Ferrari's New Jony Ive-Designed EV Is Swathed in Glass and Aluminum We got a peek at the interior of Ferrari's new Luce electric car, which was dreamed up by famed ex-Apple designer and his firm, LoveFrom. It looks and feels a whole lot like an Apple product. Despite Ferrari dramatically scaling back its EV plans at the end of 2025, it's no exaggeration to say that the reveal of the Italian automaker's first full electric car is going to be automotive event of 2026. While the exterior is still under wraps, Ferrari has unveiled the interior of its upcoming electric vehicle designed by LoveFrom, the creative firm of Apple's former chief designer, Jony Ive. It may not turn out quite like the Project Titan car Apple worked on for a decade then killed in 2024, but it sure does look like it has similar DNA. "We are entering a new era in Ferrari," the company's CEO Benedetto Vigna said at the unveiling, which took place last week at San Francisco's pyramid-shaped Transamerica building.

Watch Party: The Best TAG in Years, a '60s Sensation, and Omega Goes All White It's LVMH Watch Week, so here's WIRED's pick of the timepieces that made their debut--plus one notable gatecrasher. The watch world is readying itself for the slew of new releases from the likes of Patek Philippe and Rolex when Watches and Wonders descends on Geneva in April. But this week, the watchmaker Omega and the luxury conglomerate LVMH both spotted a window of opportunity to get pieces out ahead of the annual gathering. Since 2020, LVMH has been kicking off each new year by serving up watches from its stable of brands, including Zenith, TAG Heuer, Hublot, and Louis Vuitton. Meanwhile, Omega--muscling in on LVMH's party somewhat--is leaning into its connection to next month's Winter Olympics in Italy, where it will once again serve as the event's official timekeeper.

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Multi-agent debate often wastes compute by using a fixed adversarial stance, aggregating without deliberation, or stopping on heuristics. We introduce MACI, an active controller with two independent dials that decouple information from behavior: an information dial that gates evidence by quality, and a behavior dial that schedules contentiousness from exploration to consolidation. A moderator tracks disagreement, overlap, evidence quality, and argument quality, and halts when gains plateau. We provide theory-lite guarantees for nonincreasing dispersion and provable termination, with a budget-feasible scheduler. Across clinical diagnosis and news-bias tasks, MACI improves accuracy and calibration while reducing tokens, and converts residual uncertainty into precision RAG plans that specify what to retrieve next. We use a cross-family LLM judge (CRIT) as a conservative soft weight and stop signal, validated for order invariance and judge-swap stability; stability depends on using high-capability judges. MACI turns debate into a budget-aware, measurable, and provably terminating controller.

Ensuring that neural models used in real-world applications cannot infer sensitive information, such as demographic attributes like gender or race, from text representations is a critical challenge when fairness is a concern. We address this issue through concept erasure, a process that removes information related to a specific concept from distributed representations while preserving as much of the remaining semantic information as possible. Our approach involves learning an orthogonal projection in the embedding space, designed to make the class-conditional feature distributions of the discrete concept to erase indistinguishable after projection. By adjusting the rank of the projector, we control the extent of information removal, while its orthogonality ensures strict preservation of the local structure of the embeddings. Our method, termed $\overline{\mathrm{L}}$EOPARD, achieves state-of-the-art performance in nonlinear erasure of a discrete attribute on classic natural language processing benchmarks. Furthermore, we demonstrate that $\overline{\mathrm{L}}$EOPARD effectively mitigates bias in deep nonlinear classifiers, thereby promoting fairness.

We will prove it by contradiction. To prove Lemma 2 we will use the following lemma. This is a special case of the simulation lemma (Kearns and Singh, 2002). We will prove it by contradiction. There is a sizeable body of literature that concentrates on the non-stationarity issues arising from having multiple agents learning simultaneously in the same environment (Laurent et al., 2011; In contrast, Foerster et al. (2018a) add an extra term to The works by Lowe et al. (2017) and Foerster The works by de Witt et al. (2020) and Y u et al. (2021) show that Y u et al. attribute the positive empirical results to the clipping parameter Global simulator, observation functions, and joint policy for n 0, ...,N/T do s The bar plots show the total runtime of training for 4M timesteps with the three simulators.

Operating home appliances, among the most common tools in every household, is a critical capability for assistive home robots. This paper presents ApBot, a robot system that operates novel household appliances by "reading" their user manuals. ApBot faces multiple challenges: (i) infer goal-conditioned partial policies from their unstructured, textual descriptions in a user manual document, (ii) ground the policies to the appliance in the physical world, and (iii) execute the policies reliably over potentially many steps, despite compounding errors. To tackle these challenges, ApBot constructs a structured, symbolic model of an appliance from its manual, with the help of a large vision-language model (VLM). It grounds the symbolic actions visually to control panel elements. Finally, ApBot closes the loop by updating the model based on visual feedback. Our experiments show that across a wide range of simulated and real-world appliances, ApBot achieves consistent and statistically significant improvements in task success rate, compared with state-of-the-art large VLMs used directly as control policies. These results suggest that a structured internal representations plays an important role in robust robot operation of home appliances, especially, complex ones.