coding patterns are bimodal: in 41% of sessions, agents author virtually all committed code ('vibe coding'), while in 23%, humans write all code themselves.

The overall conclusion, therefore, is that AI for Science should be understood as both a scientific and a civilizational project.

The central question is not whether AI can imitate human conversation, but whether it can participate in the production of publishable scientific knowledge at a level comparable to a recognized human contributor.

Without a mechanism for continuous and diverse learning, AI systems will tend to reproduce the dominant patterns already present in their training data. That limitation would make truly creative work difficult.

The most effective pattern of human-AI cooperation may differ substantially across disciplines, and these patterns will likely be discovered through practice rather than designed in advance.

If publication becomes more agentic, it may create new ways to recognize and evaluate such contributions. Although the final form of such a system remains uncertain... the evaluation and reward structure of academia will change in a fundamental way.

The application of LLMs in science is already underway... We believe that AI will ultimately bring a fundamental big change to scientific research across disciplines.

The most fundamental change brought by the LLM revolution is that human know-how is becoming replicable and shareable at scale.

The results demonstrate consistent improvements over strong baselines, supporting the effectiveness of agent resource management and closed loop self evolution.

Building on AGP, we present Autogenesis System (AGS), a self-evolving multi-agent system that dynamically instantiates, retrieves, and refines protocol-registered resources during execution.

Its Self Evolution Protocol Layer (SEPL) specifies a closed loop operator interface for proposing, assessing, and committing improvements with auditable lineage and rollback.

Its Resource Substrate Protocol Layer (RSPL) models prompts, agents, tools, environments, and memory as protocol registered resources with explicit state, lifecycle, and versioned interfaces.

We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs.

existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code.

Building on AGP, we present Autogenesis System (AGS), a self-evolving multi-agent system that dynamically instantiates, retrieves, and refines protocol-registered resources during execution.

Its Resource Substrate Protocol Layer (RSPL) models prompts, agents, tools, environments, and memory as protocol registered resources with explicit state, lifecycle, and versioned interfaces.

However, existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code.

Scan your website to see how ready it is for AI agents. We check multiple emerging standards — from robots.txt and Markdown negotiation to MCP, OAuth, Agent Skills and agentic commerce.

We want email agent tooling to be composable and reusable. Rather than every team rebuilding the same inbound-classify-reply pipeline, start with this reference application.

Each agent gets its own identity from a single domain. The address-based resolver routes support@yourdomain.com to a 'support' agent instance, sales@yourdomain.com to a 'sales' instance, and so on.

The inbox becomes the agent's memory, without needing a separate database or vector store.

A chatbot responds in the moment or not at all. An agent thinks, acts, and communicates on its own timeline.

Email is the most accessible interface in the world. It is ubiquitous. There's no need for a custom chat application, no custom SDK for each channel.

frontier AI models are not too big because the technology is complex and too big because the training data is garbage

The immediate danger is not that machines will act without human oversight; it is that human overseers have no idea what the machines are actually 'thinking.'

Cursor still uses and sells access to Claude and GPT models even as both firms roll out their own coding tools, an awkward arrangement that this new SpaceX partnership may be designed to eventually escape.

Either figure would represent a significant expense for SpaceX, which is widely seen to be losing money following the acquisition of xAI and the social media network X and is planning extensive capital investment.

The deal won’t shock those who follow the industry closely. Last week, it was reported that xAI would begin renting computing power from its data centers to Cursor, with the coding startup using tens of thousands of xAI chips to train its latest AI model.

Neither Cursor nor xAI has proprietary models that can match the leading offerings from Anthropic and OpenAI — the same companies now competing directly with Cursor for the developer market.

Members have been using Mythos regularly since gaining access — providing screenshots and a live demonstration of the model as evidence to _Bloomberg_ — though reportedly not for cybersecurity purposes in an attempt to avoid detection by Anthropic.

The group accessed Mythos by using knowledge of Anthropic’s other model formats obtained from a recent [Mercor data breach](https://www.theverge.com/ai-artificial-intelligence/907083/a-company-that-makes-ai-training-data-has-been-hit-by-a-security-breach) to make “an educated guess” about its online location.

Official access to the model is limited to a handful of companies through the [Project Glasswing initiative](https://www.theverge.com/ai-artificial-intelligence/908114/anthropic-project-glasswing-cybersecurity), including Nvidia, Google, Amazon Web Services, Apple, and Microsoft.

Anthropic currently has no plans to release the model publicly due to concerns that it could be weaponized.

It also surpasses all peer-scale dense models by a wide margin.

