Meta Publishes Advanced AI Scaling Framework and Safety & Preparedness Report for Muse Spark
Meta has released an updated Advanced AI Scaling Framework that expands risk evaluation categories—including chemical/biological threats, cybersecurity, and loss-of-control risks—and introduces formal Safety & Preparedness Reports tied to specific model deployments. The first such report covers Muse Spark, Meta's advanced reasoning model, detailing pre- and post-safeguard evaluations across severe risk categories and ideological balance. Meta also describes a shift in safety methodology: rather than scenario-specific refusal training, Muse Spark is trained on the reasoning behind safety principles, enabling more generalizable behavior in novel situations. The framework applies across open, API, and closed deployments.
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Meta Pivots to Closed Weights with Muse Spark; The Batch Issue 349 Roundup
Meta introduced Muse Spark, its first AI model in roughly a year and the first product from its Superintelligence Labs, marking a pivot away from its open-weights strategy toward a closed model. Muse Spark is a natively multimodal reasoning model supporting tool use and multi-agent orchestration, with three reasoning modes and a novel 'thought compression' post-training technique using RL to penalize excessive reasoning tokens. The model ranks fourth on the Artificial Analysis Intelligence Index and matches Llama 4 Maverick's capabilities with over an order of magnitude less training compute, though it trails in coding and agentic benchmarks. The issue also covers broader industry themes including AI-native software engineering team structures, big pharma AI adoption, and regulatory developments.
Meta Introduces Muse Spark: First Model from Meta Superintelligence Labs with Multimodal Reasoning and Multi-Agent Orchestration
Meta has launched Muse Spark, the first model from its newly formed Meta Superintelligence Labs, positioned as a natively multimodal reasoning model with tool-use, visual chain-of-thought, and multi-agent orchestration capabilities. The model introduces 'Contemplating mode,' which runs multiple agents in parallel to compete with frontier reasoning modes, achieving 58% on Humanity's Last Exam and 38% on FrontierScience Research. Meta claims a greater than 10x compute efficiency improvement over Llama 4 Maverick through a rebuilt pretraining stack, and describes predictable scaling across pretraining, RL, and test-time reasoning axes. Muse Spark is available at meta.ai with a private API preview, and is framed as the first step on a scaling ladder toward 'personal superintelligence.'
Meta Introduces Muse Spark: First Closed-Weights Model from Superintelligence Labs
Meta released Muse Spark, its first AI model in roughly a year and the debut product of its Superintelligence Labs, marking a significant departure from its open-weights Llama strategy. The natively multimodal reasoning model supports tool use and multi-agent orchestration, achieves fourth place on the Artificial Analysis Intelligence Index, and claims notable token efficiency—matching Llama 4 Maverick with over 10x less training compute. Meta withheld parameter count, architecture, and training details, positioning Muse Spark as a closed commercial product competing with OpenAI, Google, and Anthropic. The release introduces 'thought compression' via RL and a parallel multi-agent 'contemplating' mode, while showing gaps in coding and agentic benchmarks.
Anthropic publishes major update to Responsible Scaling Policy with new capability thresholds and ASL standards
Anthropic released a significant revision to its Responsible Scaling Policy (RSP), its risk governance framework for managing catastrophic risks from frontier AI. The update introduces two explicit capability thresholds—autonomous AI R&D and CBRN weapons uplift—that trigger mandatory upgrades to AI Safety Level (ASL) standards, with current models operating under ASL-2. New elements include safety-case-inspired documentation processes, internal governance stress-testing, and external expert input mechanisms, drawing on risk management practices from high-consequence industries like biosafety.
OpenAI Updates Its Preparedness Framework
OpenAI has published an updated version of its Preparedness Framework, which governs how the company measures and mitigates severe risks from frontier AI capabilities. The framework sets thresholds and protocols for evaluating dangerous capability levels across domains such as CBRN, cybersecurity, and persuasion. This update reflects ongoing evolution in OpenAI's internal safety governance as frontier models grow more capable.
Anthropic publishes Responsible Scaling Policy with AI Safety Level framework
Anthropic released its Responsible Scaling Policy (RSP), a formal framework of technical and organizational protocols for managing catastrophic risks from increasingly capable AI systems. The policy introduces AI Safety Levels (ASL-1 through ASL-5+), modeled on US biosafety level standards, requiring progressively stricter safety, security, and operational standards as models become more capable. Current Claude models are classified as ASL-2; ASL-3 triggers stricter deployment constraints including adversarial red-teaming requirements. The policy has been approved by Anthropic's board and is intended as a template for industry-wide adoption.
Anthropic Releases Responsible Scaling Policy Version 3.0
Anthropic has published the third version of its Responsible Scaling Policy (RSP), a voluntary framework for mitigating catastrophic risks from increasingly capable AI systems. The update reflects two-plus years of experience with the original RSP, reinforcing what worked (ASL-3 safeguards activated in May 2025, industry adoption by OpenAI and Google DeepMind, informing early AI policy) while addressing shortcomings in accountability and transparency. The new version refines the AI Safety Level (ASL) framework and introduces new measures for decision-making transparency. Anthropic acknowledges that some elements of its original theory of change—particularly multilateral coordination and government action at higher capability thresholds—have not fully materialized as hoped.
DeepMind Publishes Framework for Evaluating Cybersecurity Threats of Advanced AI
DeepMind has released a framework designed to help cybersecurity experts assess and prioritize defenses against potential threats posed by advanced AI systems. The framework aims to systematically identify which defensive measures are necessary given AI's expanding capabilities in offensive cyber operations. This represents DeepMind's structured approach to evaluating AI-enabled cyber risks before they materialize at scale.