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Related Articles from SNS
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
Announce Type: replace Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kernel-based integral operators for function-space approximation of operators (maps from functions to functions). The KNO decouples the choice of kernel from the numerical integration scheme (quadrature), thereby naturally allowing for operator learning with explicitly-chosen trainable kernels on irregular geometries....
Agent Operating Systems (AOS): Integrating Agentic Control Planes into, and Beyond, Traditional Operating Systems
arXiv:2606.01508v1 Announce Type: new Abstract: Traditional operating systems were designed around deterministic programs, explicit control flow, and human initiated workflows. Their core abstractions processes, threads, system calls, files, and permissions assume bounded behavior and predictable interaction patterns. Agentic AI systems introduce a different execution model: long-lived, goal-directed entities that reason probabilistically, invoke tools dynamically, and adapt behavior based...
Differentiable Efficient Operator Search
Announce Type: new Abstract: Efficient multimodal foundation models often rely on manually designed token-reduction operators, such as pruning, merging, pooling, and adaptive reweighting. Although these operators appear different, we show that they can be interpreted as distinct regimes of a shared operator space. Based on this view, we introduce Efficient Operator Search, a differentiable framework that jointly searches where to reduce tokens, how many tokens to retain, and how reduced...
A Multi-Operator Mixed-Reality Interface for Multi-Robot Control and Coordination: Co-Located and Private Workspace Collaboration
arXiv:2606.07013v1 Announce Type: new Abstract: Multi-operator control of robot teams requires not only access to the same mission information, but also mechanisms for maintaining shared awareness and preventing conflicting interventions. Building on our previous HORUS interface (Holistic Operational Reality for Unified Systems) we present a mixed-reality interface that extends single-operator multi-robot supervision to collaborative multi-operator use. The system supports two complementary...
Topological Neural Operators
arXiv:2606.09806v1 Announce Type: new Abstract: We introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or edges to topological domains. TNOs represent data as features defined on cells of varying dimension and model their interactions through Discrete Exterior Calculus, enabling explicit cross-dimensional coupling via gradient-, curl-, and divergence-type operators. The...
Meta leads largest-ever anti-scam operation with FBI and DOJ, resulting in 63 arrests
A sweeping anti-scam operation led by Meta and backed by the FBI, Department of Justice, Microsoft, Coinbase and Starlink resulted in 63 arrests, millions of dollars in frozen cryptocurrency and the removal of more than a million scam-related online accounts, officials announced Tuesday. Meta said the operation was the company’s largest anti-scam operation to date and described it as the first coordinated anti-scam effort of its kind for the company to bring together major technology...
[EMBARGO] Meta leads largest-ever anti-scam operation with FBI and DOJ, resulting in 63 arrests
A sweeping anti-scam operation led by Meta and backed by the FBI, Department of Justice, Microsoft, Coinbase and Starlink resulted in 63 arrests, millions of dollars in frozen cryptocurrency and the removal of more than a million scam-related online accounts, officials announced Tuesday. Meta said the operation was the company’s largest anti-scam operation to date and described it as the first coordinated anti-scam effort of its kind for the company to bring together major technology...
Spectral Audit of In-Context Operator Networks
arXiv:2606.02427v1 Announce Type: new Abstract: Existing evaluations of neural operators and in-context operator learning rely primarily on prediction error, but accurate output prediction does not guarantee the correct local dynamical structure. A model may match solutions while exhibiting incorrect sensitivities, distorted frequency response, spurious mode coupling, or unstable tangent behavior. We introduce a Jacobian-based spectral audit for in-context operator learning.
FBI charges 35 in West Virginia drug and firearms operation, launches nationwide summer crime initiative
The FBI on Tuesday said 35 people had been charged for narcotics and firearms offenses stemming from a yearlong federal operation in West Virginia while also unveiling a new nationwide summer crime-fighting initiative. The bureau said FBI Pittsburgh and FBI Baltimore launched Operation Turf War in early 2025 alongside the Eastern Panhandle Drug and Violent Crimes Task Force. The operation resulted in the seizure of illegal firearms and narcotics, along with the forfeiture of proceeds...
On the training of physics-informed neural operators for solving parametric partial differential equations
Announce Type: new Abstract: Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying solely on paired input-output simulation data. By incorporating physical constraints into the training objective, PINOs combine the cross-instance generalization of neural operators with the data efficiency of physics-informed learning. Despite this promise, how to train PINOs efficiently...