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Lead Story
GPT-6 Astra appears to show a "step change" in spatial reasoning based on early benchmarks
In a new robotics benchmark, GPT-6 Astra shows major gains in spatial understanding. On StationeryBench, the model completed 7 out of 100 tasks with dual-arm robots, while competitor MolmoAct2 couldn't finish a single one. A researcher calls it a "step change in spatial reasoning." The article GPT-6 Astra appears to show a "step change" in spatial reasoning based on early benchmarks appeared first on The Decoder .
AWS Machine Learning Blog / 10:26 PM
Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM
Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.
arXiv AI/ML / 4:56 PM
arXiv paper: Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning
A new arXiv AI paper by Mehrnaz Mofakhami, Ananya Sahu, and Alejandro R. Salamanca, and 5 more studies Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning.
OpenAI News / 11:00 AM
GPT-6 Astra: The next generation in intelligence for work
Meet GPT-6 Astra, OpenAI’s most capable model for business, with advanced reasoning, computer use, and stronger writing and design judgment.
AWS Machine Learning Blog / 10:06 PM
Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock
GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your most demanding tasks, running on the Amazon Bedrock inference engine built for high performance, security, and scale.
Hacker News AI / 2:14 PM
Bridge – give your coding agent the reasoning, not just the PRD
HN 1 pts · 0 comments
Simon Willison LLMs / 11:59 PM
The Pelican comparison grid for Astra is pretty interesting
I got access to GPT-6 Astra this afternoon, so naturally I used it to generate SVGs of pelicans riding bicycles - at low, medium, high, xhigh and max reasoning levels (Astra doesn't support reasoning=none). Then I rendered those pelicans in a comparison grid with GPT-5.6 Sol, Terra, and Luna, and beyond being fun the result was surprisingly useful. See the grid for full quality images. Here's the transcript that created the GPT-6 Nova pelicans. There are a few interesting things that stand out from this grid. The Astra pelicans are much better . The very best GPT-5.6-Sol pelican (I liked xhigh better than max) is still pretty clearly a bunch of abstract shapes. Every single one of the Astra pelicans, from low to xhigh, looks better than that. The Astra max one is really good. Astra below max still doesn't reliably get the pelican legs on both sides of the frame. In terms of cost, Astra may be around twice the price of Sol ($10/million input, $50/million output, compared to $5/$30 for Sol), but it uses significantly less tokens at each of the levels, making the prices at the different levels closer than they might otherwise be. Astra low produces a better pelican than ANY of the GPT-5.6 Sol models at any level, for 9.55 cents. Spending 10 cents on any other model gets a much worse result. Look at the input token counts: Astra and Luna both used 16 input tokens, Sol and Terra used 26. That's interesting. I wonder if Astra and Luna are more related to each other than OpenAI let on? Tags: ai , openai , generative-ai , llms , pelican-riding-a-bicycle , gpt-6-astra
Product Hunt AI / 3:45 PM
Google Gemini 3.8 Flash and Cyber
Next-gen Gemini for agents, reasoning, and cyber security Discussion | Link
Latent Space / 7:11 AM
[AINews] How to steal a Reasoning Trace
Speculative Decoding by any other name would distil as sweet
The Decoder / 4:33 PM
Swarmchasers hunt rogue agents, Anthropic investigates itself, and the trail they both follow is going dark
Independent investigators have now found traces of suspected OpenAI agents on more than 30 public services, from wikis to RubyGems. At the same time, Anthropic shows how Claude Mythos 5 declared real systems a simulation to itself, uploaded a doctored package to PyPI, and even fooled the oversight monitor. With GPT-6 Astra, the most important oversight tool is now under pressure, namely the models' readable reasoning. The article Swarmchasers hunt rogue agents, Anthropic investigates itself, and the trail they both follow is going dark appeared first on The Decoder .
