Geometric and Topological Deep Learning are rapidly growing research areas that enhance machine learning through the use of geometric and topological structures. Within this framework, Group Equivariant Non-Expansive Operators (GENEOs) have emerged as a powerful class of operators for encoding symmetries and designing efficient, interpretable neural architectures. Originally introduced in Topological Data Analysis, GENEOs have since found applications in Deep Learning as tools for constructing equivariant models with reduced parameter complexity. GENEOs provide a unifying framework bridging Geometric […]

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How managing the information environment — not perfecting queries — determines whether your AI system ships or fails 1. The Fundamental Misconception Killing Production AI The AI industry has spent two years obsessed with the wrong problem. We’ve treated prompt engineering as the solution to every production challenge, iterating endlessly on phrasing, temperature parameters, and system instructions. Meanwhile, the actual bottleneck has been hiding in plain sight: information architecture. The core insight driving the shift to context engineering is brutally simple: no amount […]

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arXiv:2602.18534v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for program translation, particularly for migrating systems code to memory-safe languages such as Rust. However, existing approaches struggle when source programs depend on external libraries: LLMs frequently hallucinate non-existent target APIs and fail to generate call-enabling imports; moreover, validating semantic equivalence is challenging when the code manipulates opaque, library-defined types. We present a translation and validation framework for translating Go projects with external dependencies to Rust. […]

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“The demand for tokens in the world has gone completely exponential,” Nvidia CEO Jensen Huang said about the company’s earnings.

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arXiv:2601.21066v1 Announce Type: new Abstract: Backdoor attacks pose a severe threat to deep learning, yet their impact on object detection remains poorly understood compared to image classification. While attacks have been proposed, we identify critical weaknesses in existing detection-based methods, specifically their reliance on unrealistic assumptions and a lack of physical validation. To bridge this gap, we introduce BadDet+, a penalty-based framework that unifies Region Misclassification Attacks (RMA) and Object Disappearance Attacks (ODA). The core mechanism utilizes a […]

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arXiv:2601.18975v1 Announce Type: new Abstract: We explore how humanoid robots can be repurposed as haptic media, extending beyond their conventional role as social, assistive, collaborative agents. To illustrate this approach, we implemented HumanoidTurk, taking a first step toward a humanoid-based haptic system that translates in-game g-force signals into synchronized motion feedback in VR driving. A pilot study involving six participants compared two synthesis methods, leading us to adopt a filter-based approach for smoother and more realistic feedback. A […]

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arXiv:2602.05812v1 Announce Type: cross Abstract: We present a principled framework for confidence estimation in computed tomography (CT) reconstruction. Based on the sequential likelihood mixing framework (Kirschner et al., 2025), we establish confidence regions with theoretical coverage guarantees for deep-learning-based CT reconstructions. We consider a realistic forward model following the Beer-Lambert law, i.e., a log-linear forward model with Poisson noise, closely reflecting clinical and scientific imaging conditions. The framework is general and applies to both classical algorithms and deep […]

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Marketing teams were segmenting audiences based on who people are rather than what they do. Customer acquisition costs across digital channels have jumped 40 to 60 percent between 2023 and 2025.

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I just went through some pain (again) when I wanted to install some of Python’s scientific libraries on my second Mac. I summarized the setup and installation process for future reference. If you encounter any different or additional obstacles let me know, and please feel free to make any suggestions to improve this short walkthrough.

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arXiv:2601.18921v1 Announce Type: new Abstract: The integration of large-scale chemical databases represents a critical bottleneck in modern cheminformatics research, particularly for machine learning applications requiring high-quality, multi-source validated datasets. This paper presents a case study of integrating three major public chemical repositories: PubChem (176 million compounds), ChEMBL, and eMolecules, to construct a curated dataset for molecular property prediction. We investigate whether byte-offset indexing can practically overcome brute-force scalability limits while preserving data integrity at hundred-million scale. Our results […]

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