With only 27B parameters, it outperforms the Qwen3.5-397B-A17B (397B total / 17B active) on every major coding benchmark

It also surpasses all peer-scale dense models by a wide margin.

TPU 8i is designed with more memory bandwidth to serve the most latency-sensitive inference workloads, which is critical because interactions between agents at scale magnify even small inefficiencies.

Both chips can run various workloads, but specialization unlocks significant efficiencies and gains.

By customizing and co-designing silicon with hardware, networking and software, including model architecture and application requirements, we can deliver dramatically more power efficiency and absolute performance.

These two chips are designed to power our custom-built supercomputers, to drive everything from cutting-edge model training and agent development, to massive inference workloads.

TypeScript 7.0 now performs many steps in parallel, including parsing, type-checking, and emitting.

We have reworked our JavaScript support to be more consistent with how we analyze TypeScript files.

The stable release of TypeScript 7.0 will be published under the `typescript` package and will use the `tsc` entry point.

The new Go codebase was methodically ported from our existing implementation rather than rewritten from scratch.

We spent days loading the system with hundreds of threads, refining rough edges and polishing corners that developers may never see.

Multi-agent orchestration isn't new, but we believe we've built a great experience for working with agents at scale.

What we've found works best for crafting high-quality software is somewhere in between: using AI, and also engaging directly with code.

At one extreme, there's [fully giving into the vibes], and at the other extreme, there's [disabling all AI features].

Ask ten different programmers how they use AI, and you can get ten different answers.

Meta is not alone in pursuing such a vision: Anthropic debuted tech capable of doing this [in 2024] and OpenAI last year announced [“Operator”] – a tool that can use a web browser on a human’s behalf.

Meta, the company built on watching everything its billions of users do online so it can keep them clicking on ragebait and targeted ads, is reportedly now installing surveillance software on employees’ work computers.

Reasoning models are generally assumed to be better at coding tasks, and they do score higher on Pass@1.

While the output passes the tests and is functionally correct, the diff is enormous, and none of those additions were asked for or even necessary.

A common piece of advice for working with AI coding tools is to simply write more tests because if the tests pass, the code is fine.

Because agents have memory and can be guided and corrected in conversation, they get better as teams use them.

They run in the cloud, so they can keep working even when you’re not.

Workspace agents will be free until May 6, 2026, with credit-based pricing starting on that date.

Because agents have memory and can be guided and corrected in conversation, they get better as teams use them.

AI has already helped people work faster on their own, but many of the most important workflows inside an organization depend on shared context, handoffs, and decisions across teams.

And it’s not just office work. Multi-agent tools like Google DeepMind’s Co-Scientist let researchers use teams of AI agents to coordinate literature searches, generate and test hypotheses, design experiments, and more.

Think of multi-agent systems as the new assembly lines. Henry Ford’s innovation upended entire industries last century. In theory, networks of AI agents could do to white-collar knowledge work what assembly lines did to manufacturing.

That matters because AI hype is dying down, and companies are shifting focus from buzzy pilots to deployment and integration, where cheaper and more customizable tools tend to win.

US tech CEOs believe the best models should stay proprietary, partly so they can recoup enormous training costs and partly out of concern that powerful frontier models could be weaponized. Chinese labs, for their part, are not purely idealistic: Open-source is not only free advertising but also a shrewd workaround.

Chinese open-weight models accounted for 17.1% of global AI model downloads over the year ending in August 2025. That narrowly surpassed the US share of 15.86%—the first time China had led in this metric.

Telling people to avoid using generative AI is increasingly telling them they must avoid taking part in society.

We have not really begun to make this progress with AI. Why, for example, is this dashboard not found on a government website?

to stand out from the AI-generated pack we will need to become so weird and unexpected as to be off-putting to most people

The AI has learned to code. The AI is building itself.

Is this what we signed up for? Is today the day? Did the drones wake up? Did it achieve consciousness? Is it alive?

We have not really begun to make this progress with AI. Why, for example, is this dashboard not found on a government website?

to stand out from the AI-generated pack we will need to become so weird and unexpected as to be off-putting to most people

The 21st-century average American lies in bed staring at their phone. ... Talking for hours and ages to melted sand.

We do not plan to make Claude Mythos Preview generally available, but our eventual goal is to enable our users to safely deploy Mythos-class models at scale.

There will be more attacks, faster attacks, and more sophisticated attacks. Now is the time to modernize cybersecurity stacks everywhere.

In the past, security expertise has been a luxury reserved for organizations with large security teams. Open source maintainers—whose software underpins much of the world's critical infrastructure—have historically been left to figure out security on their own.