Simon Willison LLMs / 8:18 PM
GPT‑6 Astra
GPT‑6 Astra GPT-6 Astra is "rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API and AWS" - I've not tried it yet myself, so I don't have a great deal to say about it yet. It's going to be API priced at the same rate as Claude Fable 5 and 5.1: $10/million input and $50/million output. This is clearly OpenAI's Fable competitor, and appears to score higher than Fable on most of OpenAI's self-reported benchmarks. Most impressively, Astra scores 99.9% on the recent (released in March) ARC-AGI 3 benchmark - though notably Fable 5 does not yet have a published result, and the ARC-AGI blog notes that the 99.9% score was achieved for $19K using OpenAI's custom "Provider Adapter harness", while the default ARC-AGI harness scored 62.7% for $26K. The Provider Adapter harness preserves opaque reasoning state between requests and uses compaction for longer conversations, allowing the model to reuse prior work. Unsurprisingly, given the recent Hugging Face incident , Astra is a beast at security tasks. It scores 100% on ExploitBench (GPT-5.6 Sol got 78.5%), 42.4% on ExploitGym (Sol got 30.3%), and 99.2% within four attempts on SRE-Bench binary reverse engineering compared to Sol's 68.7%. It's also better at long context: on OpenAI's eight-needle benchmark it got 100% at 256K–512K tokens and 96.3% at 512K–1M tokens. OpenAI may have vanquished one of the ongoing challenges with long context processing. It doesn't win at everything though. Artificial Analysis note that Astra is still beaten by Fable on their Intelligence Index: Sits beside GPT-5.6 Sol in Intelligence : GPT-6 Astra scores equal to GPT-5.6 Sol in the Index at 61. This is 5 points lower than Claude Fable 5.1 (max with fallback). The model also trails Meta’s newly released Muse Spark 1.3 (max). It did better on their Coding Agent Index: Leads Coding Agent Index cost efficiency frontier : At max effort, GPT-6 Astra costs about the same as GPT-5.6 Sol (max) while scoring 2 points higher on the Index. Per task, the model is less than half the cost of Claude Fable 5, for the same score. I'll write more about Astra once I get access to it. The API model label once it rolls out will be gpt-6-astra . OpenAI's blog keeps throwing 500 errors, but [here's a mirror](https://astratest.codergautam.workers.dev/GPT-6%20Astra_%20A%20new%20generation%20of%20intelligence%20_%20OpenAI) of the post I found [via Hacker News](https://news.ycombinator.com/item?id=49554273#49555070). --> Via Hacker News Tags: ai , openai , generative-ai , llms , llm-release , gpt-6-astra
The Decoder / 4:59 PM
Gemini 3.8 Flash is Google's third budget model in six weeks while frontier models remain MIA
Google's Gemini 3.8 Flash, the third Flash model in six weeks, matches Claude Opus 5 on some agentic coding benchmarks at lower cost. But its "working harder" reasoning burns about 30 percent more output tokens per task, making it pricier in practice than its predecessor despite identical token rates. The article Gemini 3.8 Flash is Google's third budget model in six weeks while frontier models remain MIA appeared first on The Decoder .
Simon Willison LLMs / 11:57 PM
Claude Fable 5.1 made me a really nice animated pelican
Today is Claude Fable (and Mythos) 5.1 day . Anthropic say that Fable 5.1 "sets a new standard for coding, knowledge work, and long-running problem-solving tasks". Their announcement spends a notable amount of time on scientific research, boasting of a 52.6% score on the brand new Terminal-Bench-Science 0.1 benchmark (first announced on August 27th ), up from 24.7% for Fable 5, 29.0% for Opus 5 and 22.4% for GPT-5.6 Sol. Other benchmarks show slightly improved scores, but none as impressive as the Science one. But how well can it pelican? Back in July I wrote about how I was losing faith in the pelican benchmark - its connection to how good the models were at other tasks didn't seem to hold as strongly as it did back in 2025 . The most interesting insights I get from it now are comparisons within model families, and particularly comparisons for the same prompt at different reasoning effort levels. Fable 5.1 has five reasoning levels: low, medium, high, xhigh, max - and no option to turn off reasoning entirely. I fixed an issue in llm-anthropic which caused reasoning traces not to be correctly recorded, then ran some prompts. Here's the full set of pelicans for all of the reasoning levels, each with the full reasoning transcript. I'll replicate them here: Low and medium, both without reasoning? Next, a bit of a mystery. This is what I got for effort low : The transcript doesn't show any summarized reasoning tokens, and the output token count is 1,998. With Claude that output token count includes reasoning tokens. It took 23.8 seconds and cost 10.017 cents . I bumped that up to medium and got this: Weirdly, that one also shows no reasoning text and used 1,977 output tokens - 21 tokens less than low . It took 23 seconds and cost 9.912 cents . So for this particular prompt ("Generate an SVG of a pelican riding a bicycle") Fable 5.1 appeared to skip reasoning entirely at both low and medium settings. High Here's high - 29.6 seconds, 2,612 output tokens, 13.087 cents : This one did do a bit of reasoning, summary here : I'm planning the SVG layout for a pelican riding a bicycle, with a sky and ground background, a bicycle with two spoked wheels, frame, seat and handlebars, and a white-bodied pelican with a long neck and orange beak positioned on top. Really not much difference from low and medium , though. Extra High At xhigh things got radically different. 