Clair Obscur: Expedition 33 has been named game of the year in a record-breaking haul at this year's Game Awards. The French-developed role-playing game (RPG) cleaned up in nine of the 10 categories it was up for, with further wins in best narrative, best music and best performance. It fended off competition from Death Stranding 2, Nintendo platformer Donkey Kong Bananza, indie games Hollow Knight: Silksong and Hades 2, and medieval adventure Kingdom Come: Deliverance 2 to claim the top prize. During the ceremony in Los Angeles, players also got their first glimpses of two new Tomb Raider games, sequel Control Resonant and a new Star Wars role-playing game. Clair Obscur is set in a world where a supernatural being known as The Paintress prevents the population from growing past a certain age.

Clair Obscur: Expedition 33 won nine awards, including game of the year, while newly announced games at the show include the next project from Baldur's Gate 3 developer Larian Studios New titles were announced, celebrities appeared, and at one point, screaming people were suspended from the ceiling in an extravagant promotion for a new role-playing game. Acclaimed French adventure Clair Obscur: Expedition 33 began the night with 12 nominations - the most in the event's history - and ended it with nine awards. The Gallic favourite took game of the year, as well as awards for best game direction, best art direction, best narrative and best performance (for actor Jennifer English). Elsewhere, Hades II took best action game, Hollow Knight: Silksong won in best action/adventure and Arc Raiders won best multiplayer. There was a decent showing for the new(ish) Nintendo Switch 2, with Donkey Kong Bananza taking best family game and Mario Kart World scorching across the line with best sports/racing game.

It's rude to boast, but here in 2025, you've got to take the wins where you can get them. This morning, magazine announced its Person of the Year, and it's me. If you want to get all technical about it, 's Person of the Year is not a person at all but a collection of people: the architects of AI. One of the two covers released is a re-creation of the "Lunch Atop a Skyscraper" photograph from 1932, which depicted blue-collar ironworkers suspended hundreds of feet in the air during the construction of 30 Rockefeller Plaza. In its image, replaces these laborers with tech personalities such as Mark Zuckerberg, Elon Musk, Sam Altman, and Jensen Huang.

Pine is the Creative Director at TIME. To illustrate the choice of the Architects of AI as TIME's 2025 Person of the Year, we asked two separate artists to help us visualize the incredibly complex technological revolution that is currently underway. London-based illustrator and graphics animator Peter Crowther and digital painter Jason Seiler each created an image that speaks to the duality AI has produced - man vs. machine. Inspired by the inner workings of computer chips, Crowther's intricate AI structure looms large over the busy construction site.

Eating boogers is funny no matter your species. A hilariously lucky moment I caught of these these three lions yawning at the same time. Breakthroughs, discoveries, and DIY tips sent every weekday. Nature can be captivating, awe-inspiring, and downright metal . It can also be hilarious.

The 2025 Nobel Prize in Chemistry for Metal-Organic Frameworks (MOFs) and recent breakthroughs by Huanting Wang's team at Monash University establish angstrom-scale channels as promising post-silicon substrates with native integrate-and-fire (IF) dynamics. However, utilizing these stochastic, analog materials for deterministic, bit-exact AI workloads (e.g., FP8) remains a paradox. Existing neuromorphic methods often settle for approximation, failing Transformer precision standards. To traverse the gap "from stochastic ions to deterministic floats," we propose a Native Spiking Microarchitecture. Treating noisy neurons as logic primitives, we introduce a Spatial Combinational Pipeline and a Sticky-Extra Correction mechanism. Validation across all 16,129 FP8 pairs confirms 100% bit-exact alignment with PyTorch. Crucially, our architecture reduces Linear layer latency to O(log N), yielding a 17x speedup. Physical simulations further demonstrate robustness against extreme membrane leakage (beta approx 0.01), effectively immunizing the system against the stochastic nature of the hardware.

Multi-agent role-playing has recently shown promise for studying social behavior with language agents, but existing simulations are mostly monolingual and fail to model cross-lingual interaction, an essential property of real societies. We introduce MASim, the first multilingual agent-based simulation framework that supports multi-turn interaction among generative agents with diverse sociolinguistic profiles. MASim offers two key analyses: (i) global public opinion modeling, by simulating how attitudes toward open-domain hypotheses evolve across languages and cultures, and (ii) media influence and information diffusion, via autonomous news agents that dynamically generate content and shape user behavior. To instantiate simulations, we construct the MAPS benchmark, which combines survey questions and demographic personas drawn from global population distributions. Experiments on calibration, sensitivity, consistency, and cultural case studies show that MASim reproduces sociocultural phenomena and highlights the importance of multilingual simulation for scalable, controlled computational social science.

Abstract--In this paper, the time-varying formation tracking control of wheeled mobile robots with region constraint is investigated from a generalized Udwadia-Kalaba framework. The communication topology is directed, weighted and has a spanning tree with the leader being the root. By reformulating the time-varying formation tracking control objective as a constrained equation and transforming the region constraint by a diffeomor-phism, the time-varying formation tracking controller with the region constraint is designed under the generalized Udwadia-Kalaba framework. Compared with the existing works on time-varying formation tracking control, the region constraint is taken into account in this paper, which ensures the safety of the robots. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control strategy. VER the past three decades, cooperative control of wheeled mobile robots has attracted considerable attention [1]. The cooperative control of wheeled mobile robots is generally categorized into synchronization control [2]- [5], formation control [6]-[8], formation-containment control [9]-[11], and so on.

The Endless Tuning is a design method for a reliable deployment of artificial intelligence based on a double mirroring process, which pursues both the goals of avoiding human replacement and filling the so-called responsibility gap (Matthias 2004). Originally depicted in (Fabris et al. 2024) and ensuing the relational approach urged therein, it was then actualized in a protocol, implemented in three prototypical applications regarding decision-making processes (respectively: loan granting, pneumonia diagnosis, and art style recognition) and tested with such as many domain experts. Step by step illustrating the protocol, giving insights concretely showing a different voice (Gilligan 1993) in the ethics of artificial intelligence, a philosophical account of technical choices (e.g., a reversed and hermeneutic deployment of XAI algorithms) will be provided in the present study together with the results of the experiments, focusing on user experience rather than statistical accuracy. Even thoroughly employing deep learning models, full control was perceived by the interviewees in the decision-making setting, while it appeared that a bridge can be built between accountability and liability in case of damage.

We propose Point-PNG, a novel self-supervised learning framework that generates conditional pseudo-negatives in the latent space to learn point cloud representations that are both discriminative and transformation-sensitive. Conventional self-supervised learning methods focus on achieving invariance, discarding transformation-specific information. Recent approaches incorporate transformation sensitivity by explicitly modeling relationships between original and transformed inputs. However, they often suffer from an invariant-collapse phenomenon, where the predictor degenerates into identity mappings, resulting in latent representations with limited variation across transformations. To address this, we propose Point-PNG that explicitly penalizes invariant collapse through pseudo-negatives generation, enabling the network to capture richer transformation cues while preserving discriminative representations. To this end, we introduce a parametric network, COnditional Pseudo-Negatives Embedding (COPE), which learns localized displacements induced by transformations within the latent space. A key challenge arises when jointly training COPE with the MAE, as it tends to converge to trivial identity mappings. To overcome this, we design a loss function based on pseudo-negatives conditioned on the transformation, which penalizes such trivial invariant solutions and enforces meaningful representation learning. We validate Point-PNG on shape classification and relative pose estimation tasks, showing competitive performance on ModelNet40 and ScanObjectNN under challenging evaluation protocols, and achieving superior accuracy in relative pose estimation compared to supervised baselines.