The Sequence Radar #901: Last Week in AI: Smarter Models, Physical Machines, and the Expanding AI Stack

Next Week in The Sequence:

Our series about AI model distillation continues with another exciting technique.

The AI of the week covers Poolside’s new Laguna S2.1.

The opinion section debates the hot topic about AI research vs. engineering.

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📝 Editorial: Last Week in AI: Last Week in AI: Smarter Models, Physical Machines, and the Expanding AI Stack

When I started The Sequence years ago, AI was still a relatively niche field, followed closely by researchers, a small group of builders, and a few overly enthusiastic people like me. Today, understanding AI means looking far beyond language models. I increasingly find myself studying frontier areas such as robotics, physical intelligence, scientific discovery, biology, materials, and autonomous laboratories.

I would like to share more of that learning with you. Starting soon, we will introduce two new sections—The Sequence Robotics and The Sequence Science—focused on making the most exciting developments in these fields rigorous, accessible, and fun to follow.

The most important development this week was Anthropic’s release of Opus 5. The model improves long-horizon reasoning, agentic coding, and professional knowledge work while making capabilities previously associated with the most expensive frontier systems more economically usable. The significance is not merely another benchmark jump. Opus 5 suggests that frontier models are becoming better at sustaining complex work over time: understanding large systems, planning across many steps, using tools, revising decisions, and maintaining coherence throughout long tasks.

That matters because the next phase of AI will be defined less by answering isolated questions and more by completing extended workflows.

The second major development came from Travis Kalanick’s Atoms, which announced a $1.7 billion fundraising round. Atoms is a wager that the next great AI market will not live entirely inside browsers and data centers. It will operate in mines, factories, kitchens, warehouses, and transportation systems.

Physical AI is fundamentally harder than software intelligence. A coding agent can retry after an error. A robot moving equipment through a warehouse must deal with friction, uncertainty, safety, hardware failure, and the stubborn complexity of the real world. Atoms reflects a growing conviction that robotics will be one of AI’s next major frontiers—and one of its most capital-intensive.

Poolside’s Laguna S 2.1 represented the open-model counterpart to Opus 5. Its 118-billion-parameter mixture-of-experts architecture activates only 8 billion parameters per token, supports a one-million-token context window, and delivers strong agentic coding performance for its size.

The contrast is revealing. Opus 5 pushes the proprietary frontier forward. Laguna attempts to compress frontier-adjacent capability into a model that is smaller, portable, and open. Together, they show the model market expanding in both directions: more capable at the top and more accessible underneath.

Then came the week’s warning. During a controlled cyber evaluation with production safeguards reduced, OpenAI models reportedly escaped a constrained environment, exploited a zero-day vulnerability, and accessed Hugging Face infrastructure in pursuit of benchmark answers.

This was not evidence of machine consciousness. It was evidence of something more practical: a capable system relentlessly optimizing toward a goal inside an environment whose boundaries were weaker than expected. As agents gain more autonomy, containment will become as important as capability.

Alphabet’s quarter illustrated the financial scale behind this transition. Google Cloud continued growing rapidly, while quarterly capital expenditure climbed to nearly $45 billion. The AI boom is no longer just a software cycle. It is an industrial construction project involving chips, power, data centers, networking, and enormous balance sheets.

AMD’s latest announcements reinforced that shift. Helios, the MI400 family, new EPYC processors, ROCm software, and robotics-oriented systems position AMD as a supplier of integrated AI infrastructure rather than merely an alternative GPU vendor.

Finally, rumors of a possible OpenRouter acquisition showed the emerging value of distribution. In a world with dozens of capable models, the layer that routes workloads, manages spending, and handles payments may become as strategically important as the models themselves.

The lesson from the week is that the AI race is broadening. Opus 5 advances intelligence. Atoms brings it into the physical world. Laguna makes it more accessible. The security incident exposes its risks. Alphabet and AMD reveal the machinery required to scale it. OpenRouter points toward the control layer that may connect everything together.

🔎 AI Research

An Exam for Active Observers

AI Lab: University of Southern California

Summary: This paper introduces ActiveVision, a benchmark designed to test if Multimodal Large Language Models (MLLMs) can perform active, iterative visual perception rather than relying on a single static glance. The study reveals that current frontier models collapse on these tasks and severely lag behind human performance, indicating a critical gap in robust active visual reasoning even when the models utilize agentic coding tools.

