KDE isn't the only organization reaching out to " as Microsoft prepares to end support for Windows 10. "Now, The Document Foundation, maker of LibreOffice, has also joined in to support the Endof10 initiative," reports the tech blog Neowin: The foundation writes: "You don't have to follow Microsoft's upgrade path. There is a better option that puts control back in the hands of users, institutions, and public bodies: Linux and LibreOffice. Together, these two programmes offer a powerful, privacy-friendly and future-proof alternative to the Windows + Microsoft 365 ecosystem." It further adds the "real costs" of upgrading to Windows 11 as it writes: "The move to Windows 11 isn't just about security updates. It increases dependence on Microsoft through aggressive cloud integration, forcing users to adopt Microsoft accounts and services. It also leads to higher costs due to subscription and licensing models, and reduces control over how your computer works and how your data is managed. Furthermore, new hardware requirements will render millions of perfectly good PCs obsolete.... The end of Windows 10 does not mark the end of choice, but the beginning of a new era. If you are tired of mandatory updates, invasive changes, and being bound by the commercial choices of a single supplier, it is time for a change. Linux and LibreOffice are ready — 2025 is the right year to choose digital freedom!" The first words on LibreOffice's announcement? "The countdown has begun...." Read more of this story at Slashdot.

Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025 Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerial (or satellite) image. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset. Key Takeaways: Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task. Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map. Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models. Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal. Challenge: Seeing the World from Two Different Angles The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-View (BEV) but are often limited to the ground plane, ignoring crucial vertical structures like buildings. FG2: Matching Fine-Grained Features The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map. Here’s a breakdown of their innovative pipeline: Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment. Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the vertical (height) dimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view. Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoF (x, y, and yaw) pose. Unprecedented Performance and Interpretability The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research. Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems. “A Clearer Path” for Autonomous Navigation The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them. Check out the Paper. 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. Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models

This article was originally published on GameDevDigest.comEnjoy!What was Radiant AI, anyway? - A ridiculously deep dive into Oblivion's controversial AI system and its legacyblog.paavo.meConsider The Horse Game - No I don’t think every dev should make a horse game (unlike horror, which I still think everyone should at least one). But I do think every developer should at least look at them, maybe even play one because, it is very important that you understand the importance of genre, fandom, and how visibility works. Even if you are not making a horse game, the lessons you can learn by looking at this sub genre are very similar to other genres, just not as blatantly clear as they are with horse games.howtomarketagame.comMaking a killing: The playful 2D terror of Psycasso® - I sat down with lead developer Benjamin Lavender and Omni, designer and producer, to talk about this playfully gory game that gives a classic retro style and a fresh (if gruesome) twist.UnityIntroduction to Asset Manager transfer methods in Unity - Unity's Asset Manager is a user-friendly digital asset management platform supporting over 70 file formats to help teams centralize, organize, discover, and use assets seamlessly across projects. It reduces redundant work by design, making cross-team collaboration smoother and accelerating production workflows.UnityVideosRules of the Game: Five Tricks of Highly Effective Designers - Every working designer has them: unique techniques or "tricks" that they use when crafting gameplay. Sure, there's the general game design wisdom that everyone agrees on and can be found in many a game design book, but experienced game designers often have very specific rules that are personal to them, techniques that not everyone knows about or even agrees with. In this GDC 2015 session, five experienced game designers join the stage for 10 minutes each to share one game design "trick" that they use.Game Developers ConferenceBinding of Isaac Style Room Generator in Unity [Full Tutorial] - Our third part in the series - making the rooms!Game Dev GarnetIntroduction to Unity Behavior | Unity Tutorial - In this video you'll become familiar with the core concepts of Unity Behavior, including a live example.LlamAcademyHow I got my demo ready for Steam Next Fest - It's Steam Next Fest, and I've got a game in the showcase. So here are 7 tips for making the most of this demo sharing festival.Game Maker's ToolkitOptimizing