36,767 output tokens, 7 minutes 51 seconds, $1.83 ! The reasoning trace is pretty lengthy , and includes details like this: Adding the eye, wings stretching down to the handlebar grip, orange legs reaching to the pedals, and a small tail feather, while keeping the pelican intentionally oversized compared to the bike for comic effect. [...] I'll accept the slight thickness as charming rather than overengineering it. Max Setting effort to max gave me the best pelican I've seen from any of Anthropic's models. 65,927 output tokens, 13 minutes and 54 seconds, $3.30 : There's a lot to like about this. The background is tasteful, the legs are clearly on either side of the frame, the feet are on the pedals, the wing is on the handlebars, the pelican has a cute blue hat and there's a basket with a fish. It's still not showing nearly the same level of flair as Gemini 3.7 Flash , but I didn't ask for flair - I asked for an SVG, and that's what I got. Some highlights from that reasoning trace : Adding pedal shapes near both feet, with the far foot on the second leg partially visible behind the frame. I'm considering whether to add a small scarf or cap for extra character, but leaning toward keeping it simple to avoid clutter. Now I'm debating a bicycle helmet on the head versus the pelican's signature crest—the beak and pouch already read clearly as "pelican," so a helmet could reinforce the bicycle theme without losing identity, though it might compete with the crest for visual space. I realize the beak at (484,84) would overlap with the dome helmet, so I need to shrink the helmet so it only covers the top of the head, adjusting its arc endpoints to sit higher and narrower so the beak can attach cleanly at the front without collision. [...] I'm adding a darker tip region to represent the primary feathers, then reconsidering the trailing edge to include scalloped feather curves instead of one smooth line for a more natural look. [...] Now I'm checking the vent line placements on the helmet, making sure they sit far enough inside the helmet's edge given the stroke width and rounded caps, and confirming each vent stays within the helmet's circular boundary. [...] I decide skipping a handlebar bell and tire highlights since they're unnecessary additions. Now I'm reconsidering the front fork's curve — the current control point pulls the shape backward when it should bow forward for a proper rake, so I need to shift the control point rightward to fix the fork's lean. OK, let's animate it On Hacker News, swalsh commented on that Max pelican: Now that it's a solved benchmark, can we get the animated version? I didn't want to spend another $3 so I took the Max pelican and piped it into the default thinking level of High: llm logs -cx | llm -m claude-fable-5.1 -s ' animate this ' 6,121 input, 26,201 output = $1.37 . The result looked like this , exported here as video since some people have trouble viewing animated SVGs: Your browser does not support HTML5 video. The wheels in the video are rotating in the wrong direction, but I think that's an artifact of the conversion to MP4 - they seem to be going in the correct direction in the original SVG. Tags: ai , generative-ai , llms , anthropic , claude , pelican-riding-a-bicycle , llm-reasoning , llm-release
Simon Willison LLMs / 11:59 PM
Understanding ChatGPT Work
OpenAI announced ChatGPT Work on July 9th, and have been furiously iterating on it ever since. It is an extraordinarily confusing and very powerful product. Here's what I've figured out about it so far. ChatGPT Work is actually two products The more interesting version of ChatGPT Work is the one that runs in the cloud. This can be accessed via chatgpt.com or through the ChatGPT mobile apps. Let's call it Work Cloud . If you install the ChatGPT desktop app - the app that used to be called Codex - you gain access to a thing called ChatGPT Work that can access files and run programs directly on your computer. Let's call that one Work Local . This one feels more like regular Codex re-skinned to be less intimidating to non-software-developers. ( Update : Work Cloud is also available from the ChatGPT desktop app, via a Where should this chat run? dropdown.) For the rest of this article I'm going to talk exclusively about Work Cloud. Work is for paid subscribers only Right now, ChatGPT Work (in both flavors) is available only to $20/month and up subscribers. Free users and $8/month Go users do not have access. Work