Anthropic just revealed how AI-orchestrated cyberattacks actually work—Here’s what enterprises need to knowAI News For years, cybersecurity experts debated when – not if – artificial intelligence would cross the threshold from advisor to autonomous attacker. That theoretical milestone has arrived. Anthropic’s recent investigation into a Chinese state-sponsored operation has documented [PDF] the first case of AI-orchestrated cyber attacks executing at scale with minimal human oversight, altering what enterprises must
The post Anthropic just revealed how AI-orchestrated cyberattacks actually work—Here’s what enterprises need to know appeared first on AI News.
For years, cybersecurity experts debated when – not if – artificial intelligence would cross the threshold from advisor to autonomous attacker. That theoretical milestone has arrived. Anthropic’s recent investigation into a Chinese state-sponsored operation has documented [PDF] the first case of AI-orchestrated cyber attacks executing at scale with minimal human oversight, altering what enterprises must
The post Anthropic just revealed how AI-orchestrated cyberattacks actually work—Here’s what enterprises need to know appeared first on AI News. Read More
DF-Mamba: Deformable State Space Modeling for 3D Hand Pose Estimation in Interactionscs.AI updates on arXiv.org arXiv:2512.02727v1 Announce Type: cross
Abstract: Modeling daily hand interactions often struggles with severe occlusions, such as when two hands overlap, which highlights the need for robust feature learning in 3D hand pose estimation (HPE). To handle such occluded hand images, it is vital to effectively learn the relationship between local image features (e.g., for occluded joints) and global context (e.g., cues from inter-joints, inter-hands, or the scene). However, most current 3D HPE methods still rely on ResNet for feature extraction, and such CNN’s inductive bias may not be optimal for 3D HPE due to its limited capability to model the global context. To address this limitation, we propose an effective and efficient framework for visual feature extraction in 3D HPE using recent state space modeling (i.e., Mamba), dubbed Deformable Mamba (DF-Mamba). DF-Mamba is designed to capture global context cues beyond standard convolution through Mamba’s selective state modeling and the proposed deformable state scanning. Specifically, for local features after convolution, our deformable scanning aggregates these features within an image while selectively preserving useful cues that represent the global context. This approach significantly improves the accuracy of structured 3D HPE, with comparable inference speed to ResNet-50. Our experiments involve extensive evaluations on five divergent datasets including single-hand and two-hand scenarios, hand-only and hand-object interactions, as well as RGB and depth-based estimation. DF-Mamba outperforms the latest image backbones, including VMamba and Spatial-Mamba, on all datasets and achieves state-of-the-art performance.
arXiv:2512.02727v1 Announce Type: cross
Abstract: Modeling daily hand interactions often struggles with severe occlusions, such as when two hands overlap, which highlights the need for robust feature learning in 3D hand pose estimation (HPE). To handle such occluded hand images, it is vital to effectively learn the relationship between local image features (e.g., for occluded joints) and global context (e.g., cues from inter-joints, inter-hands, or the scene). However, most current 3D HPE methods still rely on ResNet for feature extraction, and such CNN’s inductive bias may not be optimal for 3D HPE due to its limited capability to model the global context. To address this limitation, we propose an effective and efficient framework for visual feature extraction in 3D HPE using recent state space modeling (i.e., Mamba), dubbed Deformable Mamba (DF-Mamba). DF-Mamba is designed to capture global context cues beyond standard convolution through Mamba’s selective state modeling and the proposed deformable state scanning. Specifically, for local features after convolution, our deformable scanning aggregates these features within an image while selectively preserving useful cues that represent the global context. This approach significantly improves the accuracy of structured 3D HPE, with comparable inference speed to ResNet-50. Our experiments involve extensive evaluations on five divergent datasets including single-hand and two-hand scenarios, hand-only and hand-object interactions, as well as RGB and depth-based estimation. DF-Mamba outperforms the latest image backbones, including VMamba and Spatial-Mamba, on all datasets and achieves state-of-the-art performance. Read More
Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Trainingcs.AI updates on arXiv.org arXiv:2512.02652v1 Announce Type: cross
Abstract: Existing methods for expressive music performance rendering rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language. To address this gap, we introduce Pianist Transformer, with four key contributions: 1) a unified Musical Instrument Digital Interface (MIDI) data representation for learning the shared principles of musical structure and expression without explicit annotation; 2) an efficient asymmetric architecture, enabling longer contexts and faster inference without sacrificing rendering quality; 3) a self-supervised pre-training pipeline with 10B tokens and 135M-parameter model, unlocking data and model scaling advantages for expressive performance rendering; 4) a state-of-the-art performance model, which achieves strong objective metrics and human-level subjective ratings. Overall, Pianist Transformer establishes a scalable path toward human-like performance synthesis in the music domain.