lighting in Projekt Z: Beyond Order - 314 Arts studio lead and founder Justin Miersch discuss how the team used the Screen Space Global Illumination feature in Unity’s High Definition Render Pipeline (HDRP), along with the Unity Profiler and Timeline to overcome the lighting challenges they faced in building Projekt Z: Beyond Order.UnityMemory Arenas in Unity: Heap Allocation Without the GC - In this video, we explore how to build a custom memory arena in Unity using unsafe code and manual heap allocation. You’ll learn how to allocate raw memory for temporary graph-like structures—such as crafting trees or decision planners—without triggering the garbage collector. We’ll walk through the concept of stack frames, translate that to heap-based arena allocation, and implement a fast, disposable system that gives you full control over memory layout and lifetime. Perfect for performance-critical systems where GC spikes aren’t acceptable.git-amendCloth Animation Using The Compute Shader - In this video, we dive into cloth simulation using OpenGL compute shaders. By applying simple mathematical equations, we’ll achieve smooth, dynamic movement. We'll explore particle-based simulation, tackle synchronization challenges with double buffering, and optimize rendering using triangle strips for efficient memory usage. Whether you're familiar with compute shaders or just getting started, this is the perfect way to step up your real-time graphics skills!OGLDEVHow we're designing games for a broader audience - Our games are too hardBiteMe GamesAssetsLearn Game Dev - Unity, Godot, Unreal, Gamemaker, Blender & C# - Make games like a pro.Passionate about video games? Then start making your own! Our latest bundle will help you learn vital game development skills. Master the most popular creation platforms like Unity, Godot, Unreal, GameMaker, Blender, and C#—now that’s a sharp-lookin’ bundle! Build a 2.5D farming RPG with Unreal Engine, create a micro turn-based RPG in Godot, explore game optimization, and so much more.__Big Bang Unreal & Unity Asset Packs Bundle - 5000+ unrivaled assets in one bundle. Calling all game devs—build your worlds with this gigantic bundle of over 5000 assets, including realistic and stylized environments, SFX packs, and powerful tools. Perfect for hobbyists, beginners, and professional developers alike, you'll gain access to essential resources, tutorials, and beta-testing–ready content to start building immediately. The experts at Leartes Studios have curated an amazing library packed with value, featuring environments, VFX packs, and tutorial courses on Unreal Engine, Blender, Substance Painter, and ZBrush. Get the assets you need to bring your game to life—and help support One Tree Planted with your purchase! 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There is currently 50+ packs with 1000's of sounds and hours of field recordings all perfect for game SFX and UI. - I think game designers can benefit from a wide range of sounds on the site, especially those that enhance immersion and atmosphere.signaturesounds.orgSmartAddresser - Automate Addressing, Labeling, and Version Control for Unity's Addressable Asset System.CyberAgentGameEntertainment Open SourceEasyCS - EasyCS is an easy-to-use and flexible framework for Unity, adopting a Data-Driven Entity & Actor-Component approach. It bridges Unity's classic OOP with powerful data-oriented patterns, without forcing a complete ECS paradigm shift or a mindset change. 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Demo available on SteamYou can subscribe to the free weekly newsletter on GameDevDigest.comThis post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.

A dangerous strain of bird flu is spreading in US livestockMediaMedium/Alamy Since Donald Trump assumed office in January, the leading US public health agency has pulled back preparations for a potential bird flu pandemic. But as it steps back, another government agency is stepping up. While the US Department of Health and Human Services (HHS) previously held regular briefings on its efforts to prevent a wider outbreak of a deadly bird flu virus called H5N1 in people, it largely stopped once Trump took office. It has also cancelled funding for a vaccine that would have targeted the virus. In contrast, the US Department of Agriculture (USDA) has escalated its fight against H5N1’s spread in poultry flocks and dairy herds, including by funding the development of livestock vaccines. This particular virus – a strain of avian influenza called H5N1 – poses a significant threat to humans, having killed about half of the roughly 1000 people worldwide who tested positive for it since 2003. While the pathogen spreads rapidly in birds, it is poorly adapted to infecting humans and isn’t known to transmit between people. But that could change if it acquires mutations that allow it to spread more easily among mammals – a risk that increases with each mammalian infection. The possibility of H5N1 evolving to become more dangerous to people has grown significantly since March 2024, when the virus jumped from migratory birds to dairy cows in Texas. More than 1,070 herds across 17 states have been affected since then. H5N1 also infects poultry, placing the virus in closer proximity to people. Since 2022, nearly 175 million domestic birds have been culled in the US due to H5N1, and almost all of the 71 people who have tested positive for it had direct