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“Los Movimientos”: The Routing Problem That Nearly Broke My Spirit
Using mathematical optimization to solve a pickup-and-delivery problem with time windows. The post “Los Movimientos”: The Routing Problem That Nearly Broke My Spirit appeared first on Towards Data Science.
Reducing Human Annotation with ML Active Learning
In a world where human time is expensive, learn how to use it only when really necessary The post Reducing Human Annotation with ML Active Learning appeared first on Towards Data Science.
The Most Beautiful Statistic: The History and the Science of the Humble Mean
The mean keeps making its usefulness felt in all sorts of situations, often in truly non-obvious ways The post The Most Beautiful Statistic: The History and the Science of the Humble Mean appeared first on Towards Data Science.
Claude Opus 5: Near-Frontier Intelligence, On a Dial
Anthropic has released Claude Opus 5. The fourth model in two months, if you are keeping count. Most people are not. This one matters more than the count suggests. Opus is the workhorse tier, the model that does the actual paid work, and it just got a step change rather than a bump. Anthropic s own [ ] The post Claude Opus 5: Near-Frontier Intelligence, On a Dial appeared first on Analytics Vidhya.
How I Reproduced BM25, Dense Retrieval, and SPLADE on a 16GB MacBook
A practical reproduction of three retrieval baselines, including the crashes, fixes, and score checks that matter for RAG systems. The post How I Reproduced BM25, Dense Retrieval, and SPLADE on a 16GB MacBook appeared first on Towards Data Science.
Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees
arXiv:2607.21623v1 Announce Type: new Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal prediction with finite-sample-corrected Adaptive Prediction Sets, calibration assessment, drift detection via RFF-approximated Maximum Mean Discrepancy, fairness monitoring with bootstrap confidence intervals, a DAG-based pipeline orchestrator, and a result storage API. We validate four key methodological concerns. First, empirical coverage is consistent with the marginal conformal guarantee across K=50 random calibration/test splits, with mean coverage within 1.4 percentage points of the nominal target. Second, all four MMLU answer tokens appear in the top-20 logprobs with 0% imputation needed, and simulated imputation at 10% produces less than 1.5% coverage impact. Third, RFF-MMD achieves 100% detection power for mild and severe drift at the median heuristic bandwidth, with Type I error b
Progress Reward Modeling for Robotic Learning: A Comprehensive Survey
arXiv:2607.21655v1 Announce Type: new Abstract: Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain whether the current behavior is making progress, remaining unchanged, or undoing earlier progress. For this reason, recent studies have increasingly explored progress rewards that provide feedback during task execution. However, the current literature lacks a shared framework. Existing methods use different observations, goal specifications, output signals, supervision sources, and evaluation protocols. This makes it difficult to compare them and understand what their results actually validate. In this survey, we provide a unified view of progress reward modeling for robotic learning. We organize the field in three connected steps. We first study the interface of a progress model. This defines the problem from the outside by asking what information the model rece
On the Depth Scalability of Logic Gate Networks
arXiv:2607.21633v1 Announce Type: new Abstract: Logic Gate Networks (LGNs) implement computation through compositions of Boolean operations, yet unlike classical Boolean circuits, existing LGNs do not reliably benefit from increased depth. We identify two distinct causes: optimization collapse in deep relaxed LGNs and a topology-induced limitation that persists even when skip-biased initialization and straight-through estimation stabilize training. Thus, trainability alone is insufficient; deeper layers must also receive information that supports useful computation. We introduce Input-Anchored Logic Gate Networks (IALGNs), in which each gate combines an evolving hidden feature with a direct input anchor. This topology preserves a computational spine while conditioning every layer on the original input. We show that a depth-D path can depend on up to D+1 input bits and establish a strict path-wise depth hierarchy. Random-k anchor relaxation further improves anchor selection without rela
Learning Diverse Humanoid Tasks via Synthetic Video Scenarios without Real World Data
arXiv:2607.21648v1 Announce Type: new Abstract: The human-like morphology of humanoid robots grants them exceptional potential for agile and versatile motor capabilities, but it also introduces significant challenges in acquiring complex skills. Traditional Learning-from-Demonstrations methods are often constrained by the high cost of collecting real-world data, the difficulty of capturing motion-specific behaviors, and the limited diversity of demonstrations across individuals. Moreover, even for the same task, humans may execute the motion in multiple distinct ways. In this paper, we propose a new framework that leverages the power of Generative AI to convert textual prompts into realistic and diverse sequences of human body movements, enabling the robot to observe multiple variations of how a single task can be performed. These synthetic demonstrations are then used as a training resource, allowing the robot to learn a broad range of task-execution styles without requiring direct hu
How to Efficiently Prompt Claude Code
Maximize your efficiency with Claude Code The post How to Efficiently Prompt Claude Code appeared first on Towards Data Science.
How to Give an LLM Agent a Browser
Building a browser-use agent with OpenAI Agents SDK and Playwright MCP The post How to Give an LLM Agent a Browser appeared first on Towards Data Science.
Cracking the Data Science Case Study Interview
Data science case study interviews are not just about writing code. They test how you think through a problem, analyze data, make decisions, and explain your approach in a way that solves a real business challenge. In this guide, you ll learn a simple framework called SCOPE that you can use to approach almost any data [ ] The post Cracking the Data Science Case Study Interview appeared first on Analytics Vidhya.