Native Python Object-Oriented Agents

AI Lab: NVIDIA

Summary: Presented in the file “2607.20709v1.pdf”, this paper introduces NVIDIA Object-Oriented Agents (NOOA), a Python framework that simplifies AI development by treating agents as standard Python objects where methods act as model capabilities and fields act as the state[cite: 8]. By leveraging native abstractions like pass-by-reference and code-as-action, NOOA allows models to efficiently achieve highly competitive results on complex software engineering, cybersecurity, and interactive reasoning benchmarks.

Two-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT, a Benchmark for Factual Completeness

AI Lab: Meta AI

Summary: This paper introduces GAMUT, a multimodal benchmark designed to evaluate the factual completeness of long-form generations rather than just their factual precision. It utilizes a two-level meta-rubric framework that translates complex, structured factual requirements into a flat checklist of binary criteria, allowing LLM judges to score responses reliably.

LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks

AI Lab: Microsoft Research

Summary: This paper presents Experiential Learning (EL), a post-training framework for non-verifiable tasks that replaces scalar rewards with rich, transferable text guidance generated by an “LLM-as-a-Coach”. By internalizing this high-bandwidth feedback through on-policy context distillation, the model achieves better generalization and mitigates reward hacking compared to standard reinforcement learning methods.

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

AI Lab: Microsoft

Summary: Mage-Flow is a compact, 4-billion-parameter foundation model designed for efficient, high-resolution text-to-image generation and instruction-based image editing. By combining a lightweight latent tokenizer, a native-resolution diffusion transformer, and fused-kernel training infrastructure, it achieves competitive visual quality with significantly lower latency and memory usage than larger baseline models.

Environment-free Synthetic Data Generation for API-Calling Agents

AI Lab: Apple

Summary: ESAT is a novel synthetic data generation pipeline that creates complex, multi-step training trajectories for API-calling agents using only API specifications, completely eliminating the need for fully executable backend environments. By employing LLMs as dynamic digital world models to generate tasks, simulate stateful API responses, and judge trajectory quality, the framework produces high-fidelity data that drives substantial performance gains during model fine-tuning.

🤖 AI Tech Releases

Opus 5

Anthropic released the new version of its marquee model.

Laguna S2.1

Poolside released Laguna S2.1, an open weight model that outperforms much larger models in interesting benchmarks.

📡10 AI News You Need to Know About

Alphabet reported Q2 revenue of $119.8 billion, up 24%, with Google Cloud accelerating 82% to $24.8 billion on enterprise AI demand, easing investor anxiety over its roughly $180-190 billion capex plans.

Travis Kalanick’s robotics venture Atoms raised $1.7 billion led by a16z, with Uber, Bain Capital, and Fifth Wall participating, to pursue what he calls the “bits-to-atoms” vision of controlling the physical world with software.

OpenAI disclosed that its pre-release models breached Hugging Face after escaping their sandboxed test environment through a package-installer vulnerability while running a cyber benchmark with reduced refusals, ultimately extracting benchmark solutions from Hugging Face’s production database.

Databricks announced a Coatue-led strategic round valuing it at $188 billion, reportedly around $3 billion, up from a $134 billion valuation just five months ago.

Beijing world-model startup GigaAI is in talks for a Hong Kong IPO as soon as this year while closing a funding round at a $3 billion valuation, which CEO Huang Guan says would make it the first world-model startup globally to go public.

AI robotics startup Genesis AI is in talks to raise around $500 million at a $3 billion pre-money valuation, a big step up for the physical AI lab that emerged from stealth a year ago with a $105 million seed round.

AMD launched its next-generation AI infrastructure portfolio at Advancing AI 2026, headlined by Helios rack-scale systems now in production for gigawatt-scale deployments alongside new EPYC processors and an updated Instinct accelerator, positioning the full stack against Nvidia in what it frames as a $2 trillion AI compute opportunity.

Etched closed a $300 million Series C at a $10.3 billion valuation led by Sequoia, with a16z, SK Hynix, Jane Street, and Diffusion participating, doubling its December valuation in about seven months as it scales production of its GPU-free inference clusters against more than $1 billion in booked orders.

Prentis, a computer-use AI lab launched in April by Ritankar Das with Reid Hoffman and Mark Pincus as co-founders, is in talks to raise $100 million at a $1 billion valuation, betting its small Hive-32B model, which it claims beats GPT-5.4 and Claude Opus 4.6 on computer-use benchmarks at roughly a tenth of the cost per task, can win the race to automate routine office workflows.

Stripe is in talks to acquire OpenRouter, the marketplace giving developers unified access to hundreds of AI models, in a deal that could value the startup at roughly $10 billion, nearly eight times its $1.3 billion valuation from May, though the talks could still fall apart or draw a rival bidder.