arXiv:2603.28796v1 Announce Type: new Abstract: Boolean satisfiability (SAT) problem, the first problem proven to be NP-complete, has become a fundamental challenge in computational complexity, with widespread applications in optimization and verification across many domains. Despite significant algorithmic advances over the past two decades, the performance of SAT solvers has improved at a limited pace. Notably, the 2025 competition winner shows only about a 2X improvement over the 2006 winner in SAT Competition performance after nearly 20 years of […]

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arXiv:2601.08928v1 Announce Type: new Abstract: Supply chain forecasting models degrade over time as real-world conditions change. Promotions shift, consumer preferences evolve, and supply disruptions alter demand patterns, causing what is known as concept drift. This silent degradation leads to stockouts or excess inventory without triggering any system warnings. Current industry practice relies on manual monitoring and scheduled retraining every 3-6 months, which wastes computational resources during stable periods while missing rapid drift events. Existing academic methods focus narrowly […]

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arXiv:2605.04344v1 Announce Type: new Abstract: We introduce a simple yet powerful framework for training large language models. In contrast to the standard autoregressive next-token prediction based on an exact prefix, we propose a perturbation-based procedure that first transforms the prefix into a semantic neighbor and then conditions on this perturbed variant for next-token prediction. This yields a hierarchical model with a pre-post-additive noise structure. Within this framework, we develop a rigorous theory of extrapolability, namely, the capacity of […]

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arXiv:2601.02530v1 Announce Type: new Abstract: We present Connection-Aware Motif Sequencing (CamS), a graph-to-sequence representation that enables decoder-only Transformers to learn molecular graphs via standard next-token prediction (NTP). For molecular property prediction, SMILES-based NTP scales well but lacks explicit topology, whereas graph-native masked modeling captures connectivity but risks disrupting the pivotal chemical details (e.g., activity cliffs). CamS bridges this gap by serializing molecular graphs into structure-rich causal sequences. CamS first mines data-driven connection-aware motifs. It then serializes motifs via […]

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Hi everyone, I’m a 3rd-year undergraduate student currently working on a reinforcement learning project. My goal is to train a WLKATA Mirobot (6-DOF) in NVIDIA Isaac Lab for a “reach and stop” task and successfully transfer the policy to the real robot (Sim-to-Real). I am specifically focusing on overcoming the mechanical limitations (such as backlash and joint friction) of the Mirobot through Domain Randomization and System Identification. Before I dive deeper into designing the environment, I wanted to […]

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arXiv:2601.00142v1 Announce Type: new Abstract: This paper compares three methodological categories of neural reasoning: LLM reasoning, supervised learning-based reasoning, and explicit model-based reasoning. LLMs remain unreliable and struggle with simple decision-making that animals can master without extensive corpora training. Through disjunctive syllogistic reasoning testing, we show that reasoning via supervised learning is less appealing than reasoning via explicit model construction. Concretely, we show that an Euler Net trained to achieve 100.00% in classic syllogistic reasoning can be trained […]

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What happens when the person who onboarded 150 million people into crypto decides the next frontier is autonomous AI? That is the question the tech and AI industry now faces as Kris Marszalek, the co-founder and CEO of Crypto.com, formally announced the launch of ai.com as a consumer-facing autonomous AI agent platform, set to go live on February 8, 2026 with a Super Bowl LX commercial on NBC. This is not another chatbot. This is not another wrapper […]

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Let’s be honest: labeling data is the absolute worst part of computer vision. For years, the standard playbook was to scrape millions of images, pay an army of annotators to draw bounding boxes, and feed them into a supervised Convolutional Neural Network. It worked, but it didn’t scale. Then self-supervised learning (SSL) came along, promising a world where models could learn from raw, unlabeled images. But early contrastive methods like SimCLR and MoCo were notoriously heavy, requiring massive batch […]

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arXiv:2604.13217v1 Announce Type: new Abstract: Reliable evaluation of blastocyst quality is critical for the success of in vitro fertilization (IVF) treatments. Current embryo grading practices primarily rely on visual assessment of morphological features, which introduces subjectivity, inter-embryologist variability, and challenges in standardizing quality assurance. In this study, we propose a multitask embedding-based approach for the automated analysis and prediction of key blastocyst components, including the trophectoderm (TE), inner cell mass (ICM), and blastocyst expansion (EXP). The method leverages […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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