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1-Click Rewriter Pro Review: The AI System That Helps You Transform Existing Content Into Fresh…
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Bending Spoons makes first post-IPO acquisition with $1.3B Airtable deal
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Apple Is Suing OpenAI and Moving Siri to Google Gemini. Here's What's Really Going On
Two of the biggest names in tech just went to war, and the fallout lands right in your pocket.Continue reading on Medium »
Data Leakage: The Silent Killer of Machine Learning Models
IntroductionContinue reading on Medium »
Two Boundaries for Trustworthy Educational AI
New preprints on executable assurance for AI-generated educational plugins and release control for pedagogical leakage in LLM tutorsContinue reading on Medium »
Military AI Robots: Opportunities And Ethical Challenges — The Future Of Intelligent Defense
Artificial Intelligence (AI) is reshaping industries worldwide, from healthcare and manufacturing to education and finance.Continue reading on No Time »
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.
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
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
AI This Week #01: What Happened, What Matters, and What I Think (July 27-August 2, 2026)
This was a strange week. The same people building increasingly powerful AI spent seven days arguing about slowing it down, securing it Continue reading on Medium »
The New Cybersecurity War Is Being Fought in Prompts
As AI systems gain access to emails, browsers, databases, and business tools, attackers no longer need to hack only the code. Sometimes Continue reading on Medium »