Steve Tan

New research underscores a paradigm shift in performance optimization: CPU speeds now vastly outpace memory access, redirecting focus from instruction-level tweaks to data structure design. While compilers master low-level code, they falter on data layout transformations, elevating early architectural choices as critical for enterprise-scale systems. For AI/ML leaders in SaaS and fintech, this demands data-centric engineering to unlock ROI through superior efficiency and scalability. #EnterpriseAI #DigitalTransformation #TechLeadership

MeanFlow: Residual Learning for One-Step Image Generation Before the introduction of skip-connections in ResNets (2015), deep neural networks often struggled with optimization as depth increased. Training directly to map x→y was harder than learning the residual x→(y−x) and adding x back at the end. This residual formulation simplified the learning task and made deeper architectures practical. A similar situation has existed in image generation. Diffusion models can produce high-quality images from noise, but typically require dozens or hundreds of iterative refinement steps. One-step generation — mapping noise directly to an image — is possible, but has generally not matched the quality of multi-step approaches. The recent MeanFlow paper reframes this problem in a way that parallels residual learning. Instead of predicting the entire image from noise, it learns the average change required to transform noise into the final image — the analogue of a residual in this context. This “average velocity” is smoother and more structured than the raw image, making it easier to learn. Applying this change in one step produces results that close much of the quality gap with multi-step methods. In short, MeanFlow does for one-step generative modeling what skip-connections did for deep classification networks: it turns a complex direct mapping into a simpler residual-style prediction problem. #DeepLearning #GenerativeAI #ImageGeneration #NeuralNetworks #DiffusionModels #MachineLearning #MeanFlow #ResidualLearning #ResNet Paper: https://lnkd.in/g-74xvTK

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Neutral-atom Quantum Processors Achieve 84% Feature Selection Accuracy with 2.3% Redundancy Reduction Researchers demonstrate that programmable arrays of neutral atoms successfully identify the most important features in datasets, improving the accuracy of machine learning models while dramatically reducing the number of features required for effective classification. #quantum #quantumcomputing #technology https://lnkd.in/eEWiQtQ8

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Neutral-atom Quantum Processors Achieve 84% Feature Selection Accuracy with 2.3% Redundancy Reduction Researchers demonstrate that programmable arrays of neutral atoms successfully identify the most important features in datasets, improving the accuracy of machine learning models while dramatically reducing the number of features required for effective classification. #quantum #quantumcomputing #technology https://lnkd.in/e5EN-uDM

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Traditional causal modeling defines explicit signal flow from inputs to outputs—but as systems grow, it becomes rigid and hard to extend. Acausal modeling focuses on the physical relationships between components, letting equations form automatically. In this post, see how Dyad, built on Julia and #SciML, makes acausal #modeling scalable, reusable, and ideal for complex system design. https://lnkd.in/eTCEdqUZ #JuliaLang #SystemSimulation #ModelBasedDesign #Dyad

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Rachit Jain's walkthrough shows how to profile kernels with PyTorch tools, import traces into NVIDIA Nsight or Intel Advisor, and read the slanted ceilings that separate compute-bound from memory-bound regions.

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NeurIPS 2026 call for papers is live now. Make your AI agents get to work. Papers won't write themselves. Tell them to hurry! /s

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Research Paper Highlights: "Evaluating Large Language Models in Scientific Discovery", By Zhangde Song et al. Large language models are increasingly used in scientific research, but existing benchmarks fail to capture how real scientific discovery unfolds. Most evaluations focus on isolated factual questions, ignoring hypothesis formation, experimentation, and iterative reasoning. This work introduces a scenario-grounded Scientific Discovery Evaluation (SDE) framework to assess LLMs on realistic, expert-defined research workflows. Challenges: - Decontextualized evaluation: Traditional benchmarks overlook the project-level reasoning central to scientific discovery. - Misleading scaling signals: Gains from larger models and stronger reasoning do not translate proportionally to discovery tasks. - Hidden weaknesses: Existing benchmarks mask systematic reasoning gaps that emerge only in end-to-end research scenarios. Key Takeaways: - Scenario-grounded design: Expert-defined research projects are decomposed into modular scenarios across biology, chemistry, materials, and physics. - Two-level evaluation: Models are assessed on both question-level accuracy and project-level scientific reasoning, including hypothesis generation and interpretation. - Revealing insights: Top-tier LLMs show consistent performance gaps, large variability across scenarios, and no clear winner across discovery projects. This research emphasizes that true scientific intelligence cannot be measured by isolated QA performance alone. The SDE framework exposes fundamental limitations of current LLMs in discovery-driven reasoning, showing they remain far from scientific superintelligence. At the same time, it highlights LLMs’ emerging potential in guided exploration and provides a reproducible path forward for advancing AI-assisted scientific discovery. Further reading-https://lnkd.in/gYEpMaDB

