arXiv:2603.09310v1 Announce Type: cross Abstract: We study training algorithms with data following a Gaussian mixture model. For a specific family of such algorithms, we present a non-asymptotic result, connecting the evolution of the model to a surrogate dynamical system, which can be easier to analyze. The proof of our result is based on the celebrated Gordon comparison theorem. Using our theorem, we rigorously prove the validity of the dynamic mean-field (DMF) expressions in the asymptotic scenarios. Moreover, we […]

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The Job Shop Scheduling Problem (JSSP) is commonly formulated as a disjunctive graph in which nodes represent operations and edges encode technological precedence constraints as well as machine-sharing conflicts. Most existing reinforcement learning approaches model this graph as homogeneous, merging job-precedence and machine-contention edges into a single relation type. Such a simplification overlooks the intrinsic heterogeneity of the problem structure and may lead to the loss of critical relational information. To address this limitation, we propose the Heterogeneous […]

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Current Large Language Models (LLMs) exhibit a critical modal disconnect: they possess vast semantic knowledge but lack the procedural grounding to respect the immutable laws of the physical world. Consequently, while these agents implicitly function as world models, their simulations often suffer from physical hallucinations-generating plans that are logically sound but physically unexecutable. Existing alignment strategies predominantly rely on resource-intensive training or fine-tuning, which attempt to compress dynamic environmental rules into static model parameters. However, such parametric encapsulation […]

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arXiv:2506.15643v3 Announce Type: replace Abstract: Feature subsampling is a core component of random forests and other ensemble methods. While recent theory suggests that this randomization acts solely as a variance reduction mechanism analogous to ridge regularization, these results largely rely on base learners optimized via ordinary least squares. We investigate the effects of feature subsampling on greedy forward selection, a model that better captures the adaptive nature of decision trees. Assuming an orthogonal design, we prove that ensembling […]

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We address real-time sampling and estimation of autoregressive Markovian sources in dynamic yet structurally similar multi-hop wireless networks. Each node caches samples from others and communicates over wireless collision channels, aiming to minimize time-average estimation error via decentralized policies. Due to the high dimensionality of action spaces and complexity of network topologies, deriving optimal policies analytically is intractable. To address this, we propose a graphical multi-agent reinforcement learning framework for policy optimization. Theoretically, we demonstrate that our proposed […]

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Mamba-2 restructured its recurrence from parallel scans (10-20% Tensor Core utilization) to block-diagonal GEMMs (60-70%). The architecture bent to fit the silicon. RetNet was published by Microsoft Research in July 2023 with promising results at 6.7B. Five months later, the same organization shipped Phi-2, a dense Transformer. Then Phi-3. Then Phi-4. The co-authors didn’t bet on their own architecture. I wrote an analysis of why this pattern keeps repeating. The short version: Transformers and NVIDIA GPUs co-evolved into […]

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arXiv:2603.08741v1 Announce Type: new Abstract: The IEEE 754 floating-point standard is the bedrock of modern computing, but its structural requirements — a hidden leading bit, Base-2 bit-level normalization, and Sign-Magnitude encoding — impose significant silicon area and power overhead in massively parallel Neural Processing Units (NPUs). Furthermore, the industry’s recent shift to 8-bit formats (e.g., FP8 E4M3, OCP MX formats) has introduced a new hardware penalty: the strict necessity of Block-Scaling (AMAX) logic to prevent out-of-bound Large Language […]

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Falls pose severe risks to vulnerable populations, particularly the elderly and individuals with adverse neurological conditions, necessitating reliable and non-obstructive detection systems. While previous multimodal approaches utilising video and audio have demonstrated strong performance, they face significant limitations regarding sensitivity to environmental noise. This paper presents a robust, video-only fall detection framework that eliminates reliance on acoustic data to enhance universality. We conduct a comprehensive comparative analysis of five Optical Flow (OF) algorithms—Horn-Schunck, Lucas-Kanade (LK), LK-Derivative of Gaussian, […]

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Here is a PyTorch port of the Disco103 update rule: https://github.com/asystemoffields/disco-torch pip install disco-torch The port loads the pretrained disco_103.npz weights and reproduces the reference Catch benchmark (99% catch rate at 1000 steps). All meta-network outputs match the JAX implementation within float32 precision (<1e-6 max diff), and the full value pipeline is verified (14 fields, <6e-4 max diff). It includes a high-level DiscoTrainer API that handles meta-state management, target networks, replay buffer, and the training loop: from disco_torch […]

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