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Are Home Teams Favoured by Referees in Football/Soccer?
Data Storytelling Series, Chapter 1 The post Are Home Teams Favoured by Referees in Football/Soccer? appeared first on Towards Data Science.
Agent Harness vs Loop vs Graph Engineering: A Technical Guide
One of your colleagues asserts that we require improved loop engineering, yet the fundamental issue lies within the harness itself. Others may create graphs with 40 nodes before they observe how the agent executes a given task at a single time. Does this sound like something you have encountered before? This ongoing confusion surrounding agent [ ] The post Agent Harness vs Loop vs Graph Engineering: A Technical Guide appeared first on Analytics Vidhya.
Using Agents as Tools
Building manager–specialist workflows with the OpenAI Agents SDK The post Using Agents as Tools appeared first on Towards Data Science.
Motion Planning for Mobile Manipulators Navigating Doorways via Model Predictive Control
arXiv:2608.00206v1 Announce Type: new Abstract: Navigating doorways is a fundamental capability for mobile manipulators operating in human environments, requiring coordinated motion between the mobile base and manipulator arm. This paper presents a motion planning framework that generates dynamically feasible and collision-free trajectories for autonomously opening and traversing both push and pull doors. The proposed method formulates the robot and door as a coupled dynamical system within a nonlinear Model Predictive Control (MPC) optimization framework. Manipulation feasibility is enforced through a penalty-based constraint, avoiding explicit arm kinematic modeling in the planner. Simulations and a hardware experiment demonstrate that the approach successfully plans feasible trajectories for door traversal.
Uncertainty-Aware Simulation-Based Inference for Operations Research with Large Language Models
arXiv:2608.00019v1 Announce Type: new Abstract: Deploying large language models (LLMs) for operations research (OR) tasks remains challenging because correctness depends on a coherent modeling process, not merely a correct final answer. Standard autoregressive generation operates on a myopic policy, which sometimes fails to anticipate whether a partial formulation can be validly extended into a globally consistent optimization model. Consequently, locally plausible steps may propagate into catastrophic downstream formulation or solver code errors. To address this, we propose an uncertainty-aware, training-free inference framework for OR mathematical modeling. Without updating model parameters, our method evaluates intermediate candidate steps using short lookahead simulations to quantify downstream predictive uncertainty or probability concentration. Candidates that demonstrate a higher likelihood of yielding coherent mathematical formulations are then dynamically selected via importan
Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark
arXiv:2608.00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it. A controller may answer directly, decompose a request, retrieve evidence, execute code, delegate to a specialist, or verify an intermediate result. Existing routing work largely selects model endpoints, retrieval depth, or tools in isolation. We introduce an executable benchmark and a budget-aware meta-router that composes heterogeneous operations from raw task text. The benchmark contains 216 training, 72 development, 108 held-out test, and 108 locked lexical-shift challenge tasks across data analysis, frozen-corpus research, and document processing. Outcomes are machine checked after operations execute. Independent regularized logistic heads predict operation probabilities from word and character features, are temperature-scaled on development data, and are greedily composed under route-cost and action-count budge
Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning
arXiv:2608.00113v1 Announce Type: new Abstract: In Formula 1, drivers optimize racing lines within tire grip limits to minimize lap times; however, in rally racing, drivers intentionally break traction to drift on loose surfaces. This maneuver rapidly aligns the vehicle for corner exits, ultimately reducing lap time. Autonomously executing such maneuvers formulates a complex dual-objective control problem: stabilizing highly nonlinear drift dynamics while strictly minimizing lap time. Addressing this challenge motivates the development of advanced Minimum-Lap-Time (MLT) drift control architectures. This paper proposes a planning-control framework specifically designed for MLT drifting scenario. First, we formulate an optimal control problem to generate a MLT drift planning trajectory, which is used as prior data to train a deep reinforcement learning drift controller. Given that drifting involves extremely large sideslip angles and is therefore challenging to learn directly, a Track-gu
Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On
Enterprise Document Intelligence [Vol.1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops). Knowing which layer you are standing on is half of building and debugging RAG The post Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On appeared first on Towards Data Science.
How to Build CLI Agents with Python & Ollama
Create a local CLI Agent from scratch completely for free The post How to Build CLI Agents with Python Ollama appeared first on Towards Data Science.
The AI Was the Easy Part: What Is a Forward-Deployed Engineer in a Supply Chain?
What actually makes a Forward Deployed Engineer, told through one supply chain project. The post The AI Was the Easy Part: What Is a Forward-Deployed Engineer in a Supply Chain? appeared first on Towards Data Science.
How Claude Help Me Build My $200k+ ML Resume
How use Claude to craft an outstanding resume that lands offers The post How Claude Help Me Build My $200k+ ML Resume appeared first on Towards Data Science.
Topology-Aware Data Movement for Disaggregated GPU Inference
arXiv:2607.28633v1 Announce Type: new Abstract: Disaggregated LLM inference creates a datacenter networking problem that no existing system solves correctly. When prefill and decode run on separate GPU pools, the KV cache must be transferred between them. For a 70B model this is 2.6 GB per request, exceeding 100 GB/s aggregate at production scale. Yet DistServe, Splitwise, and Mooncake all use uniform RDMA, ignoring that bandwidth between two GPUs varies by 72x depending on their physical relationship: 900 GB/s via NVLink within a domain, 50 GB/s via InfiniBand across nodes, 12.5 GB/s via TCP across data centers. We design a topology-aware transfer orchestrator that discovers interconnect hierarchy at startup and selects optimal transport per transfer. Three mechanisms work together: (1) pipelined layer-by-layer transfer that overlaps transmission with ongoing prefill, hiding 60 to 85 percent of latency behind computation; (2) NVLink domain-aware placement for Mixture-of-Experts models