This paper presents a systematic comparison of neural architectures for English-to-Spanish machine translation. We implement and evaluate five model configurations ranging from vanilla LSTM encoder-decoders to Transformer models with pretrained embeddings. Using the OPUS-100 corpus (1M training pairs) and FLORES+ benchmark (2,009 test pairs), we evaluate translation quality using BLEU, chrF, and COMET metrics. Our best Transformer model achieves a BLEU score of 20.26, closing approximately 65% of the performance gap between our strongest LSTM baseline (BLEU 10.66) […]

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arXiv:2601.15484v1 Announce Type: new Abstract: Online encyclopedias are central to contemporary information infrastructures and have become focal points of debates over ideological bias. Wikipedia, in particular, has long been accused of left-leaning bias, while Grokipedia, an AI-generated encyclopedia launched by xAI, has been framed as a right-leaning alternative. This paper presents a comparative analysis of Wikipedia and Grokipedia on well-established politically contested topics. Specifically, we examine differences in semantic framing, political orientation, and content prioritization. We find that […]

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arXiv:2601.12179v1 Announce Type: new Abstract: Modern language models like GPT-3, BERT, and LLaMA require massive training data, yet with sufficient training they reliably learn to distinguish grammatical from ungrammatical sentences. Children aged as young as 14 months already have the capacity to learn abstract grammar rules from very few exemplars, even in the presence of non-rule-following exceptions. Yang’s (2016) Tolerance Principle defines a precise threshold for how many exceptions a rule can tolerate and still be learnable. The […]

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arXiv:2601.18626v3 Announce Type: replace-cross Abstract: Natural gradients have long been studied in deep reinforcement learning due to their fast convergence properties and covariant weight updates. However, computing natural gradients requires inversion of the Fisher Information Matrix (FIM) at each iteration, which is computationally prohibitive in nature. In this paper, we present an efficient and scalable natural policy optimization technique that leverages a rank-1 approximation to full inverse-FIM. We theoretically show that under certain conditions, a rank-1 approximation to […]

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Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, planning, and structured prediction. In many practical settings, the graphical model remains fixed while inference must be performed repeatedly for varying evidence patterns. Stochastic Local Search (SLS) algorithms scale to large models but rely on myopic best-improvement rule that prioritizes immediate likelihood gains and often stagnate in poor local optima. Heuristics such as Guided Local […]

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arXiv:2601.22174v1 Announce Type: new Abstract: In this paper, we investigate Durrmeyer-type generalizations of maximum-minimum neural network operators. The primary objective of this study is to establish the convergence of these operators in the $L^{p}$ norm for functions $fin L^{p}([a,b],[0,1])$ with $1leq p<infty$. To this end, we analyze the properties of sigmoidal functions and maximum-minimum operations, subsequently establishing the convergence of the proposed operator in pointwise, supremum, and $L^{p}$ norms. Furthermore, we derive quantitative estimates for the rates of […]

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We demonstrate that the fine structure constant alpha−1 ≈ 137.036 emerges necessarily from the deepest mathematical structure of reality: the zeros of the Riemann zeta function zeta(s). We present an exact formula connecting alpha−1 to the first four nontrivial zeros gamma_1, gamma_2, gamma_3, gamma_4 of zeta(1/2 + it). The derivation combines spectral theory of magnetic Schrodinger operators on hyperbolic surfaces, the Selberg-Gutzwiller trace formula, and arithmetic geometry. The resulting value matches the experimental CODATA 2018 value with precision […]

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arXiv:2601.00215v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a promising approach for eliciting reasoning chains before generating final answers. However, multimodal large language models (MLLMs) generate reasoning that lacks integration of visual information. This limits their ability to solve problems that demand accurate visual perception, such as visual puzzles. We show that visual perception is the key bottleneck in such tasks: converting images into textual descriptions significantly improves performance, yielding gains of 26.7% for Claude […]

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Hey folks, My team and I are diving into RL training setups and want to chat with folks who have hands-on experience. Could share your process for picking an environment (e.g., Gym, custom sims) and getting it up and running? What pain points have you hit—like scaling, reward shaping, or integration issues—and what fixes made life easier? DMs open or reply below—happy to hop on a quick call! Thanks! submitted by /u/Popular_Piglet_1443 [link] [comments]

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