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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 »
Is Our Society Mature Enough For AI?
Tech is neutral, and that s why we need politicsContinue reading on An Injustice! »
Top 10 AI Tools for eCommerce in 2026
If you run an online store, you ve probably noticed how much time repetitive tasks can take. That s where AI tools can make a real Continue reading on Medium »
The studio behind Pokémon struggles to find its voice in Beast of Reincarnation
You have to imagine that the team at Game Freak is bursting with ideas. The studio puts out new mainline Pok mon adventures with a machinelike precision. But every so often it launches a curious experiment, whether it's an action title about a militarized elephant or turning horse races into a card game, showing that it's capable of more than the latest generation of pocket monsters. That's part of what makes the idea of Beast of Reincarnation so enticing. Here is an established studio breaking free of the franchise it has become known for to make its take on a blockbuster action game. Unfortunately new ideas are in short supply in Beast, an … Read the full story at The Verge.
Qwen3.8-Max arrives with a bold claim: it outperforms GPT-5.6 Sol Max and Fable 5 on agentic computer use
Chinese e-commerce and cloud giant Alibaba s famed Qwen team of AI researchers last night unveiled Qwen3.8-Max, a new flagship 2.4-trillion-parameter mixture-of-experts (MoE) multimodal large language model (LLM) that targets one of the most competitive corners of the frontier AI market: autonomous software engineering and long-horizon enterprise work. If the company s published benchmarks hold up under broader independent testing, Qwen3.8-Max doesn t merely compete with today s leading proprietary models — it surpasses several of them on some key benchmarks in agentic computing.Most notably, Qwen reports that Qwen3.8-Max scores 86.1 on the OSWorld-Verified benchmark measuring how well ahead of GPT-5.6 Sol Max (83.2) and Fable 5 (85.0), while also posting the highest reported score on PaperBench and leading or remaining highly competitive across software engineering, research reproduction, multimodal reasoning, and visual web development benchmarks.The release also signals a potentiall
Interviewing AI – What It Cannot Experience
After misalignment, a more direct questionContinue reading on Medium »