenge is not only how to curate each task-specific corpus, but also how to organize heterogeneous supervision according to the dependencies among generative capabilities. We present a capability-driven data infrastructure that couples capability-specific supervision construction with capabi

Flock’s surveillance cameras have already sparked outrage. WIRED reconstructed its next-generation AI system, already in use by some police, to confirm it goes much further than tracking license plates.

The launch of the new study features marks Google's latest effort to make Gemini the AI assistant that students turn to when learning and studying, as it continues to compete with companies like OpenAI.

arXiv:2608.16977v1 Announce Type: new Abstract: AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited resource, and expert mathematical review is even more sharply constrained. Allocating these scarce resources well is therefore central to making AI-assisted mathematical discovery efficient. In most current AI-for-math workflows, human effort is concentrated at the beginning and end, in selecting suitable researc

High-resolution image editing is increasingly demanded in professional workflows, yet existing diffusion-based models remain constrained to resolutions below 1K due to quadratic attention complexity and prohibitive memory requirements. A prevalent workaround employs a two-stage pipeline: editing at low resolution followed by independent super-resolution. However, this approach suffers from two critical issues: information divergence, where hallucinated details contradict the original high-resolu

arXiv:2608.17150v1 Announce Type: new Abstract: To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content to a user's evolving understanding and cognitive capacity. Yet user simulators used to evaluate and train LLMs do not explicitly model user knowledge so they neither produce realistic interactions across knowledge levels nor reflect how interactions unfold as that knowledge evolves. To close this ga

arXiv:2608.17170v1 Announce Type: new Abstract: Algorithm selection for constraint satisfaction problems requires extracting features that capture problem structure. Manually designing feature extractors demands deep domain expertise and quickly becomes a bottleneck when new problem classes appear. We present an automated approach that uses Large Language Models (LLMs) in an agentic check--fix--verify loop to synthesize executable Python scripts that act as interpretable, problem-specific featur

AI agents that run in a loop Discussion | Link

Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely on resource-intensive conditioning, using either heavyweight branches or costly source concatenation. Is there any efficient way to model editing intent? Thus, we introduce GRNEdit, a lightweight two-stage framework. GRN inspires our approach by encoding visual semantics through combinations of bits. Through task-specific fine-tuning, we take this

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. We still don’t know how people are really using AI AI companies like Anthropic and OpenAI regularly publish reports on how people are using their products. But they only release the…

AI workspace for reproducible scientific research Discussion | Link

用 4× RTX 3060 12GB 跑 DeepSeek V4 Flash Q4_K_XL，在 360k–376k 上下文窗口下达到约 100 tok/s 提示处理速度。

ByteDance Seed and Tsinghua AIR have released CUDA Agent, an agentic reinforcement learning system that trains a large language model to write GPU kernels that beat a compiler. The gap it targets is narrow but stubborn: frontier models already produce correct CUDA, they just produce slow CUDA. On KernelBench, the base model Seed1.6 passes 74.0% […] The post ByteDance Seed and Tsinghua AIR Introduces CUDA Agent: A Large-Scale Agentic RL System for CUDA Kernel Generation appeared first on MarkTech

Long-horizon agents can fail even when their underlying models can solve the constituent steps. They may lose track of mutable state, fail to reactivate lessons from earlier executions, skip known procedures, or stop prematurely. We bet on harness scaling to improve the execution system around an agent without changing its model weights. We introduce StateM, an agent-native runtime that organizes execution around durable states, phase-local context, checked transitions, recoverable runbooks, and

🤖StateM is a runtime system that improves long-horizon agent execution through durable states, recoverable runbooks, and enforceable procedural controls without altering model weights.

Secret parameter allowed hackers to steal passwords when a target clicked on a link.