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📣 #CCSL's position paper on #NearField-#ISAC has been accepted for publication in IEEE Wireless Communications Magazine. ⁉️Ultra-massive #MIMO systems operating within the NF region utilize spherical wavefronts, enabling precise beam manipulation in both angular and distance domains through #BeamFocusing. While this approach dramatically boosts communication capacity and sensing precision, it also introduces increased beam management complexity. 💡🎯 We propose a 3️⃣-Step interplay between communication codebooks and Space-Time Adaptive Processing (#STAP) to reduce overall beam training overhead and sensing complexity: 1️⃣ We begin by leveraging #FarField (FF) discrete Fourier transform (#DFT) codebooks, periodically swept during synchronization signal block (#SSB) transmissions by #5G networks. NF users observing DFT beams with spherical wavefronts experience an angular spread dependent on their angle and range, allowing them to reference a look-up table for coarse estimations. 2️⃣ The initial range and angle estimates are further refined using #polar #codebooks, which oversample beams within the candidate polar regions, enhancing estimation accuracy and reducing training overhead. 3️⃣ Utilizing the refined estimates, we reduce the dimensions of the #radar #datacube, enabling a low-complexity Space-Time Adaptive Processing (#STAP) execution. This results in a three-orders-of-magnitude reduction in computational complexity, providing highly precise estimates of range, angle, radial velocity, and transverse velocity (unattainable with planar wavefronts in the far-field) 🔍 This integrated approach significantly minimizes the overhead associated with beam-training in both angular and distance domains while enhancing estimation accuracy. By co-designing NF communication and sensing systems, our framework ensures high efficiency and seamless integration of both functionalities, paving the way for more intelligent and efficient future wireless networks. 👥 Ahmed Hussain, Asmaa Abdallah, Ph.D,Abdulkadir Çelik, Ahmed Eltawil 🏛 KAUST (King Abdullah University of Science and Technology) KAUST CEMSE 🔗ResearchGate: https://lnkd.in/eAbuHZjc IEEE IEEE Signal Processing Society IEEE Computer Society IEEE Xplore IEEE ComSoc Young Professionals ACM, Association for Computing Machinery ACM arXiv Machine Learning Research at arXiv IEEE Communications Society IEEE Access IEEE The Institute Proceedings of the IEEE Computer Engineering Computer Science Computer Networking Computer Literacy Shannon Wireless Huawei Ericsson 6G-life Wireless Networking Wireless Communications Systems Huawei Wireless 6G Flagship telecomhall International Telecommunication Union

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Memory Is Not Storage In Agentic AI ----------------------------------------- Hallucinations are often blamed on model uncertainty, but in agentic systems they are more often memory pathologies. Intelligence is not about how much a system remembers, but what it remembers, what it forgets, and when experience becomes belief. Short-term memory preserves coherence, episodic memory preserves honesty, and long-term memory preserves identity. Confuse them and the system becomes confidently wrong; separate them and learning becomes possible without drift. Memory is not storage, it is a theory of cognition. Models generate possibilities, decoding chooses actions, but memory determines who the system becomes over time. For a deeper cognitive view of this, see my blog on agentic memory and learning (https://lnkd.in/gmmreWqf). #AgenticAI #AIMemory #AIArchitecture #AIHallucinations #CognitiveAI

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> Sharing Resource < Survey on Quantum Federated Learning by Dinh C. Nguyen, Md Raihan Uddin, Shaba Shaon, Ratun Rahman, Octavia Dobre, Dusit Niyato Abstract: Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field. Link: https://lnkd.in/eRen6hgu #quantummachinelearning #quantumfederatedlearning #quantumalgorithms #research #paper

