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Pose-Aware Modeling to Mitigate Pose-Related Artifacts in Tactile Gloves
arXiv:2607.22964v1 Announce Type: new Abstract: Tactile gloves digitize contact and force during hand-object interactions, enabling robotics applications in dexterous manipulation, teleoperation, and learning from demonstration. To preserve hand dexterity and capture the nuances of natural interactions, these gloves and the integrated tactile sensors are designed to be soft, flexible, and comfortable. However, such flexible sensors are sensitive not only to contact forces but also unavoidably to hand pose changes, resulting in pose-related artifacts (PRAs). PRAs are especially problematic in the low-force range, resulting in misdetections or late-onset detections of contact, which raises the minimum detectable force (MDF) of the glove. In this work, we characterize the PRAs in relation to pose and force. Building on these insights, we introduce a glove-agnostic algorithmic framework that leverages hand pose information, which is increasingly available, to mitigate PRAs without glove mo
A Replay-Constrained Simulation Framework for Personalization of Powered Knee--Ankle Prosthesis Controllers
arXiv:2607.22858v1 Announce Type: new Abstract: Personalization of impedance controllers for powered prosthetic legs is critical to accommodating individual gait biomechanics but remains challenging. Existing methods rely on time-intensive human-in-the-loop exploration and/or constrain optimization to low-dimensional, single-joint parameter subspaces. Sim-to-real transfer has enabled high-dimensional locomotion control for legged robots, but in assistive device control the human partner remains un-modelable. We present a replay-constrained simulation framework: a MuJoCo-based simulator reproduces prosthetic knee-ankle dynamics while replaying recorded hip kinematics and feedback-based ground reaction forces from individual walking data, bypassing the need to model complex human neuromuscular control mechanisms. We demonstrate the framework with a deep reinforcement learning policy that personalizes phase-dependent stiffness, damping, and equilibrium angle at both joints simultaneously,
Semalith v1.4: A Calibrated 184M Safety Classifier Achieving State-of-the-Art Prompt-Injection Detection at 44x Fewer Parameters than Llama-Guard-3-8B
arXiv:2607.22545v1 Announce Type: new Abstract: Deploying large language models in financial-services and agentic settings requires safety classifiers that simultaneously handle prompt injection, regulatory compliance, and general harm, a combination no existing open guardrail addresses in a single inference pass. Semalith v1.4 is a 184M-parameter DeBERTa-v3-base classifier performing simultaneous three-axis safety classification including prompt injection, general harm, and financial-services regulatory compliance, in a single forward pass. Its 22-class head (BENIGN, nine prompt-injection sub-types, general-harm, eleven BFSI labels) is trained with a 4-class auxiliary super-category head under jointly weighted loss, on a 76,204-row corpus mined from 49 public sources with SHA-1 deduplication against every held-out evaluation set, with 21 of 22 benchmarks at zero contamination (max 0.22%). Against Llama-Guard-3-8B on 22 held-out benchmarks, Semalith v1.4 wins every prompt-injection eva
SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series
arXiv:2607.22548v1 Announce Type: new Abstract: Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics. Current machine learning approaches for HPC and its telemetry typically rely on a static subset of anonymous, fixed-position sensor variables tailored to single tasks. Consequently, these models become obsolete when target tasks change or sensor metrics vary. We propose SeT-Diff, the first foundational model for compute node telemetry and time-series. Unlike rigid architectures, our diffusion-based approach conditions the generative process on each sensor's semantic description, decoupling the system dynamics from the structure of the dataset. Experiments on a real-world supercomputer dataset demonstrate a Mean Absolute Error (MAE) of 0.0470 on reconstruction tasks. SeT-Diff exhibits zero-shot permutation stability, maintaining accuracy with negligible degr
How to Solve Complex Problems While Writing Angular Applications in Modern Development
Practical strategies, architectural patterns, and debugging techniques to build scalable, maintainable, and high-performance Angular Continue reading on Medium »
BlackTrace Explained | Python-Powered OSINT Tool for Security Researchers
Build Smarter Open-Source Intelligence Workflows with Python and Ethical Investigation TechniquesContinue reading on Medium »
Kimi K3's full weights are here, but they're 'open' with a caveat: What enterprises should know
Chinese AI startup Moonshot AI, creators of the increasingly famous Kimi K family of powerful, open AI models, today released the full weights for its largest and most performant version yet — Kimi K3. But enterprises evaluating the model should read the attached custom Kimi K3 usage license as carefully as the benchmark charts.VentureBeat previously covered Kimi K3 when it debuted through Moonshot s hosted API earlier this month, including its 2.8 trillion-parameter architecture, one million-token context window and frontier benchmark performance. Today s announcement completes that rollout with the release of the full model weights, a 47-page technical report documenting its training innovations and obstacles, and much of the infrastructure required to run the model independently.That includes the complete 2.8 trillion-parameter Mixture-of-Experts model, inference infrastructure, optimized attention kernels, MoE communication libraries and deployment components aimed at researchers a
AI isn’t replacing Product Designers. It’s promoting them to a higher level.
AI isn t replacing great product designers it s exposing the difference between those who execute and those who create real product Continue reading on Medium »
The Singularity Equation: An Empirical Investigation Using TOPO-2026
Continue reading on AI Simplified in Plain English »
Attention Decode on AMD MI450 GPUs: A Gluon Kernel Optimization Guide
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Anthropic’s Dario Amodei responds: doesn’t oppose open-weight models, but fears Chinese AI
Anthropic founder and CEO Dario Amodei made his views clear about open-weight models and China's growing AI capabilities.
Can Parakeet, Whisper, and Qwen Replace Wispr Flow?
Speech recognition is only half the pipeline. Small local language models are starting to handle the cleanup too.Continue reading on Level Up Coding »