The window between a vulnerability being discovered and being exploited by an adversary has collapsed—what once took months now happens in minutes with AI.

AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities.

RED outperforms eight competitive baselines, achieving performance gains of up to 19.0% while reducing token consumption by 37.7% ~ 70.4%

alternative solutions are not merely suboptimal but potentially detrimental

We characterize errors as a forest-structured Forest of Errors (FoE) and conclude that FoE makes the First the Best

This observation challenges widely accepted test-time scaling laws, leading us to hypothesize that errors within the reasoning path scale concurrently with test time.

The First is The Best, where alternative solutions are not merely suboptimal but potentially detrimental.

For small entrepreneurs in the US, deciding what to sell and where to make it has traditionally been a slow, labor-intensive process that can take months. Now that work is increasingly being done by AI tools like Accio, which help connect businesses with manufacturers in countries including China and India.

Zhang, of Alibaba.com, says Accio currently does not include advertising. Suppliers can pay for higher placement in Alibaba.com's regular search results, but Zhang says Accio is 'not integrated' with that system.

Sellers say that while AI tools have made it easier to come up with ideas and get a business off the ground, they do not replace the core skills that make someone good at e-commerce.

Sally Li, a representative at a makeup packaging company in Wuhan, China, says her firm has started writing more detailed product descriptions and adding information about its equipment and manufacturing experience on Alibaba.com because it suspects those details make its listings more likely to be surfaced by AI.

McClary took the process from there, contacting the supplier himself to discuss the revised design. Within a month, the new version of the Guardian flashlight was back up for sale on Amazon and on his brand's website.

A three-stage progressive training strategy -- large-scale pre-training, hard sample fine-tuning, and GRPO alignment -- sequentially exploits these data at different quality tiers.

SOTA models of different architectures and parameter scales exhibit highly consistent failure patterns on the same set of hard samples, suggesting that the performance bottleneck stems from shared deficiencies in training data rather than architecture itself.

Current document parsing methods compete primarily on model architecture innovation, while systematic engineering of training data remains underexplored.

the design of the retrieval and cache policy, especially how they decide what to keep, reuse, or drop across scenes, seems to be what actually drives the latency and throughput gains

they fuse streaming data construction with a unified model so the memory supports both real-time q&a and long-horizon interaction, which is nontrivial under strict latency constraints

We release the AURA model together with a real-time inference framework to facilitate future research

It achieves state-of-the-art performance on streaming benchmarks and supports a real-time demo system with ASR and TTS running at 2 FPS on two 80G accelerators

current approaches often rely on decoupled trigger-response pipelines or are limited to captioning-style narration, reducing their effectiveness for open-ended question answering and long-horizon interaction

leading baselines achieve only about half the accuracy at the same efficiency

TriAttention enables OpenClaw deployment on a single consumer GPU, where long context would otherwise cause out-of-memory with Full Attention

TriAttention matches Full Attention reasoning accuracy while achieving 2.5x higher throughput or 10.7x KV memory reduction

queries rotate with position during RoPE, making representative queries very few, leading to poor top-key selection and unstable reasoning.

we use the distance preference characterized by these centers to score keys according to their positions, and also leverage Q/K norms as an additional signal for importance estimation

TriAttention enables OpenClaw deployment on a single consumer GPU, where long context would otherwise cause out-of-memory with Full Attention

TriAttention matches Full Attention reasoning accuracy while achieving 2.5x higher throughput or 10.7x KV memory reduction

queries rotate with position during RoPE, making representative queries very few, leading to poor top-key selection and unstable reasoning.

The government has so far favoured a pro-innovation, sector-led approach, prioritising voluntary principles over hard regulation.

The government has so far favoured a pro-innovation, sector-led approach, prioritising voluntary principles over hard regulation.

memory organized for future control improves delayed retrieval under cue conflict and load

fast local feedback plus predictive compensation improves robustness under hidden dynamics shifts

A downstream conjecture is that role-differentiated proposer/executor/checker/adversary systems may reduce correlated error under asymmetric information and verification burden.

We introduce a minimal hierarchical partially observed control model with latent dynamics, structured episodic memory, observer-belief state, option-level actions, and delayed verifier signals.

This article argues that squirrel ecology offers a sharp comparative case because arboreal locomotion, scatter-hoarding, and audience-sensitive caching couple all three demands in one organism.

Agentic AI is increasingly judged not by fluent output alone but by whether it can act, remember, and verify under partial observability, delay, and strategic observation.

verifiers and observer models inside the action-memory loop reduce silent failure and information leakage while remaining vulnerable to misspecification.

role-differentiated proposer/executor/checker/adversary systems may reduce correlated error under asymmetric information and verification burden.

