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We present MINT (Multilingual Indic Neural Transformer), a compact 14.7M parameter encoder-decoder architecture for abstractive summarization across seven Indic languages. MINT is designed specifically to operate within the memory envelope of a single commodity NVIDIA T4 GPU (15 GB VRAM), addressing the paradox in which models serving the most resource-constrained communities are themselves the most resource-intensive to deploy. The architecture incorporates Rotary Position Embeddings (RoPE), SiLU feed-forward activations, DropPath regularization, weight tying, and a custom 32,000-token SentencePiece Unigram […]

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We decompose the Kullback–Leibler generalization error (GE) — the expected KL divergence from the data distribution to the trained model — of unsupervised learning into three non-negative components: model error, data bias, and variance. The decomposition is exact for any e-flat model class and follows from two identities of information geometry: the generalized Pythagorean theorem and a dual e-mixture variance identity. As an analytically tractable demonstration, we apply the framework to $ε$-PCA, a regularized principal component analysis in […]

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In this review article, we introduce the problem of team interaction, cover the mathematics, results, discussion, conclusions, and a path forward. To begin, cognitive science assumes a 1:1 relationship between beliefs and actions, whether with games, concepts, preferences, rational choices, eyewitness accounts, or self-reported pain. Unfortunately, it generalizes to reinforcement for generative-AI (gen-AI), a lower form of learning which can not account for higher-level cognition, the resistance of biases to be rectified, the inability to predict successfully without […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## AI Products Have Terrible UX: Here’s Why By @deeflect [ 8 Min read ] Most AI products have terrible UX – not because the AI is bad, but because no one who understands both AI and design is building them. Read More. OpenAI Bought […]

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We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past experimental data. A key challenge in this setting is data scarcity: in many scientific applications, only small or poor-quality datasets are available, which severely limits the effectiveness of existing algorithms. Prior work has theoretically and empirically shown that performance of offline optimization algorithms depends on how well the surrogate model captures the optimization bias (i.e., ability […]

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Batch active learning (BAL) is a crucial technique for reducing labeling costs and improving data efficiency in training large-scale deep learning models. Traditional BAL methods often rely on metrics like Mahalanobis Distance to balance uncertainty and diversity when selecting data for annotation. However, these methods predominantly focus on the distribution of unlabeled data and fail to leverage feedback from labeled data or the model’s performance. To address these limitations, we introduce TrustSet, a novel approach that selects the […]

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World models have emerged as a pivotal research direction, with recent breakthroughs in generative AI underscoring their potential for advancing artificial general intelligence. For embodied AI, world models are critical for enabling robots to effectively understand, interact with, and make informed decisions in real-world physical environments. This survey systematically reviews recent progress in embodied world models, under a novel technical taxonomy. We hierarchically organize the field by model architectures, training methodologies, application scenarios, and evaluation approaches, thus offering […]

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Last post covered evaluation, monitoring, and model degradation. This one covers guardrails — how you prevent LLMs from hallucinating, leaking data, following malicious instructions, or generating harmful content in production systems. LLMs generate probabilistic outputs. In healthcare, finance, or legal — any regulated domain — you can’t have the model hallucinating symptoms, giving medical advice it shouldn’t, or producing content that causes harm. Guardrails are the safety net between what the model generates and what reaches the user. The Layered Architecture No single guardrail catches […]

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