What 10 years of trust-based platforms and one zero-to-one product unlocked about intent qualification along with the mechanism most marketing and growth teams are missing. I’ve spent over a decade working inside trust-based technology platforms. Consumer products where a stranger getting into your car, opening their home to you, or meeting you alone in a foreign city were the entire value proposition. Platforms which operated across 20+ markets or even across several continents, with millions of users. In […]

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arXiv:2602.00983v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models particularly in mathematics. Current approaches in this domain present a clear trade-off: PPO-style methods (e.g., GRPO/DAPO) offer training stability but exhibit slow learning trajectories due to their trust-region constraints on policy updates, while REINFORCE-style approaches (e.g., CISPO) demonstrate improved learning efficiency but suffer from performance instability as they clip importance sampling weights while […]

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arXiv:2502.05709v3 Announce Type: replace-cross Abstract: Time series prediction underpins a broad range of downstream tasks across many scientific domains. Recent advances and increasing adoption of black-box machine learning models for time series prediction highlight the critical need for uncertainty quantification. While conformal prediction has gained attention as a reliable uncertainty quantification method, conformal prediction for time series faces two key challenges: (1) textbf{leveraging correlations in observations and non-conformity scores to overcome the exchangeability assumption}, and (2) textbf{constructing prediction […]

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Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low-data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in-context generalization, provide a basis for addressing these limitations. We introduce a model-agnostic association rule learning […]

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arXiv:2602.18469v1 Announce Type: new Abstract: This introductory chapter explores the transformative role of artificial intelligence (AI) in reshaping the landscape of science education. Positioned at the intersection of tradition and innovation, AI is altering educational goals, procedures, learning materials, assessment practices, and desired outcomes. We highlight how AI-supported tools, such as intelligent tutoring systems, adaptive learning platforms, automated feedback, and generative content creation–enhance personalization, efficiency, and equity while fostering competencies essential for an AI-driven society, including critical thinking, […]

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arXiv:2604.08591v1 Announce Type: new Abstract: Hallucinations in large ASR models present a critical safety risk. In this work, we propose the textit{Spectral Sensitivity Theorem}, which predicts a phase transition in deep networks from a dispersive regime (signal decay) to an attractor regime (rank-1 collapse) governed by layer-wise gain and alignment. We validate this theory by analyzing the eigenspectra of activation graphs in Whisper models (Tiny to Large-v3-Turbo) under adversarial stress. Our results confirm the theoretical prediction: intermediate models […]

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My paper got rejected from an imaging venue (A*) because it lacked clinical validation and was more “NLP suited”. I’m very disappointed by the decision as the paper had strong methods and key findings suited to the specific venue. I’m thinking of EMNLP next, but I feel it is too NLP and my paper for sure will be lost. But I see an EMNLP workshop very suited to the paper. Are such workshops especially at such conferences any […]

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arXiv:2503.09411v2 Announce Type: replace-cross Abstract: The learning rate in stochastic gradient methods is a critical hyperparameter that is notoriously costly to tune via standard grid search, especially for training modern large-scale models with billions of parameters. We identify a theoretical advantage of learning rate annealing schemes that decay the learning rate to zero at a polynomial rate, such as the widely-used cosine schedule, by demonstrating their increased robustness to initial parameter misspecification due to a coarse grid search. […]

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arXiv:2603.08725v1 Announce Type: new Abstract: This review examines the rapidly evolving landscape of ultra-low-power edge processors, covering heterogeneous Systems-on-Chips (SoCs), neural accelerators, near-sensor and in-sensor architectures, and emerging dataflow and memory-centric designs. We categorize commercially available and research-grade platforms according to their compute paradigms, power envelopes, and memory hierarchies, and analyze their suitability for always-on and latency-critical Artificial Intelligence (AI) workloads. To complement the architectural overview with empirical evidence, we benchmark a 336 million Multiply-Accumulate (MAC) segmentation model […]

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