We introduce a minimal hierarchical partially observed control model with latent dynamics, structured episodic memory, observer-belief state, option-level actions, and delayed verifier signals.

squirrel ecology offers a sharp comparative case because arboreal locomotion, scatter-hoarding, and audience-sensitive caching couple all three demands in one organism.

Existing research often studies these demands separately: robotics emphasizes control, retrieval systems emphasize memory, and alignment or assurance work emphasizes checking and oversight.

Agentic AI is increasingly judged not by fluent output alone but by whether it can act, remember, and verify under partial observability, delay, and strategic observation.

when setting up a new Macbook it presents FileVault as an optional checkbox and I can certainly tell you that there are many people (including my younger self and my family member with the Intel Mac) who do not know what it is and choose to disable it instead.

using "Open File..." dialog (`⌘+O`) you could still open and view any file on the system and could preview any file that safari could preview (e.g. `.html`, `.htm`, `.txt`, `.pdf`, and image files)

by "saving" the webpage (`file->save as`) instead of downloading it (which Safari automatically adds an extension for) I could force it to save it as `malicious_file` (with no extension).

macOS decides to boot the `Volumes` partition which includes `Data`, `Macintosh HD`, `macOS Base System`, and `Preboot` systems, and when you choose the `Macintosh HD` it allows you to save the file to the Mac's permanent disk.

Rather than treating a complex document as a single monolithic task, Deep Extract deploys sub-agents to break it down and conquer each piece, which is what allows it to remain accurate even on documents with thousands of rows across hundreds of pages.

We've seen customers go from 10-20% field accuracy with a frontier model to 99-100% just by switching to using Reducto's Deep Extract.

The issue isn't that models are bad at reading documents. It's that single-pass extraction has no mechanism to catch its own mistakes, and models get lazy.

For the documents that matter most, it gets to 99–100% field accuracy, even out-performing expert human labelers on extraction tasks.

The demand for these medications has been the most ferocious thing I have witnessed in my working life, and the hardest parts of running a telehealth company, like finding doctors and fulfilling prescriptions, can be entirely outsourced to platforms like CareValidate and OpenLoop.

Software, he argues, should be approached the same way. It's a new medium, and it deserves a native design language instead of hand-me-down forms from the physical world.

The consistent argument across the Every Slack was that if cache-breaking usage costs more to serve, make those users pay more: Meter the consumption rather than ban the interface.

Historically, AI evaluation has leaned toward the forest approach. Most researchers settle for 1 to 5 raters per item, assuming this is enough to find a single 'correct' truth.

The most encouraging finding is that one doesn't need an infinite budget. We found that by optimizing the ratings-per-item ratio correctly... one can achieve highly reproducible results with a modest budget of around 1,000 total annotations.

Moving from commentary to real impact in how this technology is distributed and understood globally is incredibly important to us.

TBPN will continue to run their programming, choose their guests, and make their own editorial decisions.

the standard communications playbook just doesn't apply to us

We're not a typical company. We're driving a really big technological shift.

we aim to cover as many methods as possible, the environment is relatively complex. This codebase primarily supports inference for different world model tasks

Despite limitations, 3D generation remains crucial for realistic physical simulation in world models

the memory module is where this design finally hits a sweet spot, separating persistence from real-time reasoning

OpenWorldLib integrates models across different tasks within a unified framework, enabling efficient reuse and collaborative inference

a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world

we have kept the memory modules separate for each pipeline — precisely so that memory can be better isolated and iteratively improved during early development.

our framework is still being refined, and the design of the memory module primarily draws from the description in Cambrian-S, implementing core memory expansion and management functions.

despite limitations, 3D generation remains crucial for realistic physical simulation in world models

OpenWorldLib integrates models across different tasks within a unified framework, enabling efficient reuse and collaborative inference.

we propose a clear definition: a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world.

Reconstructing raw inputs forces models to model irrelevant low-level detail. Predicting in a learned embedding space allows the model to focus on semantically meaningful, causally relevant features.

Whether or not this specific bet pays off, the underlying argument that the next meaningful leap in AI capability requires moving beyond language modeling is increasingly hard to dismiss.

AMI Labs is not building a product for immediate deployment. This is a fundamental research effort, likely measured in years before commercial applications emerge.

LLMs have no grounded understanding of the physical world. They model the statistical distribution of language about reality, not reality itself.

Whether or not this specific bet pays off, the underlying argument that the next meaningful leap in AI capability requires moving beyond language modeling is increasingly hard to dismiss.

