arXiv:2601.22228v1 Announce Type: new Abstract: Vision-Language Models (VLMs) perform well in 2D perception and semantic reasoning compared to their limited understanding of 3D spatial structure. We investigate this gap using relative camera pose estimation (RCPE), a fundamental vision task that requires inferring relative camera translation and rotation from a pair of images. We introduce VRRPI-Bench, a benchmark derived from unlabeled egocentric videos with verbalized annotations of relative camera motion, reflecting realistic scenarios with simultaneous translation and rotation around […]

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I already made a simple neural network and it works, however I struggle with finding a way to make it learn, I just can’t find any information about that, because most of the articles and videos cover only supervised learning which won’t work in my case, or don’t cover backpropagation at all. I just want to see if there are any articles or videos that explain this thoroughly. submitted by /u/KokorakaboboMax [link] [comments]

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I have been trying to solve classification problem on a low resource language. I am doing comparative analysis, LinearSVC and Logistic regression performed the best and the only models with 80+ accuracy and no overfitting. I have to classify it using deep learning model as well. I applied BERT on the dataset, model is ‘bert-base-multilingual-cased’, and I am fine tuning it, but issue is overfitting. Training logs: Epoch 6/10 | Train Loss: 0.4135 | Train Acc: 0.8772 | […]

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Offline Reinforcement Learning (RL) is a promising approach for next-generation wireless networks, where online exploration is unsafe and large amounts of operational data can be reused across the model lifecycle. However, the behavior of offline RL algorithms under genuinely stochastic dynamics — inherent to wireless systems due to fading, noise, and traffic mobility — remains insufficiently understood. We address this gap by evaluating Bellman-based (Conservative Q-Learning), sequence-based (Decision Transformers), and hybrid (Critic-Guided Decision Transformers) offline RL methods in […]

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Primary bottleneck for Enterprise AI is not the availability of tools or the identification of a tech stack, it is getting the data landscape in order. Success in 2026 is predicated on having total clarity of the underlying data infrastructure and establishing a foundation that is petabyte-scale, secure, and high-performing. Without a reliable data layer, AI initiatives remain experimental rather than transformational. Foundation (Scalable and Maintainable Data Acquisition) A useful litmus test for the engineering foundation is time […]

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Introduction It isn’t everyday that a company gets started based on one YouTube short. But I think that’s exactly what might happen to me after I watched this Alex Hormozi YouTube video and a while later, the idea clicked on September 1 2025. https://youtube.com/shorts/bn4N6z6MSIo?si=Qt2BbO-Q-ljehLsq&embedable=true The premise is simple: “The person asking the questions is the one closing (the deal)” – Alex Hormozi The thing was, I knew a little technical knowledge about Bitcoin. And I knew that there […]

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arXiv:2601.22185v1 Announce Type: new Abstract: The meme coin ecosystem has grown into one of the most active yet least observable segments of the cryptocurrency market, characterized by extreme churn, minimal project commitment, and widespread fraudulent behavior. While countless meme coins are deployed across multiple blockchains, they rely heavily on off-chain web and social infrastructure to signal legitimacy. These very signals are largely absent from existing datasets, which are often limited to single-chain data or lack the multimodal artifacts […]

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arXiv:2602.13218v1 Announce Type: new Abstract: Scaling verifiable training signals remains a key bottleneck for Reinforcement Learning from Verifiable Rewards (RLVR). Logical reasoning is a natural substrate: constraints are formal and answers are programmatically checkable. However, prior synthesis pipelines either depend on expert-written code or operate within fixed templates/skeletons, which limits growth largely to instance-level perturbations. We propose SSLogic, an agentic meta-synthesis framework that scales at the task-family level by iteratively synthesizing and repairing executable Generator–Validator program pairs in […]

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