Valve's Steam Machine: Everything we know The apps can be voice-controlled, but can't replace Siri. Apple plans to allow third-party voice-controlled AI apps in CarPlay, reports . Siri is the default voice assistant for things like controlling music and looking up directions, but future AI apps in CarPlay could handle the complicated, open-ended requests Siri can't answer. The expanded support would let developers like OpenAI or Google offer versions of their ChatGPT and Gemini apps for CarPlay. Similar functionality is possible just by connecting a smartphone to a car over Bluetooth and using an AI app's voice mode, but CarPlay support would presumably make the process a little more seamless.

Bloomberg reports that Apple will show off demonstrations of the revamped Siri in the second half of February. A new and improved Siri may finally make an appearance, but this time, it could be with a Google Gemini glow up. According to's Mark Gurman, Apple wants to announce a new Siri in the second half of February that will show off the results of its recently announced partnership with Google and offer demonstrations of the Gemini-powered capabilities. After this reveal, Gurman reported that the new Siri will make its way to iOS 26.4, Apple has been meaning to launch its next-gen Siri ever since its announcement at WWDC 2024, but now we know that this Gemini-powered Siri will behave more like an AI chatbot, similar to OpenAI's ChatGPT, thanks to another report from last week.

Bungie's Marathon arrives on March 5 How to claim Verizon's $20 outage credit Apple needs a better Siri, not an unproven wearable. So it's come to this: Apple is reportedly working on a wearable AI pin . According to, it is going to be a small device with multiple cameras, a speaker, microphones and wireless charging. It sounds like the perfect gadget to pair with the long-awaited AI-powered Siri update, which will also reportedly work as a chatbot . But while many Apple rumors conjure up an air of excitement, the notion of an Apple AI pin sounds downright baffling. Worse, it just seems desperate.

Spatial Description Resolution, as a language-guided localization task, is proposed for target location in a panoramic street view, given corresponding language descriptions. Explicitly characterizing an object-level relationship while distilling spatial relationships are currently absent but crucial to this task. Mimicking humans, who sequentially traverse spatial relationship words and objects with a first-person view to locate their target, we propose a novel spatial relationship induced (SIRI) network. Specifically, visual features are firstly correlated at an implicit object-level in a projected latent space; then they are distilled by each spatial relationship word, resulting in each differently activated feature representing each spatial relationship. Further, we introduce global position priors to fix the absence of positional information, which may result in global positional reasoning ambiguities. Both the linguistic and visual features are concatenated to finalize the target localization. Experimental results on the Touchdown show that our method is around 24\% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.

Explicitly characterizing an object-level relationship while distilling spatial relationships are currently absent but crucial to this task. Mimicking humans, who sequentially traverse spatial relationship words and objects with a first-person view to locate their target, we propose a novel spatial relationship induced (SIRI) network.

Explicitly characterizing an object-level relationship while distilling spatial relationships are currently absent but crucial to this task. Mimicking humans, who sequentially traverse spatial relationship words and objects with a first-person view to locate their target, we propose a novel spatial relationship induced (SIRI) network.

The experiment is somewhat inadequate.

We introduce SIRI, Scaling Iterative Reinforcement Learning with Interleaved Compression, a simple yet effective RL approach for Large Reasoning Models (LRMs) that enables more efficient and accurate reasoning. Existing studies have observed repetitive thinking patterns in LRMs, and attempts to reduce them often come at the cost of performance. In this paper, we show that this trade-off can be overcome through a training regime that iteratively alternates between compressing and expanding the reasoning budget, by dynamically adjusting the maximum rollout length during training. The compression phase cuts the rollout length, forcing the model to make precise and valuable decisions within a limited context, which effectively reduces redundant tokens and increases reasoning density. The expansion phase then relaxes the length limit, providing space for the model to explore and plan in long-horizon settings. Remarkably, we find that after each compression-expansion cycle, the model's performance improves even as its output length decreases, steadily pushing it closer to the Pareto frontier in the performance-efficiency trade-off. Training on DeepSeek-R1-Distill-Qwen-1.5B, SIRI-low improves performance on AIME24 by 43.2% while reducing token usage by 46.9% after three iterations, and SIRI-high achieves the highest accuracy compared to all other methods (Figure 1). Our findings shed light on the potential of periodically oscillating the LRM's output truncation length during training to dynamically balance exploration and efficiency in reasoning, converging towards an optimal "sweet spot" between the two. Our models are publicly available.

Apple has announced several new products, including its new slimmer iPhone "Air" model with a "high-density battery" and a brand new processor, as well as an iPhone 17, the latest upgrade to its flagship smartphone . The tech giant, based in Cupertino, California in the US, unveiled the iPhone Air model as the star of the annual product launch event on Tuesday, with CEO Tim Cook calling it a "game-changer". The company said the base model iPhone 17 will have a brighter, more scratch-resistant screen. It also said the device will feature a new A19 processor chip, which will be made with three-nanometre (3nm) chipmaking technology and have improved capabilities for on-device artificial intelligence features. Apple said the iPhone 17 will also have a better front-facing camera with a differently shaped sensor to make horizontal selfies look better.