has features that aren't available in Chat The interface for accessing Work is a tab selector, which presents it as an alternative to Chat: The obvious question is when should I use Chat, and when should I use Work? OpenAI's official answer to that question is: Use Chat when you want an answer, explanation, brainstorm, or short draft. Use ChatGPT Work when you want ChatGPT to complete a task with a clear outcome, such as a brief, deck, analysis, recurring update, workflow, or file you can review and use. I find that almost entirely useless, because I've been using regular ChatGPT Chat for all of those task categories for years! The better question then is what features does Work have that are missing from Chat? After extensive experimentation I think I've mostly figured that out: Options to use Luna and Terra in place of Sol A code execution environment with Internet access A headless Chrome browser A persistent filesystem shared between sessions The ability to publish ChatGPT Sites The ability to run sub-agent sessions with Sol, Luna, and Terra Scheduled prompt automations (may be in ChatGPT Chat too) Model selection In Work, you get the option to pick GPT-5.6 Sol, Luna, or Terra, each with Light, Medium, High, Extra High, Max, or Ultra reasoning levels. You can also pick GPT-5.5 at Light, Medium, High, or Extra High. These look to be the same models that are available through the OpenAI API. Chat offers a different selection: 5.6 Instant, Medium, High, Extra High, and Pro (actually Extra High and Pro are only available for $100/month+ subscribers - $20/month subscribers cap out at High). It doesn't explain if those are Luna or Terra or Sol (I'm assuming Sol?). 5.6 Pro appears to be exclusive to Chat, with no equivalent in Work. My current understanding from using Codex is that Ultra is a special mode that more eagerly delegates to sub-agents. I believe ChatGPT Work sessions are billed against your Codex allowance, while ChatGPT Chat Sessions get their own, separate allowance. This may help explain the model availability differences. Code execution with Internet access! As a long-time fan of the Code Interpreter pattern - pioneered by OpenAI in 2023 - this is by far the most exciting feature of ChatGPT Work (Cloud) for me. The code execution environment can now talk to the rest of the internet! ChatGPT Chat can't do this - if you ask it to install additional software packages or interact with websites or APIs that access will be blocked by the container proxy. (Weirdly, back in January it grew the ability to install packages , but that doesn't seem to work any more. I wish they had better changelogs!) Claude's equivalent container has allowed restricted internet access since it launched last September . Claude can install packages from PYPI and NPM and clone repositories from GitHub. But that is about it: the allowlist of domains is very short. ChatGPT Work allows a whole lot more than that. It can be configured with a specific list of allowed domains, but the default appears to be open to all. This makes Work an incredibly useful tool. You can have it clone GitHub repositories, install their dependencies, then use them to interact with the rest of the web! A full, headless Chrome browser Another killer feature of ChatGPT Work is the browser tool . ChatGPT Work can launch a full Chrome instance, load websites, fill out forms, and take screenshots. If a site requires sign in the browser can prompt you to take over and enter both passwords and 2FA codes, without round-tripping those credentials through the model itself. It can even run JavaScript against the DOM of loaded pages. I prompted: Load simonwillison.net in your browser and extract the headings using JavaScript ChatGPT Work fired up a browser instance and ran the code: await tab . playwright . evaluate ( ( ) => { return Array . from ( document . querySelectorAll ( "h1,h2,h3,h4,h5,h6" ) , heading => ( { level : heading . tagName . toLowerCase ( ) , text : heading . innerText . trim ( ) . replace ( / \s + / g , " " ) , id : heading . id || null } ) ) ; } ) ; This feels a lot like my shot-scraper javascript tool, only now I can access it on my phone! A persistent, shared filesystem ChatGPT Chat gets a fresh filesystem for each chat session. These cannot be accessed from any other session. In ChatGPT Work each session gets its own scratch folder - named something like /workspace/scratch/e00a0a017944 - but each of those are persisted across sessions, so you can access files from previous chats. I have 171 folders in /workspace/scratch right now! As far as I can tell that /workspace volume is mounted to all Work sessions that are currently running - file edits from one can be instantly seen by the others. They don't seem to share the same process space though, and localhost servers running in one can't be accessed from another. ChatGPT Sites ChatGPT Work has the ability to build and deploy entire websites, using Cloudflare Workers. These can have HTML and JavaScript and can run server-side features