Anker has announced a new version of its wireless sleep buds that could be even more effective at delivering a peaceful slumber by blocking out disturbing noises using active noise cancellation. Previous versions of the Soundcore Sleep earbuds blocked external sounds passively using just a snug fit inside the ear, but the new Sleep A30 finally add ANC while still offering enough battery life to last the night.As with previous versions, Anker is making its new Soundcore Sleep A30 available for preorder through a Kickstarter crowdfunding campaign that’s launching today, while full availability of the earbuds is expected sometime in August 2025 through Amazon and Soundcore’s online store. At the Sleep A30 are quite a bit more expensive than last year’s Sleep A20, but the earliest Kickstarter backers can get the A30 discounted to The Sleep A30 are slimmer and smaller than previous versions, potentially making them more comfortable to wear overnight. Image: AnkerThe Sleep A30 earbuds are now 7 percent slimmer and feature a smaller design that ensures they don’t protrude from your ears so there’s reduced pressure while wearing them and laying on a pillow if you’re a side sleeper. To help you find a snug fit, Anker includes four sizes of silicone ear tips, three sizes of memory foam tips, and three sizes of ear wings.Anker claims the new Sleep A30 block up to 30dB of external noise, but the added ANC, which uses two mics positioned inside and outside your ears, does result in reduced battery life. The A20 could run for up to 14 hours on a single charge, but the A30 max out at up to nine hours on their own, or up to 45 hours with their charging case. However, that’s only when listening to white noise or other sounds designed to help you fall asleep that are stored on the buds themselves. When streaming music or podcasts from a phone, battery life is further reduced to up to 6.5 hours or 35 hours with the case.The Sleep A30’s charging case has been upgraded to detect snoring sounds and generate audio to mask them. Image: AnkerThe Sleep A30’s charging case has been upgraded with what Anker is calling “Adaptive Snore Masking technology.” If it detects the sounds of snoring from another person nearby, it analyzes the volume and frequency of the sounds and generates “noise masking audio” that’s sent to the buds to help block it out.The new earbuds also feature sleep monitoring and sleep position tracking, allowing you to see how restful or eventful your night was through the Soundcore mobile app; a private repeatable alarm with snooze functionality; and a Find My Earbud feature should they fall out in the night and get lost in the sheets.See More: #ankers #soundcore #sleep #earbuds #finally

The Inefficiency of Static Chain-of-Thought Reasoning in LRMs Recent LRMs achieve top performance by using detailed CoT reasoning to solve complex tasks. However, many simple tasks they handle could be solved by smaller models with fewer tokens, making such elaborate reasoning unnecessary. This echoes human thinking, where we use fast, intuitive responses for easy problems and slower, analytical thinking for complex ones. While LRMs mimic slow, logical reasoning, they generate significantly longer outputs, thereby increasing computational cost. Current methods for reducing reasoning steps lack flexibility, limiting models to a single fixed reasoning style. There is a growing need for adaptive reasoning that adjusts effort according to task difficulty.  Limitations of Existing Training-Based and Training-Free Approaches Recent research on improving reasoning efficiency in LRMs can be categorized into two main areas: training-based and training-free methods. Training strategies often use reinforcement learning or fine-tuning to limit token usage or adjust reasoning depth, but they tend to follow fixed patterns without flexibility. Training-free approaches utilize prompt engineering or pattern detection to shorten outputs during inference; however, they also lack adaptability. More recent work focuses on variable-length reasoning, where models adjust reasoning depth based on task complexity. Others study “overthinking,” where models over-reason unnecessarily. However, few methods enable dynamic switching between quick and thorough reasoning—something this paper addresses directly.  Introducing OThink-R1: Dynamic Fast/Slow Reasoning Framework Researchers from Zhejiang University and OPPO have developed OThink-R1, a new approach that enables LRMs to switch between fast and slow thinking smartly, much like humans do. By analyzing reasoning patterns, they identified which steps are essential and which are redundant. With help from another model acting as a judge, they trained LRMs to adapt their reasoning style based on task complexity. Their method reduces unnecessary reasoning by over 23% without losing accuracy. Using a loss function and fine-tuned datasets, OThink-R1 outperforms previous models in both efficiency and performance on various math and question-answering tasks.  