What is Meta Muse and what can the AI agent do?

Numbers match the citations in the article. A working link isn't proof that a page supports a claim; check the quoted passage and date.

1. 1

   Introducing Muse Spark: Scaling Towards Personal Superintelligence

   Figshare (Team)Published Aug 10, 2026Checked Oct 8, 2026

   “We introduce Muse Spark, the first in the Muse family of models developed by Meta Superintelligence Labs. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. Muse Spark is the first step on our scaling ladder and the first product of a ground-up overhaul of our AI efforts. To support further scaling, we are making strategic investments across the entire stack, from research and model training to infrastructure, including the Hyperion data center. In this report, we'll first explore Muse Spark's new capabilities and applications. After these results, we'll look behind the curtain at the scaling axes driving our progress toward personal superintelligence. Muse Spark is available today at meta.ai and the Meta AI app. We're opening a private API preview to select users.”

2. 2

   Muse Spark Safety & Preparedness Report

   arXiv (Cornell University) (Menghini et al.)Published May 14, 2026Checked Oct 8, 2026

   “We then discuss additional considerations, such as Muse Spark's broader content safety and behavioral profile, that are relevant to overall safety but fall outside the catastrophic risk domains governed by the Framework. Our preparedness results covering Chemical and Biological, Cybersecurity, and Loss of Control risks assess Muse Spark's deployment within Meta AI as presenting acceptable levels of residual risks under our Advanced AI Scaling Framework. We conducted a broad set of evaluations targeting dual-use and high-risk capabilities across these catastrophic risk domains. Those evaluations identified elevated risks prior to mitigations, with Chemical and Biological capabilities assessed as likely reaching the "high risk" category under the Advanced AI Scaling Framework before safeguards were applied. We have implemented a multi-layered set of mitigations that address the identified risks, and Muse Spark demonstrates state-of-the-art refusal across a range of benchmarks related to hazardous workflows in chemistry and biology. We therefore release Muse Spark as the underlying model of Meta AI.”

3. 3

   AI agent in healthcare: applications, evaluations, and future directions

   npj Artificial Intelligence (Zhao et al.)Published Mar 5, 2026Checked Oct 8, 2026

   “With the rapid advancement of large language model (LLM) technologies, AI agents have rapidly emerged in healthcare. This review traces the historical evolution and core characteristics of AI agents, and systematically examines their applications in assisted diagnosis, clinical decision support, medical report generation, patient-facing chatbots, healthcare system management, and medical education. We further analyze existing evaluation frameworks for AI agents in healthcare, focusing on key dimensions and performance metrics. Looking ahead, we propose seven critical directions for future development: integration with embodied systems, hybrid expert models, expanded evaluation paradigms, safety and controllability assurance, ethical governance and user trust, and guidance for evolving roles of healthcare staff. This review aims to offer a comprehensive perspective on the development and implementation of AI agents in healthcare, providing theoretical support for future research, practice, and governance.”

4. 4

   IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems

   arXiv (Cornell University) (Wu et al.)Published Mar 8, 2024Checked Oct 8, 2026

   “These LLM app ecosystems resemble the settings of earlier computing platforms, where there was insufficient isolation between apps and the system. Because third-party apps may not be trustworthy, and exacerbated by the imprecision of natural language interfaces, the current designs pose security and privacy risks for users. In this paper, we evaluate whether these issues can be addressed through execution isolation and what that isolation might look like in the context of LLM-based systems, where there are arbitrary natural language-based interactions between system components, between LLM and apps, and between apps. To that end, we propose IsolateGPT, a design architecture that demonstrates the feasibility of execution isolation and provides a blueprint for implementing isolation, in LLM-based systems. We evaluate IsolateGPT against a number of attacks and demonstrate that it protects against many security, privacy, and safety issues that exist in non-isolated LLM-based systems, without any loss of functionality. The performance overhead incurred by IsolateGPT to improve security is under 30% for three-quarters of tested queries.”

5. 5

   Towards an Agent-First Web: Redesigning the Web for AI Agents

   arXiv (Cornell University) (Bandara et al.)Published Jun 17, 2026Checked Oct 8, 2026

   “At the economic layer, we propose an intent-based tier framework grounded in the agent-as-human-proxy principle: an agent's economic obligation mirrors that of the human it represents. A token-based subscription model meters content in tokens rather than pageviews, alongside a commissioned content economy anchoring AI content production in human intentionality. At the content layer, we identify epistemic recursion, the self-referential loop in which AI-generated content is consumed by agents to produce further content, progressively detaching web knowledge from human ground truth. We propose the Agent Text Markup Language (ATML), a four-level human supervision tier model, and a cryptographic provenance chain to counter this threat. Together these constitute ten design principles for an agent-first internet, one in which agents are first-class citizens whose integration requires renegotiating the web's foundational social contract across access, economics, and content.”

6. 6

   AI, agentic models and lab automation for scientific discovery - the beginning of scAInce.

   Frontiers in artificial intelligence (Hartung)Published Aug 29, 2025Checked Oct 8, 2026

   “In this review, I merge the substance of our 2024 white paper for the World Economic Forum Top-10-Technologies Report with the latest advances through mid-2025, charting a course from automated literature synthesis and hypothesis generation to self-driving laboratories, organoid intelligence and climate-scale forecasting. The discussion is grounded in emerging governance regimes-notably the European Union Artificial Intelligence Act and ISO 42001-and is written from the dual vantage-point of a toxicologist who has spent a career championing robust, humane science and of a field chief editor charged with safeguarding scholarly standards in Frontiers in Artificial Intelligence. I argue that research is entering a "co-pilot to lab-pilot" transition in which AI no longer merely interprets knowledge but increasingly acts upon it. This shift promises dramatic efficiency gains yet simultaneously amplifies concerns about reproducibility, auditability, safety and equitable access.”