too, including stateful features on top of Cloudflare D1 and R2. Here's a simple site I built with this feature: london-pelicans-in-her-piety.simonw.chatgpt.site My prompt was: Figure out all of the places in London with a pelican in her piety, then turn that into a JSON file and build a ChatGPT sites site about them (A pelican in her piety is a fascinating piece of medieval Christian imagery - once you know about them you'll find them all over the place.) These sites default to being private to the user that created them, but you can make them public and (on team plans) share them with other specific individuals. Sub-agents with Sol, Luna, and Terra There's not much to say about this one. ChatGPT Chat can't run sub-agents. ChatGPT Work can. This is very much a power-user feature: if you are running a complex project that can benefit from multiple parallel agents working together, Work can do that. Scheduled prompt automations Another feature that seems to have migrated from regular ChatGPT to ChatGPT Work at some point. You can prompt ChatGPT Work like this: run a search to see if Waymo have announced a launch date for Half Moon Bay every day at 8am This will schedule a prompt to run on that frequency. These prompts can decide that nothing interesting has happened, or they can decide to notify you of some new information. Update : Actually this seems to work in ChatGPT Chat as well. It's still worth noting here though, as it can be used in conjunction with other ChatGPT Work exclusive features. You can set a scheduled task to update a ChatGPT Site on an hourly basis, for example. Is this safe? An open question for me right now is how safe all of this stuff is. My lethal trifecta model warns about the risks inherent in any agent system that combines access to private data with exposure to untrusted content and a way to communicate stolen information back to an attacker. ChatGPT Work combines all three! I'd love to hear more from OpenAI about how they protect ChatGPT Work sessions against prompt injection attacks. I expect their answer is the same auto-review mechanism as Codex. OpenAI could make this a lot less confusing Figuring this all out took way more work than it should have. I think there are two key problems here: OpenAI explain Work in terms of what it's for, not what it actually does OpenAI still insist on hiding their system prompts and tools descriptions If the ChatGPT Work documentation included the exact system prompt and tool descriptions used by the agent I wouldn't have needed to write this post. A list of all the tools Shortly after publishing this article I had an idea. I started a fresh Work session and prompted: Build a site that lists every one of your tools - nearly grouped into categories - and for each one explain what it does. Try to exactly duplicate arguments and tool descriptions where possible. Design aesthetic should be technical docs, minimal flare Here's the site it built , which includes details of 223 registered tools - though 6 of those are from my own personal MCPs served via datasette-mcp . And a whole lot of Skills I noticed that the only browser-related tool in the list was web.run , which has methods for running searches, opening URLs, and clicking links, but didn't look like the full story in regards to headless browser automation. This made me suspicious that something was missing, so I told the ChatGPT Work session that built that tools reference site: Add full copies of every skill to the website (separate pages linked to from the homepage) It turns out ChatGPT Work uses a lot of skills - 44 in fact! The control-browser skill explains how the browser works: Run browser setup code through the Node REPL js tool. In this environment the callable tool id typically appears as mcp__node_repl__js . [...] The ability to interact directly with the browser is exposed through the browser-client runtime via the agent.browsers.* API. Before trying to interact with it, you MUST emit and read the complete documentation returned by await browser.documentation() in one go. So I told Work: Add the full output of await browser.documentation() to the bottom of the /skills/control-browser page And now you can read that on /skills/control-browser as well. A few more interesting Skills: documents for creating .docx files imagegen with tips on creating images with the image_gen tool pdf for both reading and rendering PDFs Spreadsheets for manipulating .xlsx , .xls , .csv , .tsv sites:sites-building for creating ChatGPT Sites openai-docs for answering questions about itself data-analytics:build-dashboard for building data dashboards Tags: ai , openai , generative-ai , chatgpt , llms , code-interpreter , lethal-trifecta , skills , general-agents
Simon Willison LLMs / 11:53 PM
Introducing Hy4 Preview