System Architecture: Reasoning Pruning and Dual-Reference Optimization The OThink-R1 framework helps LRMs dynamically switch between fast and slow thinking. First, it identifies when LRMs include unnecessary reasoning, like overexplaining or double-checking, versus when detailed steps are truly essential. Using this, it builds a curated training dataset by pruning redundant reasoning and retaining valuable logic. Then, during fine-tuning, a special loss function balances both reasoning styles. This dual-reference loss compares the model’s outputs with both fast and slow thinking variants, encouraging flexibility. As a result, OThink-R1 can adaptively choose the most efficient reasoning path for each problem while preserving accuracy and logical depth.  Empirical Evaluation and Comparative Performance The OThink-R1 model was tested on simpler QA and math tasks to evaluate its ability to switch between fast and slow reasoning. Using datasets like OpenBookQA, CommonsenseQA, ASDIV, and GSM8K, the model demonstrated strong performance, generating fewer tokens while maintaining or improving accuracy. Compared to baselines such as NoThinking and DualFormer, OThink-R1 demonstrated a better balance between efficiency and effectiveness. Ablation studies confirmed the importance of pruning, KL constraints, and LLM-Judge in achieving optimal results. A case study illustrated that unnecessary reasoning can lead to overthinking and reduced accuracy, highlighting OThink-R1’s strength in adaptive reasoning.  Conclusion: Towards Scalable and Efficient Hybrid Reasoning Systems In conclusion, OThink-R1 is a large reasoning model that adaptively switches between fast and slow thinking modes to improve both efficiency and performance. It addresses the issue of unnecessarily complex reasoning in large models by analyzing and classifying reasoning steps as either essential or redundant. By pruning the redundant ones while maintaining logical accuracy, OThink-R1 reduces unnecessary computation. It also introduces a dual-reference KL-divergence loss to strengthen hybrid reasoning. Tested on math and QA tasks, it cuts down reasoning redundancy by 23% without sacrificing accuracy, showing promise for building more adaptive, scalable, and efficient AI reasoning systems in the future.  Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Building AI-Powered Applications Using the Plan → Files → Code Workflow in TinyDevSana Hassanhttps://www.marktechpost.com/author/sana-hassan/MemOS: A Memory-Centric Operating System for Evolving and Adaptive Large Language ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Google AI Unveils a Hybrid AI-Physics Model for Accurate Regional Climate Risk Forecasts with Better Uncertainty AssessmentSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Run Multiple AI Coding Agents in Parallel with Container-Use from Dagger #othinkr1 #dualmode #reasoning #framework #cut

Here's what we looked at this week:Objets d'esign: Lexon is releasing speaker and lamp versions of Jeff Koons' Balloon Dog sculpture. Volvo's new Multi-Adaptive Safety Belt compensates for different sizes, shapes and crash severities.Dometic's designey coolers use a different manufacturing method. Wandercraft's Eve, the world's first self-balancing exoskeleton, allows people to walk again.U.C. Berkeley's tiny pogo robot has a unique locomotion style.BARE designs a better—and less expensive—Dutch oven featuring a host of UX improvements.Clever materials use: How to clear standing water on a flat roof using rope.Architecture that works with challenging terrain, not against it: The Zig-Zag Resort, by JA Joubert and UNS Architects.Industrial design firm APE creates the Echo Pro, a perfect-fitting bike helmet with a novel adjustment mechanism.The Splay Max: A folding portable 35" monitor.Industrial Design student work: Dashiell Schaeffer's Curvesse rocking chair, made from a single sheet of plywood.These unusual, "anti-ligature" doorknobs are designed with a grim functional purpose.Designey tool kits: A trend with legs.BareBag's unusual design approach: Bags that serve as hanging points for other bags.From Germany, the NOHRD SlimBeam is a handcrafted, attractive piece of home exercise equipment.Why America's streetlights have been turning purple.When industrial design is subject to aftermarket modifications: BoxPlates to undo the PlayStation 5's look.This ShowerClear design fixes the mold problem all showerheads have.Industrial design case study: Curve ID tackles industrial kitchen equipment for JAVAR. #core77 #weekly #roundup

