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GPT-6.2-Astra has started appearing in Codex usage breakdowns. The requests are likely being quietly routed to the new model as part of an A/B test or a staged rollout. A public launch could be days away. OpenAI reportedly decided to skip GPT-6.1 Astra due to concerns about deception and scope.
Anthropic disclosed in its updated IPO filing that it may soon reach its internal definition of recursive self-improvement (RSI), where AI systems help develop increasingly capable AI models.
Anthropic released message threads in public beta, allowing agents to send only new messages instead of resending their entire conversation history. The feature reduced median time to first token by approximately 35% for requests with 500K–1M tokens, without changing token usage.
Swarms are AI systems where multiple agents cooperate towards a shared goal. This post discusses a technique called collective inference-time scaling, where agents scale the amount of useful work performed in parallel and the communication needed to achieve it. It also explains how to design incentives for parallel work, and covers the general topic of designing incentives for reinforcement learning.
Microsoft's Copilot Cowork, an AI-driven agentic execution layer, achieved global availability in four and a half months, switching its runtime from Python to Rust while live. Key practices included writing clear specs before coding, running narrow agents with clear boundaries, and ensuring automated checks genuinely verify code changes. The transition to Rust improved efficiency and highlighted the importance of clear testing, boundary setting, and human oversight in the development process.
Being able to train reliably on multiple smaller clusters widens the usable training pool for frontier training. A single run could mix accelerator generations and vendors as each island runs on its own schedule. Older generations or less common chips could also participate in a frontier run. One risk with this model is that integrity and safety may become harder when models are trained across providers, as one faulty or compromised site can quietly degrade or tamper with models in ways that are harder to trace.
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GPU-accelerated Weight Teleportation optimizes RL training by improving weight syncing between trainers and samplers, reducing pause times to under 0.23 seconds. This is achieved using a co-designed compression/decompression pipeline with GPU-accelerated parallel processing, utilizing efficient codecs like LZ4 and Snappy, combined with a Zstd layer.
Framer's Harness 2 significantly upgrades the agent's execution process, allowing entire workflows in one program, achieving up to 33% cost reduction and 20% faster execution. This is done by executing JavaScript as it streams in, avoiding multiple model calls and using eager execution and early cancellation to enhance efficiency. The redesign also replaces DSL with method calls for real-time updates, making task execution streamlined for both in-app and external agents, reducing tool steps by 43% and cached input tokens by 39% per message.
NVIDIA introduced new Cosmos Framework capabilities for training, fine-tuning, reinforcement learning, and optimizing world models for physical AI. Developers can generate synchronized video and LiDAR simulations for robotics and autonomous vehicles, then deploy models through GPU-accelerated Cosmos NIM microservices.
OpenAI plans to merge the Chat and Work tabs within the ChatGPT experience after an initial testing period with select users. Codex will remain a separate experience in the ChatGPT desktop app. The change will enable users to move from exploring ideas to having the chatbot complete work within the same space. Users can import files and plugins. Both ChatGPT and Codex will soon draw from the same allowance pool.
A recent paper demonstrates GPT-6 Astra's ability to interact with neural network weights directly, surpassing human or conventional algorithm capabilities. Astra significantly improved a 0.8B Qwen LLM's mathematical skills by editing weights natively. While impressive, these capabilities reflect advanced problem-solving akin to skilled human researchers, not radically new AI capabilities.
Anthropic's amended IPO prospectus reveals a 1,200% increase in revenue to $33.24 billion for the first nine months of 2026. The company reported a net loss of $104 billion for the same period. Anthropic's commitments for compute and infrastructure total approximately $895.4 billion.
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Enclaves are a new security primitive for cross-repository GitHub Agentic Workflows that lets agents work on different repositories while limiting what they say to each other.
LifeVal is a benchmark designed to evaluate AI performance on practical everyday tasks such as budgeting, finding childcare, and accessing government services.
The research extends Anthropic's "Teaching Claude Why" methods to new models, focusing on reducing agentic misalignment through synthetic document finetuning (SDF) and supervised finetuning (SFT) with fictional stories and ethical dilemmas.
Most large-scale AI training runs use less than half the computing power they're paying for. Lambda's team benchmarked Llama 3.1 models from 8B to 405B on NVIDIA Blackwell GPUs and found the root causes of low Model FLOPS Utilization (MFU). The result: a reproducible framework that boosted efficiency by over 25%, without changing the model itself. Learn how to:Find memory inefficiencies driving up costsOptimize your training configuration for full hardware utilizationFix GPU communication bottlenecksDownload the whitepaper.
GPT-Rosalind-6 is a frontier reasoning model for biology, drug discovery, and translational medicine. It is available as GPT-Rosalind-6 Research to qualified scientists, research institutes, companies, and government partners. The model uses the same weights as GPT-6 Astra. It is designed to support legitimate biological research while refusing requests for biological weaponization. The model is rated as High Capability in the Biological and Chemical domain under OpenAI's Preparedness Framework.
A reference to Claude Mythos 5.5 was spotted on Anthropic's official Mythos webpage, indicating that the company may be preparing for an upcoming release. The change only covers the version number, and there are no new specifications and no announcement. Multiple reports suggest that the model is being A/B tested with selected users. If the company follows a similar strategy to previous releases, Mythos 5.5 may initially remain exclusive to selected cyberdefenders.