contact with livestock. Get the most essential health and fitness news in your inbox every Saturday. Sign up to newsletter “We need to take this seriously because when [H5N1] constantly is spreading, it’s constantly spilling over into humans,” says Seema Lakdawala at Emory University in Georgia. The virus has already killed a person in the US and a child in Mexico this year. Still, cases have declined under Trump. The last recorded human case was in February, and the number of affected poultry flocks fell 95 per cent between then and June. Outbreaks in dairy herds have also stabilised. It isn’t clear what is behind the decline. Lakdawala believes it is partly due to a lull in bird migration, which reduces opportunities for the virus to spread from wild birds to livestock. It may also reflect efforts by the USDA to contain outbreaks on farms. In February, the USDA unveiled a $1 billion plan for tackling H5N1, including strengthening farmers’ defences against the virus, such as through free biosecurity assessments. Of the 150 facilities that have undergone assessment, only one has experienced an H5N1 outbreak. Under Trump, the USDA also continued its National Milk Testing Strategy, which mandates farms provide raw milk samples for influenza testing. If a farm is positive for H5N1, it must allow the USDA to monitor livestock and implement measures to contain the virus. The USDA launched the programme in December and has since ramped up participation to 45 states. “The National Milk Testing Strategy is a fantastic system,” says Erin Sorrell at Johns Hopkins University in Maryland. Along with the USDA’s efforts to improve biosecurity measures on farms, milk testing is crucial for containing the outbreak, says Sorrell. But while the USDA has bolstered its efforts against H5N1, the HHS doesn’t appear to have followed suit. In fact, the recent drop in human cases may reflect decreased surveillance due to workforce cuts, says Sorrell. In April, the HHS laid off about 10,000 employees, including 90 per cent of staff at the National Institute for Occupational Safety and Health, an office that helps investigate H5N1 outbreaks in farm workers. “There is an old saying that if you don’t test for something, you can’t find it,” says Sorrell. Yet a spokesperson for the US Centers for Disease Control and Prevention (CDC) says its guidance and surveillance efforts have not changed. “State and local health departments continue to monitor for illness in persons exposed to sick animals,” they told New Scientist. “CDC remains committed to rapidly communicating information as needed about H5N1.” The USDA and HHS also diverge on vaccination. While the USDA has allocated $100 million toward developing vaccines and other solutions for preventing H5N1’s spread in livestock, the HHS cancelled $776 million in contracts for influenza vaccine development. The contracts – terminated on 28 May – were with the pharmaceutical company Moderna to develop vaccines targeting flu subtypes, including H5N1, that could cause future pandemics. The news came the same day Moderna reported nearly 98 per cent of the roughly 300 participants who received two doses of the H5 vaccine in a clinical trial had antibody levels believed to be protective against the virus. The US has about five million H5N1 vaccine doses stockpiled, but these are made using eggs and cultured cells, which take longer to produce than mRNA-based vaccines like Moderna’s. The Moderna vaccine would have modernised the stockpile and enabled the government to rapidly produce vaccines in the event of a pandemic, says Sorrell. “It seems like a very effective platform and would have positioned the US and others to be on good footing if and when we needed a vaccine for our general public,” she says. The HHS cancelled the contracts due to concerns about mRNA vaccines, which Robert F Kennedy Jr – the country’s highest-ranking public health official – has previously cast doubt on. “The reality is that mRNA technology remains under-tested, and we are not going to spend taxpayer dollars repeating the mistakes of the last administration,” said HHS communications director Andrew Nixon in a statement to New Scientist. However, mRNA technology isn’t new. It has been in development for more than half a century and numerous clinical trials have shown mRNA vaccines are safe. While they do carry the risk of side effects – the majority of which are mild – this is true of almost every medical treatment. In a press release, Moderna said it would explore alternative funding paths for the programme. “My stance is that we should not be looking to take anything off the table, and that includes any type of vaccine regimen,” says Lakdawala. “Vaccines are the most effective way to counter an infectious disease,” says Sorrell. “And so having that in your arsenal and ready to go just give you more options.” Topics:

Home Is the Newly Revealed Xbox Handheld a Switch 2 Killer? News Is the Newly Revealed Xbox Handheld a Switch 2 Killer? 6 min read Published: June 14, 2025 Key Takeaways Xbox has announced two new handheld gaming devices in partnership with Asus: the ROG Xbox Ally and ROG Xbox Ally X. They’re expected to compete with Nintendo’s Switch 2, which has sold 3.5M units in just 4 days of its launch. Xbox aims to bring a wide range of game titles to portable handheld devices in order to cater to the gaming PC market. Xbox has entered the handheld gaming market with two new launches: the ROG Xbox Ally and ROG Xbox Ally X in partnership with ASUS. Interestingly, Nintendo released its Switch 2 just last week. The public has received it with much enthusiasm, seeing as it’s already sold around 3.5M units in the first four days of its release. Needless to say, Xbox and Nintendo will be direct competitors in the handheld segment now. We looked at the spec sheets and customer reviews, and both handheld gaming devices seem to have different target audiences. Let’s unpack them in detail. Memory and Storage The ROG Xbox Ally comes in standard white color and features the AMD Ryzen Z2 A Processor with 16GB of memory and 512GB of storage, with a 60Wh battery. The Ally X, on the other hand, comes in striking black with the AMD Ryzen AI Z2 Extreme Processor, 24 GB of memory, and 1 TB of storage, and a 80Wh battery. Source: Rog Ally Life Right off the bat, we believe that Microsoft has done a good job with the storage and processors. In comparison, the Nintendo Switch 2 has 12GB of memory with just 256GB of internal storage. On paper, the Xbox series looks to have an advantage over the Switch 2, which uses a custom NVIDIA T239 chipset for raw power. Nvidia’s DLSS (Deep Learning Super-Sampling), however, gives Switch 2 an advantage over Xbox’s AMD Ryzen processors. DLSS can render games at lower resolutions and, therefore, achieve higher in-use frame rates, which boosts its overall performance. Simply put, despite the gap in on-paper specifications, the Switch 2 may render comparable performance to the Xbox Ally. Take this with a pinch of salt, though, because we’ll only be able to confirm this once we get our hands on the new Xbox handhelds. Display Both the Xbox handhelds feature a 7-inch Full HD (FHD) screen with a 120 Hz refresh rate. In comparison, the Switch 2 screen is bigger, with a 7.9-inch display, also rendering at 120 Hz. However, Switch 2 also features HDR10, giving it a significant edge over Xbox Ally. HDR10 ensures a much wider range of brightness levels and a broader spectrum of colors, so the display looks more vibrant and lifelike. Plus, you’ll see more detailed blacks and whites on the screen with better realism and depth, enhancing your overall gaming experience. The Switch 2 also features VRR technology, which prevents screen tearing and reduces stutter. Notably, the Xbox Ally range has its own version of the VRR, FreeSync Premium. So, truth be told, you might not experience much of a difference in that area. However, HDR10 can definitely prove to be a winner for Switch 2. Product Market Fit While both the Switch 2 and new Xbox handhelds are apparently the same genre of products, Microsoft and Nintendo seem to have different target markets in their minds. Microsoft is focusing more on the Windows handheld market, targeting players who want an on-the-go PC gaming experience. With access to Game Pass and titles from Steam and Epic Games, the Xbox Ally offers a more comprehensive library of games. Nintendo, on the other hand, looks to build on the legacy of the OG Nintendo Switch, which has sold 152M units since its launch in 2017. It aims to tap in on the Nintendo fan base with original titles such as Mario and an improved gaming experience. Also, Xbox is in direct competition with Valve’s Steam Deck. Both are essentially handheld PCs with wide access to PC-compatible aggregated game libraries on the go. Xbox is also introducing the ‘Xbox Experience for Handheld’ feature for its new Ally range, which will make Windows 11 more compatible and optimized for its handheld device – something similar to Valve’s SteamOS on the Steam Deck. Xbox Exploring a New Market Segment The global mobile and handheld gaming market is expected to expand at a rate of 13.8% CAGR till 2034. It may reach a market value of $35.189B.  Valve’s Steam Deck managed to sell around 3.7M units by the end of 2024. On the other hand, Windows-based devices like the ASUS ROG Ally, Lenovo Legion Go, and MSI Claw have sold ~5.9M units so far as per early 2025 reports. This shows there’s certainly demand for Windows-based handheld gaming devices.  Plus, Xbox’s partnership with ASUS could bring along a brand-value advantage for the product. With a seamless Windows 11 experience on an on-the-go device, these sales figures are expected to swell once the Xbox Ally hits the shelves. However, pricing will be a key determinant. The Switch 2 currently sells at $449.99 – or $499.99 with the Mario Kart bundle. The Steam Deck starts at $399.00 and goes up to $649. This means that the price range according to the current market demand is around $400-$600. Anything more than that may result in market adoption issues. The original ASUS ROG Ally is currently priced at $499. There’s little doubt, however, that Xbox would add a premium to this price. So, we’re expecting the price of the ROG Xbox Ally to be around $600, while the ROG Xbox Ally X may cost more than $700. This means that Xbox will be participating in the premium handheld gaming sector, which is something Nintendo and Steam do not cater to. Let’s wait for confirmation regarding the pricing and the launch date. Remember, this was only a feature comparison of the two products, and we’re yet to test them out for a detailed hands-on gaming experience comparison. Stick around for that. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors. More from News View all View all

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