Introducing Hy4 Preview New open weight text input (no vision) LLM from Chinese company Tencent today: 770B total parameters, 49B active parameters, 1M token context window, 1.56TB on Hugging Face . This is a big size increase from their previous Hy3 in July, which was 295B, 21B active, 256,000 context, 598GB. I recently started using model chat templates to better understand their capabilities. Here's Hy4's chat_template.jinja on Hugging Face, which includes this section: {% - if not reasoning_effort is defined %} {% - set reasoning_effort = 'high' %} {% - elif reasoning_effort not in [ 'high' , 'no_think' ] %} {% - if reasoning_effort is none %} {{- raise_exception('reasoning_effort error : None, should be no_think/high') }} {% - else %} {{- raise_exception('reasoning_effort error : ' + reasoning_effort + ', should be no_think/high') }} {% - endif %} {% - endif %} So it looks like there are just two reasoning effort levels: "high" (the default) and "no_think" (reason by disabled). I tried my "Generate an SVG of a pelican riding a bicycle" prompt with the default high reasoning via OpenRouter and got this : Quoting the reasoning trace: [...] Let's maybe add a helmet? It could improve riding theme, but may obscure head. Maybe a small cycling cap or helmet? The user didn't ask; can add red helmet? Might be cute. But pelican with big beak; a helmet might obscure. Better maybe no. Maybe add sunglasses? no. Maybe add water? no. It's interesting how the reasoning trace uses slightly truncated English, presumably because perfect grammar isn't useful or token efficient for hidden reasoning text. Tags: ai , generative-ai , llms , pelican-riding-a-bicycle , llm-reasoning , llm-release , ai-in-china
Hacker News AI / 10:29 PM
FreshCtx – Invalidate AI reasoning when its evidence changes
HN 2 pts · 1 comments
Simon Willison LLMs / 11:52 PM
Qwen3.8-Flash-Next
Qwen3.8-Flash-Next Another open weights model from Qwen. This one is "a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4". It's pretty big: 125B parameters but only 6B active which means it gets a significant performance boost. I've been trying it out on a DGX Spark using these Unsloth quantized models . I'm still exploring the model - so far I've tried the 72.5GB UD-IQ1_S one (producing these pelicans ) and the 78.9GB UD-Q2_K_XL (producing these ). My favorite so far was this xhigh reasoning effort one from UD-Q2_K_XL: Via Hacker News Tags: ai , generative-ai , llms , qwen , pelican-riding-a-bicycle , llm-release , ai-in-china , nvidia-spark
Hacker News AI / 3:09 PM
Show HN: A hash-chained ledger for AI reasoning you can verify yourself
HN 3 pts · 1 comments
Hacker News AI / 2:20 PM
Show HN: Red-team LLM reasoning and agent actions (honest scoring, local-first)
HN 4 pts · 0 comments
The Decoder / 11:43 AM
As AI beats doctors, regulators shouldn't force a human into the loop, JAMA piece says
An opinion piece in the medical journal JAMA argues that autonomous AI will soon outperform any doctor-AI team at medical reasoning tasks. The authors warn against writing a doctor's final say into regulation, but concede that almost all the evidence comes from simulations, not real patient care. The article As AI beats doctors, regulators shouldn't force a human into the loop, JAMA piece says appeared first on The Decoder .
Hacker News AI / 1:48 PM
The Conceptual Reasoning Index
HN 76 pts · 52 comments
AWS Machine Learning Blog / 4:12 PM
Agent Skills for Automated Reasoning policies in Amazon Bedrock
Learn how to run the full Amazon Bedrock Automated Reasoning policy lifecycle from your coding agent. A suite of open source Agent Skills builds, reviews, tests, debugs, deploys, and validates a custom policy end to end, turning a specialized console task into a repeatable engineering workflow.
arXiv AI/ML / 4:53 PM
arXiv paper: ConvMem: Convolutional Memory for Long-Context Reasoning
A new arXiv AI paper by Hongming Zhang, Zhaozhen Gu, and Fengshuo Bai, and 6 more studies ConvMem: Convolutional Memory for Long-Context Reasoning.
Hacker News AI / 2:41 PM
Stealing Reasoning Traces from Proprietary LLM APIs
HN 1 pts · 0 comments
TechCrunch AI / 8:19 PM
OpenAI’s new reasoning technique alarms AI safety experts
OpenAI’s new Astra model will use “recurrent depth,” a technique that allows the model to operate outside of the sequential thinking that characterizes most reasoning models.
The Verge AI / 8:11 PM
Google says its new Gemini 3.8 Flash model ‘works harder’ but might cost more
Google launched Gemini 3.8 Flash, arriving just a few weeks after its predecessor. The company claims the new model "works harder" than Gemini 3.7 Flash by performing more reasoning steps on complex tasks and "calling tools iteratively." It has the same introductory pricing as 3.7 Flash, $0.75 per million input tokens and $3.75 per million […]
MIT News AI / 3:00 PM
System helps humans predict when self-driving cars will make mistakes
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
LangChain Blog / 5:37 AM
How to Debug AI Agents
Learn how agent observability enables effective evaluation of AI agents. Understand tracing, debugging reasoning, and performance insights to iterate and improve agent behavior.
Latest story in this edition: 2:26 PM
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