Things are warming up as Remedy’s FBC: Firebreak approaches its June 17 launch on PlayStation 5 as part of the PlayStation Plus Game Catalog. We chatted with Communications Director Thomas Puha, Lead Level Designer Teemu Huhtiniemi, Lead Designer/Lead Technical Designer Anssi Hyytiainen, and Game Director/Lead Writer Mike Kayatta about some of the fascinating and often hilarious development secrets behind the first-person shooter. PlayStation Blog: First, what PS5 and PS5 Pro features did you utilize? Thomas Puha: We’ll support 3D Audio, and we’re prioritising 60 FPS on both formats. We’re aiming for FSR2 with an output resolution of 2560 x 1440on PS, and PSSR with an output resolution of 3840×2160on PS5 Pro. Some of the DualSense wireless controller’s features are still a work in progress, but we’re looking to use haptic feedback in a similar way to our previous titles, such as Control and Alan Wake 2. For example, we want to differentiate the weapons to feel unique from each other using the adaptive triggers. Going into the game itself, were there any other influences on its creation outside of Control? Mike Kayatta: We looked at different TV shows that had lots of tools for going into a place and dealing with a crisis. One was a reality show called Dirty Jobs, where the host Mike Rowe finds these terrible, dangerous, or unexpected jobs that you don’t know exist, like cleaning out the inside of a water tower. We also looked at PowerWash Simulator. Cleaning dirt is oddly meditative and really fulfilling. It made me wish a zombie attacked me to break the Zen, and then I’d go right back to cleaning. And we were like, that would be pretty fun in the game. Play Video Were there specific challenges you faced given it’s your first multiplayer game and first-person shooter? Anssi Hyytiainen: It’s radically different from a workflow point of view. You can’t really test it alone, necessarily, which is quite a different experience. And then there are times when one player is missing things on their screen that others are seeing. It was like, “What are you shooting at?” What’s been your favorite moments developing the game so far? Teemu Huhtiniemi: There were so many. But I like when we started seeing all of these overlapping systems kind of click, because there’s a long time in the development where you talk about things on paper and have some prototypes, but you don’t really see it all come together until a point. Then you start seeing the interaction between the systems and all the fun that comes out of that. Kayatta: I imagine there’s a lot of people who probably are a little skeptical about Remedy making something so different. Even internally, when the project was starting. And once we got the trailer out there, everyone was so nervous, but it got a pretty positive reaction. Exposing it to the public is very motivating, because with games, for a very long time, there is nothing, or it is janky and it’s ugly and you don’t find the fun immediately. Were there any specific ideals you followed while you worked on the game? Kayatta: Early on we were constantly asking ourselves, “Could this only happen in Control or at Remedy?” Because the first thing you hear is, “Okay, this is just another co-op multiplayer shooter” – there’s thousands of them, and they’re all good. So what can we do to make it worth playing our game? We were always saying we’ve got this super weird universe and really interesting studio, so we’re always looking at what we could do that nobody else can. Huhtiniemi: I think for me it was when we chose to just embrace the chaos. Like, that’s the whole point of the game. It’s supposed to feel overwhelming and busy at times, so that was great to say it out loud. Kayatta: Yeah, originally we had a prototype where there were only two Hiss in the level, but it just didn’t work, it wasn’t fun. Then everything just accidentally went in the opposite direction, where it was super chaos. At some point we actually started looking at Overcooked quite a bit, and saying, “Look, just embrace it. It’s gonna be nuts.” How did you finally decide on the name FBC: Firebreak, and were there any rejected, alternate, or working titles? Kayatta: So Firebreak is named after real world firebreaks, where you deforest an area to prevent a fire from spreading, but firebreaks are also topographical features of the Oldest House. And so we leaned into the term being a first responder who stops fires from spreading. The FBC part came from not wanting to put ‘Control’ in the title, so Control players wouldn’t feel like they had to detour to this before Control 2, but we didn’t want to totally detach from it either as that felt insincere. An external partner pitched a title. They were very serious about talking up the game being in the Oldest House, and then dramatically revealed the name: Housekeepers. I got what they were going for, but I was like, we cannot call it this. It was like you were playing as a maid!   FBC: Firebreak launches on PS5 June 17 as a day on PlayStation Plus Game Catalog title. #fbc #firebreak #developers #discuss #inspiration