We introduce Lumine, the first open recipe for developing generalist agents capable of completing hours-long complex missions in real time within challenging 3D open-world environments. Lumine adopts a human-like interaction paradigm that unifies perception, reasoning, and action in an end-to-end manner, powered by a vision-language model. It processes raw pixels at 5 Hz to produce precise 30 Hz keyboard-mouse actions and adaptively invokes reasoning only when necessary. Trained in Genshin Impact, Lumine successfully completes the entire five-hour Mondstadt main storyline on par with human-level efficiency and follows natural language instructions to perform a broad spectrum of tasks in both 3D open-world exploration and 2D GUI manipulation across collection, combat, puzzle-solving, and NPC interaction. In addition to its in-domain performance, Lumine demonstrates strong zero-shot cross-game generalization. Without any fine-tuning, it accomplishes 100-minute missions in Wuthering Waves and the full five-hour first chapter of Honkai: Star Rail. These promising results highlight Lumine's effectiveness across distinct worlds and interaction dynamics, marking a concrete step toward generalist agents in open-ended environments.

We present EdgeRunner 20B, a fine-tuned version of gpt-oss-20b optimized for military tasks. EdgeRunner 20B was trained on 1.6M high-quality records curated from military documentation and websites. We also present four new tests sets: (a) combat arms, (b) combat medic, (c) cyber operations, and (d) mil-bench-5k (general military knowledge). On these military test sets, EdgeRunner 20B matches or exceeds GPT-5 task performance with 95%+ statistical significance, except for the high reasoning setting on the combat medic test set and the low reasoning setting on the mil-bench-5k test set. Versus gpt-oss-20b, there is no statistically-significant regression on general-purpose benchmarks like ARC-C, GPQA Diamond, GSM8k, IFEval, MMLU Pro, or TruthfulQA, except for GSM8k in the low reasoning setting. We also present analyses on hyperparameter settings, cost, and throughput. These findings show that small, locally-hosted models are ideal solutions for data-sensitive operations such as in the military domain, allowing for deployment in air-gapped edge devices.

Enshrouded is a beautiful combination of Minecraft, Skyrim and resource gathering that makes it at least three games in one. My daughter told me I would love it and I ignored her for too long. I've tackled Elden Ring, but much prefer the often gentler combat of Enshrouded. It sometimes makes me feel like an elite fighter, then other times kicks my arse in precisely the right measures. Its real joy is the flexibility to spend your time doing whatever tickles your fancy. I'll spend a few hours growing crops to make a cake or smelting metals for better armour, then knock off a few quests to unlock new materials and weapons.

When Pragmata was first announced five years ago, it wasn't clear exactly what Resident Evil publisher Capcom was making. The debut trailer featured eerie, futuristic imagery, an astronaut, and a blond-haired little girl, but there was nothing concrete or clear about its content. And when it missed its 2022 release window and was "paused indefinitely" in 2023, it wasn't clear if Pragmata would ever come to be. That all changed on 4 June, when a brand-new trailer was broadcast during a PlayStation showcase. The blond-haired little girl turns out to be a weaponised android, accompanying an astronaut called Hugh (of course) through space-station shootouts. I played about 20 minutes of the game during Summer Game Fest the following weekend.

Over the years, ComBAT has become the standard method for harmonizing MRI-derived measurements, with its ability to compensate for site-related additive and multiplicative biases while preserving biological variability. However, ComBAT relies on a set of assumptions that, when violated, can result in flawed harmonization. In this paper, we thoroughly review ComBAT's mathematical foundation, outlining these assumptions, and exploring their implications for the demographic composition necessary for optimal results. Through a series of experiments involving a slightly modified version of ComBAT called Pairwise-ComBAT tailored for normative modeling applications, we assess the impact of various population characteristics, including population size, age distribution, the absence of certain covariates, and the magnitude of additive and multiplicative factors. Based on these experiments, we present five essential recommendations that should be carefully considered to enhance consistency and supporting reproducibility, two essential factors for open science, collaborative research, and real-life clinical deployment.

I've been wondering why everyone seems so hyped on Clair Obscur: Expedition 33. It's the debut game from Sandfall Interactive, an independent French studio with fewer than 30 employees, and it's attracted massive partnerships in video games and film over the past five years. Expedition 33 has a high-profile cast of voice actors, including Andy Serkis, Charlie Cox, Shala Nyx and Jennifer English. It received an Epic MegaGrant in 2022, it was picked up by Pacific Drive publisher Kepler Interactive in 2023, and it was a tentpole of Xbox's first showcase of 2025. Even though the game isn't out until April, Story Kitchen has already signed on to turn it into a live-action film.

Multi-UAV air combat is a complex task involving multiple autonomous UAVs, an evolving field in both aerospace and artificial intelligence. This paper aims to enhance adversarial performance through collaborative strategies. Previous approaches predominantly discretize the action space into predefined actions, limiting UAV maneuverability and complex strategy implementation. Others simplify the problem to 1v1 combat, neglecting the cooperative dynamics among multiple UAVs. To address the high-dimensional challenges inherent in six-degree-of-freedom space and improve cooperation, we propose a hierarchical framework utilizing the Leader-Follower Multi-Agent Proximal Policy Optimization (LFMAPPO) strategy. Specifically, the framework is structured into three levels. The top level conducts a macro-level assessment of the environment and guides execution policy. The middle level determines the angle of the desired action. The bottom level generates precise action commands for the high-dimensional action space. Moreover, we optimize the state-value functions by assigning distinct roles with the leader-follower strategy to train the top-level policy, followers estimate the leader's utility, promoting effective cooperation among agents. Additionally, the incorporation of a target selector, aligned with the UAVs' posture, assesses the threat level of targets. Finally, simulation experiments validate the effectiveness of our proposed method.

PS4/5, Xbox, PC, Nintendo Switch Taiwanese studio Red Candle Games broke through in 2019 with the first-person horror game, Devotion. Its follow-up, Nine Sols, is less grungy but no less distinct, a robust 2D action-platformer with an exquisite "taopunk" aesthetic. This vivid sci-fi world feels as if it is constructed as much from bamboo and jade as steel and microchips. Alongside absorbing exploration and blistering combat, you study and grow various strains of alien flora found aboard a labyrinthine spaceship. The ultimate goal is escape, but you may never actually want to leave the strange, bioluminescent garden you come to cultivate.

Machine learning (ML) models benefit from large datasets. Collecting data in biomedical domains is costly and challenging, hence, combining datasets has become a common practice. However, datasets obtained under different conditions could present undesired site-specific variability. Data harmonization methods aim to remove site-specific variance while retaining biologically relevant information. This study evaluates the effectiveness of popularly used ComBatbased methods for harmonizing data in scenarios where the class balance is not equal across sites. We find that these methods struggle with data leakage issues. To overcome this problem, we propose a novel approach "PrettYharmonize", designed to harmonize data by pretending the target labels. We validate our approach using controlled datasets designed to benchmark the utility of harmonization. Finally, using real-world MRI and clinical data, we compare leakageprone methods with "PrettYharmonize" and show that it achieves comparable performance while avoiding data leakage, particularly in site-target-dependence scenarios.

This study employs Natural Language Processing (NLP) to analyze the intricate linguistic and emotional dimensions within the plays of Bernard-Marie Kolt\`es, a central figure in contemporary French theatre. By integrating advanced computational techniques, we dissect Kolt\`es' narrative style, revealing the subtle interplay between language and emotion across his dramatic oeuvre. Our findings highlight how Kolt\`es crafts his narratives, enriching our understanding of his thematic explorations and contributing to the broader field of digital humanities in literary analysis.