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I Trained a Tiny Network to Compress Data. It Drew a Pentagon.
Reproducing Anthropic's Toy Models of Superposition from scratch in NumPy, with hand-derived gradients and no borrowed numbers. The post I Trained a Tiny Network to Compress Data. It Drew a Pentagon. appeared first on Towards Data Science.
From Words to Vectors: What Happens in Between?
A Journey through TF-IDF, vector space, and text classification The post From Words to Vectors: What Happens in Between? appeared first on Towards Data Science.
How GRPO Trains Small Language Models with Verifiable Rewards
The mechanics behind local reasoning experiments with Unsloth and why the reward function matters as much as the model. The post How GRPO Trains Small Language Models with Verifiable Rewards appeared first on Towards Data Science.
How to Make Your First World Model from Scratch
A beginner-friendly guide to building a world model in Python, letting it daydream its way through CartPole, and accurately measuring when the illusion collapses. The post How to Make Your First World Model from Scratch appeared first on Towards Data Science.
Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
arXiv:2609.25082v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purely classical approaches. Hybrid quantum-classical models operationalize this potential by embedding a parameterized quantum circuit within a model where all other components remain classical-a design already applied to chemistry simulation, financial modeling, and image classification. However, their deployment in privacy-sensitive, multi-party settings is constrained by the need to avoid centralizing raw data and by the requirement that modern quantum circuits remain parameter-efficient to stay trainable at scale. In this paper, we address these constraints by evaluating federated learning (FL) as a means of combining a hybrid quantum-classical active party with a classical passive party, using Sherpa.ai's Blind Vertical FL (SBVFL) protocol to avoid centralizing
Cosserat Modeling of Trimmed Helicoid Soft Arms with a Separated-Section Constitutive Law
arXiv:2609.25264v1 Announce Type: new Abstract: Cosserat rod models for soft robots usually construct sectional stiffness by summing material properties over a common cross-section. This assumption becomes inaccurate for trimmed helicoid arms, where load-bearing helix domains are separated and connected only through sparse fused crossings. This paper formulates a separated-section constitutive law that evaluates each helix domain in its local frame and pulls its constitutive response back to the backbone, yielding an effective backbone stiffness. Sparse-fusion mechanics captures the additional compliance caused by relative motion between neighboring domains and determines channel-wise reduction profiles $\eta_c(s/L)$ for bending, torsion, and extension. The resulting effective sectional stiffness is strongly anisotropic: bending and extension are reduced by about one order of magnitude, whereas torsion remains close to the effective backbone stiffness. The resulting sectional law is em
Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models
arXiv:2609.22161v1 Announce Type: new Abstract: Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how these data types differentially shape model capabilities remains unclear. We address this issue with token-matched experiments that vary the didactic-to-clinical ratio and analyze how data composition affects performance, capability profiles, and error patterns across knowledge-intensive and clinic-oriented tasks. We uncover an asymmetric transfer across task types: clinical data improves clinic-oriented tasks while remaining competitive on knowledge-intensive ones, whereas didactic data mainly improves knowledge-intensive tasks. Error analysis suggests a knowing-doing gap, where improvements in knowledge recall do not reliably generalize to clinical reasoning. We further observe that modest amounts of clinical data yield most of the gains on EHR-grounded tasks, while the optimal mixture rati
An Affordable AI-Integrated Smart Cane for Multimodal Mobility Assistance of Visually Impaired Users
arXiv:2609.22277v1 Announce Type: new Abstract: Visual impairment affects over 2.2 billion people worldwide, yet conventional white canes cannot detect elevated hazards or provide semantic environmental context. Existing AI-assisted navigation systems typically rely on expensive hardware or cloud connectivity, limiting accessibility in resource-constrained settings. This paper presents an affordable (\$88 USD), fully offline AI-integrated smart cane designed for multimodal mobility assistance on an ultra-low-power Raspberry Pi Zero 2W. The system fuses RGB vision sensing with Time-of-Flight (ToF) distance estimation, pairing an INT8-quantized SSD MobileNet V1 model with distance-aware vibrotactile feedback and real-time audio alerts. To ensure operational robustness on constrained hardware, a multiprocessing architecture isolates sensor acquisition, neural inference, and haptic feedback into independent processes with fail-safe sensing support. Experimental evaluation across indoor mob
Towards Adaptive Interaction Strategies for Human Companion Robot via Deep Reinforcement Learning
arXiv:2609.25031v1 Announce Type: new Abstract: In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for various applications but also poses significant challenges. Traditional methods typically restrict robots to fixed positions relative to humans, such as tracking from behind, in front, or side-by-side, which limits robot adaptability in dynamic workspaces. This study introduces a novel human-companioning strategy that uses Reinforcement Learning (DRL) to enable mobile robots to dynamically adjust their tracking positions according to varying conditions. An interaction space is defined to capture the relationship between the human and the robot while considering the environment, which serves as the basis for state spaces in DRL to assist the robot in adapting to environmental changes. A human-robot companion controller is developed by integrating Model Predictive Path Integral (MPPI) control wi
Claude Opus 5.5 Tested: What’s New and How Good is it?
What happens when an AI model gets better at reasoning, faster at responding, and cheaper to run at the same time? That is the promise behind Claude Opus 5.5, Anthropic s latest flagship model and the first release in the Claude 5.5 family. Opus 5.5 brings several notable changes. It now reasons on every request, generates [ ] The post Claude Opus 5.5 Tested: What’s New and How Good is it? appeared first on Analytics Vidhya.
Jev Explained: The AI Model That Never Generates a Word of Text
If you follow trends in the AI world, chances are you have already come across Jev, a new AI model by TypeSafe AI. It is trending on X, and once you understand the reason behind it, you will want to try it out for yourself. TypeSafe AI came out of two years in stealth on [ ] The post Jev Explained: The AI Model That Never Generates a Word of Text appeared first on Analytics Vidhya.
Break Your Own RAG Pipeline Before Users Do
A small adversarial test set that catches the retrieval failures your evaluation set never will The post Break Your Own RAG Pipeline Before Users Do appeared first on Towards Data Science.