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Agentic AI vs AI Automation: What’s the Real Difference?
This scene is playing out across engineering teams everywhere. Someone wraps a few LangChain calls inside a loop, adds a couple of tools, and proudly declares, We ve built an AI agent. The demo looks great. Everyone is impressed. Then it goes to production. The first unexpected input arrives. The workflow breaks. Logs fill up. Alerts start firing. [ ] The post Agentic AI vs AI Automation: What s the Real Difference? appeared first on Analytics Vidhya.
Prompt Engineering Isn’t Enough: How Four Bricks of Context Engineering Stop RAG Hallucinations
Enterprise Document Intelligence [Vol.1 #9bis] - Your RAG isn’t hallucinating, it’s answering the wrong context faithfully. On real NIST and World Bank documents, watch each of the four bricks break, and the contract that closes it The post Prompt Engineering Isn’t Enough: How Four Bricks of Context Engineering Stop RAG Hallucinations appeared first on Towards Data Science.
I Tried Fine-Tuning a Robot AI Model on Colab. Here Is What Worked
A reproducible 100-step LoRA fine-tuning run for OpenVLA, with dataset checks, Colab setup, training metrics, and W B evidence. The post I Tried Fine-Tuning a Robot AI Model on Colab. Here Is What Worked appeared first on Towards Data Science.
How Much of a Data Science Workflow Can Run on a GPU Today? Part 1: Accelerating Data Preparation
Exploring GPU acceleration with cuDF, cudf.pandas, and the Polars GPU Engine The post How Much of a Data Science Workflow Can Run on a GPU Today? Part 1: Accelerating Data Preparation appeared first on Towards Data Science.
Are Your ML Experiments a Mess? Here’s the Fix
A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow. The post Are Your ML Experiments a Mess? Here’s the Fix appeared first on Towards Data Science.
Control Design for a Rideable Animatronic Two-Wheeled Robot with Quadruped Form
arXiv:2607.16302v1 Announce Type: new Abstract: In recent years, motorcycle popularity has been declining, particularly among the younger generations. To rekindle interest in motorcycles among this demographic, we developed a rideable two-wheeled robot equipped with four limbs as a future partner mobility concept and intended for use in public events. The character reproduced by this robot carries the protagonist on its back and exhibits a dynamic quadrupedal gait. To maximize the riding experience, we aimed to match the robot's weight and size to the character's specifications while ensuring rider safety and enabling expressive movements of limbs, wrists, ankles, and facial features. However, achieving locomotion solely through limb movement would require excessive motor output and increased limb strength, resulting in higher weight and extremely slow gait, thereby reducing character fidelity. To overcome these challenges, the robot performs its primary locomotion using a self-balanci
Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions
arXiv:2607.16196v1 Announce Type: new Abstract: Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect. This study presents a complete open-source MATLAB-based framework for the development and validation of compact deep learning models for affective touch recognition in soft interactive companions. As a primary contribution, a diverse FAIR-compliant dataset of 1326 labelled gesture sequences collected from 25 participants spanning children, teenagers, and adults is made publicly available, providing a reusable resource for future research in affective touch recognition. Through systematic architecture and hyperparameter exploration across 468 CNN models, the study identifies compact dilated one-dimensional convolutional neural networks (1D CNNs) as the most effective solution, with a 13.2k-parameter model ac
HyperDCM: Dynamic Cluster Memory Replay in Hyperbolic Space for Continual Robotic Navigation Across Scenes
arXiv:2607.16267v1 Announce Type: new Abstract: Continual learning in visual navigation remains challenging due to catastrophic forgetting and the difficulties associated with adapting to diverse and evolving environments. To address these issues, we propose Hyperbolic Dynamic Cluster Memory (HyperDCM), a structure-aware memory mechanism that enhances diffusion policy-based navigation through scene graph modeling and principled memory replay. HyperDCM extracts semantic scene triples from RGB observations using large vision-language models, encodes them into scene graph embeddings via a Relational Graph Convolutional Network (R-GCN), and projects the embeddings into hyperbolic space to enhance structural separability and retention in continual navigation. A dynamic clustering and structure-sensitive update strategy selects representative samples for memory replay, thereby preserving knowledge diversity and mitigating catastrophic forgetting. Experiments on multi-scene indoor and outdoor
Rater State Bias in RLHF Preference Data: An Audit Framework
arXiv:2607.16195v1 Announce Type: new Abstract: We identify a structured confound in Reinforcement Learning from Human Feedback (RLHF). Pairwise preference labels are intended to reflect the compared outputs, but they may also reflect the rater's state during annotation. Under sustained stressful or distressing conditions, raters' preferences may shift over time. As a result, preference data can encode rater state alongside judgments about response quality. These shifts differ from ordinary disagreement or random label noise. They are state dependent, can be shared across annotators working under similar conditions, and can propagate through reward modeling and policy optimization. We therefore propose rater state shift as a plausible and testable source of structured bias in RLHF preference data. This paper develops a hypothesis and an audit framework for studying this source of bias. We define rater state shift, rater state confound, and correlated rater state bias. We also define su
Reinforcement Learning-Guided NSGA-II Enhanced with Gray Relational Coefficient for Multi-Objective Optimization: Application to NASDAQ Portfolio Optimization
arXiv:2607.16194v1 Announce Type: new Abstract: In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve convergence and diversity of Pareto fronts. The agent adapts evolutionary parameters online using metrics of hypervolume, feasibility, and diversity, while the GRC tournament operator ranks parents via a unified score considering dominance rank, crowding distance, and proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR b
DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth
arXiv:2607.16203v1 Announce Type: new Abstract: Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned images into structured representations by extracting textual, visual, and layout information. While numerous Optical Character Recognition (OCR) engines and multimodal large language models (MLLMs) have been developed for this purpose, selecting an appropriate document parsing solution for a given document collection remains challenging, particularly in label-scarce settings. In this work, we conduct a systematic evaluation of text recognition performance across a diverse set of OCR engines and state-of-the-art MLLMs on multiple scanned document benchmarks spanning different domains and languages. Motivated by the limited contextual reasoning capabilities of many OCR engines and the high cost of manual annotations, we propose DocOCR-Eval, an annotation-free evaluat
How to Run Claude Code Agents for 24+ Hours
Apply long-running coding agents to become a more productive engineer The post How to Run Claude Code Agents for 24+ Hours appeared first on Towards Data Science.