arXiv:2512.02652v1 Announce Type: cross
Abstract: Existing methods for expressive music performance rendering rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language. To address this gap, we introduce Pianist Transformer, with four key contributions: 1) a unified Musical Instrument Digital Interface (MIDI) data representation for learning the shared principles of musical structure and expression without explicit annotation; 2) an efficient asymmetric architecture, enabling longer contexts and faster inference without sacrificing rendering quality; 3) a self-supervised pre-training pipeline with 10B tokens and 135M-parameter model, unlocking data and model scaling advantages for expressive performance rendering; 4) a state-of-the-art performance model, which achieves strong objective metrics and human-level subjective ratings. Overall, Pianist Transformer establishes a scalable path toward human-like performance synthesis in the music domain. Read More
Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometrycs.AI updates on arXiv.org arXiv:2503.01822v2 Announce Type: replace-cross
Abstract: Sparse Autoencoders (SAEs) are widely used to interpret neural networks by identifying meaningful concepts from their representations. However, do SAEs truly uncover all concepts a model relies on, or are they inherently biased toward certain kinds of concepts? We introduce a unified framework that recasts SAEs as solutions to a bilevel optimization problem, revealing a fundamental challenge: each SAE imposes structural assumptions about how concepts are encoded in model representations, which in turn shapes what it can and cannot detect. This means different SAEs are not interchangeable — switching architectures can expose entirely new concepts or obscure existing ones. To systematically probe this effect, we evaluate SAEs across a spectrum of settings: from controlled toy models that isolate key variables, to semi-synthetic experiments on real model activations and finally to large-scale, naturalistic datasets. Across this progression, we examine two fundamental properties that real-world concepts often exhibit: heterogeneity in intrinsic dimensionality (some concepts are inherently low-dimensional, others are not) and nonlinear separability. We show that SAEs fail to recover concepts when these properties are ignored, and we design a new SAE that explicitly incorporates both, enabling the discovery of previously hidden concepts and reinforcing our theoretical insights. Our findings challenge the idea of a universal SAE and underscores the need for architecture-specific choices in model interpretability. Overall, we argue an SAE does not just reveal concepts — it determines what can be seen at all.
arXiv:2503.01822v2 Announce Type: replace-cross
Abstract: Sparse Autoencoders (SAEs) are widely used to interpret neural networks by identifying meaningful concepts from their representations. However, do SAEs truly uncover all concepts a model relies on, or are they inherently biased toward certain kinds of concepts? We introduce a unified framework that recasts SAEs as solutions to a bilevel optimization problem, revealing a fundamental challenge: each SAE imposes structural assumptions about how concepts are encoded in model representations, which in turn shapes what it can and cannot detect. This means different SAEs are not interchangeable — switching architectures can expose entirely new concepts or obscure existing ones. To systematically probe this effect, we evaluate SAEs across a spectrum of settings: from controlled toy models that isolate key variables, to semi-synthetic experiments on real model activations and finally to large-scale, naturalistic datasets. Across this progression, we examine two fundamental properties that real-world concepts often exhibit: heterogeneity in intrinsic dimensionality (some concepts are inherently low-dimensional, others are not) and nonlinear separability. We show that SAEs fail to recover concepts when these properties are ignored, and we design a new SAE that explicitly incorporates both, enabling the discovery of previously hidden concepts and reinforcing our theoretical insights. Our findings challenge the idea of a universal SAE and underscores the need for architecture-specific choices in model interpretability. Overall, we argue an SAE does not just reveal concepts — it determines what can be seen at all. Read More
JSON Parsing for Large Payloads: Balancing Speed, Memory, and ScalabilityTowards Data Science Benchmarking JSON libraries for large payloads
The post JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability appeared first on Towards Data Science.
Benchmarking JSON libraries for large payloads
The post JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability appeared first on Towards Data Science. Read More
How to Vibe Code on a BudgetKDnuggets What if I told you that a powerful vibe coding workflow on par with Claude Code can cost you less than $10? Let me prove it.
What if I told you that a powerful vibe coding workflow on par with Claude Code can cost you less than $10? Let me prove it. Read More
How Proactive Cybersecurity Saves Money (and Reputation) (Sponsored)KDnuggets The value of a modern company isn’t in its firewalls; it’s in its terabytes of proprietary, labeled data and the predictive models built upon them.
The value of a modern company isn’t in its firewalls; it’s in its terabytes of proprietary, labeled data and the predictive models built upon them. Read More
How to Use Simple Data Contracts in Python for Data ScientistsTowards Data Science Stop your pipelines from breaking on Friday afternoons using simple, open-source validation with Pandera.
The post How to Use Simple Data Contracts in Python for Data Scientists appeared first on Towards Data Science.
Stop your pipelines from breaking on Friday afternoons using simple, open-source validation with Pandera.
The post How to Use Simple Data Contracts in Python for Data Scientists appeared first on Towards Data Science. Read More
IBM cites agentic AI, data policies, and quantum as 2026 trendsAI News Enterprise leaders are entering 2026 with an uncomfortable mix of volatility, optimism, and pressure to move faster on AI and quantum computing, according to a paper published by the IBM Institute for Business Value. Its findings are based on more than 1,000 C-suite executives and 8,500 employees and consumers. While only around a third of
The post IBM cites agentic AI, data policies, and quantum as 2026 trends appeared first on AI News.
Enterprise leaders are entering 2026 with an uncomfortable mix of volatility, optimism, and pressure to move faster on AI and quantum computing, according to a paper published by the IBM Institute for Business Value. Its findings are based on more than 1,000 C-suite executives and 8,500 employees and consumers. While only around a third of
The post IBM cites agentic AI, data policies, and quantum as 2026 trends appeared first on AI News. Read More
7 ChatGPT Tricks to Automate Your Data TasksKDnuggets This article explores how to transform ChatGPT from a chatbot into a powerful data assistant that streamlines the repetitive, the tedious, and the complex.
This article explores how to transform ChatGPT from a chatbot into a powerful data assistant that streamlines the repetitive, the tedious, and the complex. Read More