The clustering of capital and talent around this problem is itself a signal. The applications that most clearly benefit from world models are those where LLMs have struggled most.

AMI Labs is not building a product for immediate deployment. This is a fundamental research effort, likely measured in years before commercial applications emerge.

LLMs have no grounded understanding of the physical world. They model the statistical distribution of language about reality, not reality itself.

in 2024, 47% of AI solutions were built internally and 53% were purchased; today, 76% of all AI is purchased rather than developed in-house.

You have to have people that have the ability to rethink the workflow at a scale that AI can execute, versus at a scale that humans can execute.

in 2024, 47% of AI solutions were built internally and 53% were purchased; today, 76% of all AI is purchased rather than developed in-house.

You don't need a separate agent API. You need to look at every `input()` call, every CWD assumption, every pretty-printed-only output, and ask: what if the user on the other end is a process, not a person?

Implicit state is the Enemy

The funny part is that none of this made the CLI worse for humans. The TUI picker still works and looks fancy, progress spinners still spin, confirmation dialogs still confirm. We just added a second door.

Every prompt is a flag in disguise

Designing for agents forced us to build better tools for everyone.

Implicit state is the Enemy

The funny part is that none of this made the CLI worse for humans.

Every prompt is a flag in disguise

Designing for agents forced us to build better tools for everyone.

Simultaneously, the parallelism in worker layer accelerates the speed of overall task execution, mitigating the significant latency

our framework enforces strict context isolation to prevent saturation and error propagation

The parallelism in worker layer accelerates the speed of overall task execution, mitigating the significant latency

By leveraging aggregation and reflection mechanisms at the Manager layer, our framework enforces strict context isolation to prevent saturation and error propagation

most existing large language model agent systems face severe limitations in data-intensive settings, including context saturation, cascading error propagation, and high end-to-end latency

Recent agentic search systems have made substantial progress by emphasising deep, multi-step reasoning. However, this focus often overlooks the challenges of wide-scale information synthesis

Consequently, they cannot verify if tools were actually invoked, applied correctly, or used efficiently.

To enable true process-level verification, we audit fine-grained intermediate states rather than just final answers, and quantify efficiency via an overthinking metric relative to human trajectories.

However, existing evaluations fall short: they lack flexible tool integration, test visual and search tools separately, and evaluate primarily by final answers.

during the inference phase, the framework invokes both memory mechanisms synchronously

the inherent limitations of such a single-paradigm approach pose a fundamental challenge for existing models

existing methods typically attempt to address both issues simultaneously using a single paradigm

our GTPO hybrid advantage formulation eliminates the advantage misalignment problem

We introduce Iterative Reward Calibration, a methodology for designing per-turn rewards using empirical discriminative analysis of rollout data

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

our approach improves Qwen3.5-4B from 63.8 percent to 66.7 percent (+2.9pp) and Qwen3-30B-A3B from 58.0 percent to 69.5 percent (+11.5pp)

naively designed dense per-turn rewards degrade performance by up to 14 percentage points due to misalignment between reward discriminativeness and advantage direction

We introduce Iterative Reward Calibration, a methodology for designing per-turn rewards using empirical discriminative analysis of rollout data

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

naively designed dense per-turn rewards degrade performance by up to 14 percentage points due to misalignment between reward discriminativeness and advantage direction

computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments

intermediate actions that appear locally acceptable but collectively lead to unauthorized actions

model alignment alone does not reliably guarantee the safety of autonomous agents.

computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments

intermediate actions that appear locally acceptable but collectively lead to unauthorized actions

model alignment alone does not reliably guarantee the safety of autonomous agents

一张参考图 + 一段分镜脚本直接出片

运镜不是孤立的视觉效果，而是跟剧情节奏配合的叙事工具

不是改画面元素，而是改角色在演什么

让你能像导演一样控制 AI 视频的每个环节

实际效果就是你的 Claude Code、Cursor 或任何支持 MCP 的 AI Agent，可以直接'看到' 𝕏 上的实时数据并执行操作，不需要自己写 API 封装。

原来的 Basic（$200/月）和 Pro（$5,000/月）方案仍然保留，可以在 Developer Console 里切换到按量付费。

AI Agent 可以通过标准 MCP 协议直接读取和操作 𝕏 平台：搜索推文、发帖、查看用户信息、管理书签、收发私信等。

购买 𝕏 API 信用额度时，按累计消费金额获得 xAI API（Grok）的免费额度

这次更新解决的核心问题是：𝕏 的 API 之前太贵了，也太难用了。