In this tutorial, we explore TileGym GPU programming by building a practical Colab workflow that runs across different hardware conditions. We begin by probing the available CUDA environment, checking whether NVIDIA cuTile runs directly, and falling back to Triton when standard Colab GPUs lack the required cuTile stack. Through this setup, we learn the core tile-programming idea: instead of writing code for one thread at a time, we operate on entire data tiles, load them into the kernel, […]

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arXiv:2603.24844v1 Announce Type: new Abstract: Given a question, a language model (LM) implicitly encodes a distribution over possible answers. In practice, post-training procedures for LMs often collapse this distribution onto a single dominant mode. While this is generally not a problem for benchmark-style evaluations that assume one correct answer, many real-world tasks inherently involve multiple valid answers or irreducible uncertainty. Examples include medical diagnosis, ambiguous question answering, and settings with incomplete information. In these cases, we would like […]

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arXiv:2512.23671v2 Announce Type: replace Abstract: We develop an online method that guarantees calibration of quantile forecasts at multiple quantile levels simultaneously. In this work, a sequence of quantile forecasts is said to be calibrated provided that its $alpha$-level predictions are greater than or equal to the target value at an $alpha$ fraction of time steps, for each level $alpha$. Our procedure, called the multi-level quantile tracker (MultiQT), is lightweight and wraps around any point or quantile forecaster to […]

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Been working on a sim to train multiple robots some task. I tried version 5.1 but it has some very annoying flickering, version 4.5 is the most stable for now, but the installation (pip based) gets corrupted with torch dll errors, whenever I try something depending on the python API. I am sure a reinstall will fix it, but atp it’s like carrying a dynamite in a pocket. I have already reinstalled it 10 times. Is the ubuntu […]

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Embodied robotic systems increasingly rely on large language model (LLM)-based agents to support high-level reasoning, planning, and decision-making during interactions with the environment. However, invoking LLM reasoning introduces substantial computational latency and resource overhead, which can interrupt action execution and reduce system reliability. Excessive reasoning may delay actions, while insufficient reasoning often leads to incorrect decisions and task failures. This raises a fundamental question for embodied agents: when should the agent reason, and when should it act? In […]

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arXiv:2603.03325v1 Announce Type: new Abstract: Large language models (LLMs) have become integral to modern Human-AI collaboration workflows, where accurately understanding user intent serves as a crucial step for generating satisfactory responses. Context-aware intent understanding, which involves inferring user intentions from situational environments, is inherently challenging because it requires reasoning over both the immediate context and the user’s underlying motivations that drive their behavior. Moreover, existing approaches often treat intent understanding as a static recognition task, overlooking users’ accumulated […]

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Let’s be real, most AI projects die at the prototype stage. Great demo, cool tech, zero production-readiness. What separates those graveyard prototypes from systems that actually run the business? We’d argue it’s people like Andrei Shcherbinin, Team Lead at Social Discovery Group, who’s quietly been building ML infrastructure that does serious heavy lifting. We grabbed some time with him to talk about engineering roots, 12x speed improvements, chatbots that handle 95% of support on their own, and why he thinks […]

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arXiv:2601.08839v1 Announce Type: new Abstract: This paper presents a tri-agent cross-validation framework for analyzing stability and explainability in multi-model large language systems. The architecture integrates three heterogeneous LLMs-used for semantic generation, analytical consistency checking, and transparency auditing-into a recursive interaction cycle. This design induces Recursive Knowledge Synthesis (RKS), where intermediate representations are continuously refined through mutually constraining transformations irreducible to single-model behavior. Across 47 controlled trials using public-access LLM deployments (October 2025), we evaluated system stability via four […]

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