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Physics-Informed Neural ODEs with Scale-Aware Residuals for Learning Stiff Biophysical Dynamics Kamalpreet Singh Kainth, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar, Sreedat Panat https://lnkd.in/dtRD_ide This paper tackles a critical challenge in using Neural ODEs for modeling stiff biophysical systems, specifically the difficulty in achieving stable and accurate long-term predictions. The core idea is to improve training stability and accuracy by incorporating physics-informed regularization with scale-aware residual normalization within a Neural ODE framework. The authors propose "PI-NODE-SR," which combines two key elements: 1. Physics-Informed Regularization: They leverage the known governing equations (e.g., Hodgkin-Huxley) to define a physics-informed loss term that penalizes deviations from the expected behavior. This is a standard PINN approach, but the key is how they handle the residual. 2. Scale-Aware Residual Normalization: This is the novel contribution. Stiff systems often involve state variables evolving on vastly different timescales. Directly applying a physics-informed loss can lead to one timescale dominating the training process, hindering convergence and accuracy. To address this, they normalize the residual associated with each state variable by a scaling factor. This factor is chosen to balance the contributions of each variable to the overall loss, preventing the faster dynamics from overwhelming the slower ones. They use a low-order explicit solver (Heun method) to calculate the residual. The authors demonstrate the effectiveness of PI-NODE-SR on the Hodgkin-Huxley equations, showing that it can learn from a single oscillation and extrapolate accurately over longer time horizons. Importantly, they show that the method can recover morphological features in the gating variables that are typically only captured by higher-order solvers. ----- This work directly addresses a significant limitation of Neural ODEs in scientific applications: their struggle with stiff systems. By introducing scale-aware residual normalization, the authors provide a principled way to stabilize training and improve the accuracy of long-term predictions. This is particularly relevant for: - Biophysical modeling: Accurately simulating neuronal dynamics, cardiac electrophysiology, and other complex biological processes. - Chemical kinetics: Modeling reaction networks with widely varying reaction rates. - Multi-physics simulations: Situations where different physical processes evolve on different timescales. The ability to use lower-order solvers with neural correction to achieve accuracy comparable to higher-order methods has significant implications for computational efficiency. While the method's sensitivity to initialization is noted, the overall approach offers a promising direction for developing more robust and efficient Physics-Informed Neural ODEs for a wide range of scientific applications.

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𝑮𝒖𝒂𝒓𝒅𝒊𝒂𝒏-𝒓𝒆𝒈𝒖𝒍𝒂𝒓𝒊𝒛𝒆𝒅 𝑺𝒂𝒇𝒆 𝑶𝒇𝒇𝒍𝒊𝒏𝒆 𝑹𝒆𝒊𝒏𝒇𝒐𝒓𝒄𝒆𝒎𝒆𝒏𝒕 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝒇𝒐𝒓 𝑺𝒎𝒂𝒓𝒕 𝑾𝒆𝒂𝒏𝒊𝒏𝒈 𝒐𝒇 𝑴𝒆𝒄𝒉𝒂𝒏𝒊𝒄𝒂𝒍 𝑪𝒊𝒓𝒄𝒖𝒍𝒂𝒕𝒐𝒓𝒚 𝑫𝒆𝒗𝒊𝒄𝒆𝒔🫀🩺 Presented at NeurIPS by UCSD PhD CSE student, Ayşın Tümay, this research offers a great approach in the field of MedicalAI by assisting clinicians through AI powered decision making🥼 In other words, this research supports clinicians with an automated decision making strategy for gradually reducing dependence on Mechanical Circulatory (MCS) Devices, which act as a temporary blood flow support for patients who suffer from heart failure or cardiogenic shock, by utilizing historical patient data (offline) rather than real time in the moment (online) patient data - prioritizing safety for patient care in such a delicate weaning process. Some things to note is that in order to ensure safety, the Reinforcement Learning algorithm penalizes suggested actions which are highly disparate from its training data (Out Of Distribution Suppression) + expert clinical knowledge was incorporated in order to shape the AI's goal system towards specific medical desired outcomes (maintaining the patient stable through the transition off the medical device). For in depth technical details, author Ayşın Tümay has shared the article below! As someone who is deeply interested in AI for Medicine, thank you Aysin for sharing your work and for being a great resource! p.s I would definitely reach out if you are someone who is curious about AI for Cardiology👀

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Our latest PyTorch community blog from the SGLang Team covers how SGLang advances hybrid Mamba plus Attention models with improved memory efficiency, prefix caching, speculative decoding, and serving performance. 📖 Read the blog: https://lnkd.in/gfDZkZWF #PyTorch #AIInfrastructure #SGLang #LLM

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