Google reports its Project Suncatcher prototype ran Gemma 4 on a data-center-class TPU in orbit, returning a haiku transmitted through Svalbard. Upcoming tests will assess thermal and radiation resilience ahead of a planned pair of interconnected satellites in 2027, advancing research into space-based AI compute.
Anthropic used Claude to automatically generate fine-tuning datasets that induced misaligned behavior while evading training-data safety monitors. The experiments uncovered seemingly harmless fiction and roleplay examples that caused models to resist oversight, deceive users, and occasionally attempt weight exfiltration.
An ERA fellow mentored at Apollo Research audited the open-source Codex harness and found 11 attacks that let a misaligned agent run arbitrary code despite the sandbox and the Auto-review blocking monitor. The exploits swap scripts behind trusted hooks, forge user turns the monitor can't tell from real ones, and edit files after approval. OpenAI has fixed several.
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Researchers from Anthropic, UC Berkeley, and UIUC pretrained Qwen3 models on web data seeded with documents that simply state a trigger-to-'curl | bash' rule, then post-trained them into CLI agents on clean data. The 4B agent ran the attacker's command on about 92% of prompts containing an ordinary path like .../anthropic/..., with no drop in normal capability. The backdoor grew stronger with model size and still fired at a 0.0005% poison rate.
AI models can hold skills that no prompt can unlock. It is possible to enable these skills through precise tuning of only a small number of weights. This study shows how researchers unlocked hidden skills inside Google's model through this method.
dsh-superagent is a coding agent packaged as a bundle for DeepSeek Harness. It can watch GitHub, open work items, work in its own agent session and directory, and answer in the original conversation. It only acts when asked or when a rule the team wrote allows it, and it only speaks when someone must act or can use a result.
Anthropic is growing absurdly fast. It just had its first profitable quarter. The real risk is years of locked-in compute bills paid for by customers who can cut back any month. The company is also dealing with regulatory issues and copyright suits, and it has a heavy reliance on the big cloud companies.
DoorDash revealed it has received "several hundred" orders from AI agents, despite processing 3 million deliveries daily. CEO Tony Xu highlighted that most orders still come directly from the DoorDash app, but the company is interested in exploring agentic use cases. DoorDash is also developing its own agents and considering future advertising opportunities within these platforms.
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Google announced plans to support a shared AI training control via robots.txt, allowing websites to set AI training preferences with a single policy token.
81% of doctors now use AI in daily practice. But three quarters have encountered a hallucination that could have affected patient safety. Just like we test drugs and medical devices before release, healthcare AI needs an independent referee that rigorously measures AI in clinical settings. Protege's Independent Evaluations fills this gap, testing your AI model with:New data: real-world, de-identified healthcare data that models haven't seen before.Expertise: clinician-verified answers to situations that reflect how healthcare is practiced.Independence: models never see the test in advance, and Protege doesn't input to model design.Learn about how Protege evaluates clinical AI →
OpenAI updated its Model Spec to organize model behavior around five values: faithfulness to human intent, harm minimization, honesty and transparency, human agency, and care.
Mistral has released the weights for Mistral Large 4, a trillion-parameter model designed for self-hosted deployment and cybersecurity applications. The company claims leading open-weight performance on agentic coding benchmarks and comparable cybersecurity capabilities to GLM-5.3.
GLM 5.3 can identify and exploit vulnerabilities in recent versions of the Mozilla Firefox browser using security fixes that have been published but not yet shipped to users. Widely available open-weight models can now do much, or even all, of this work on their own. Open-source projects are particularly vulnerable to this kind of attack as their code is publicly available. This threat vector will likely worsen as open-weight cyber capabilities continue to improve.
Agent swarms are groups of agents that work together to accomplish a task. Researchers created an experiment in which eight agents from four different models worked together to resolve simulated outages to understand how agents organize themselves in a swarm without a designated leader. They discovered two factors that could influence an agent's potential leadership: the agent's underlying models, and the order in which their name was listed in a prompt. This suggests that even seemingly minor details in how an agent swarm is configured can affect how agents coordinate.
Muse Spark Open often mentions a 'Charter' in its chain of thought. This post reconstructs that document using the chain of thought. Everything is reconstructed from model outputs and not from a leaked document, so it might not be completely accurate.
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Meta details how it scaled its LLM-powered Just-in-Time Testing system from a research prototype into production infrastructure. The article explores how AI-generated tests catch bugs before code changes land, how the system operates at scale, and what Meta learned about reducing false positives and testing overhead.
Post-trained models can compete with frontier models on specialized tasks like Figma editing. Starting with a focused problem can turn progress into meaningful product updates. This post explains how Figma trained a Figma-fluent editing model.
Droid is an agent harness that lets agents run hundreds of tool calls in a single model request. Building a harness optimized for many models makes the harness better. It's hard to separate real progress from adaptations to that model's quirks when you tune against a single model. Working across models ensures the strongest improvements generalize and hold up beyond the model that inspired them.
Sam Altman would rather have a trillion AIs of slightly superhuman intelligence join society than one AI of 1 trillion IQ join society. Swarms of agents match a lot of the properties of a resilient society. They keep each other in check and collaborate to work towards goals. AI research shouldn't lead in a centralized direction because agents pushing against each other creates a dynamic equilibrium.
AI models are getting exponentially better in individual competence while lagging behind in coordination. Models already match or outperform experienced specialists on self-contained deliverables across dozens of professions. The labor market reflects the current capabilities of AI systems. Wages and job roles will be the earliest signals of change.
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