It’s 11:45 PM on a Thursday. Professor Miller has a stack of 12 grant proposals on her desk. She has a lecture at 8:00 AM, her lab’s centrifuge is broken, and she has exactly zero patience left. She picks up your proposal—the one you spent three months agonizing over. She reads the first paragraph. She scans the Specific Aims. She sighs, rubs her temples, and puts it in the “Maybe” pile (which we all know is the soft […]

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arXiv:2602.11292v1 Announce Type: new Abstract: We prove a complete complexity classification theorem for the planar eight-vertex model. For every parameter setting in ${mathbb C}$ for the eight-vertex model, the partition function is either (1) computable in P-time for every graph, or (2) #P-hard for general graphs but computable in P-time for planar graphs, or (3) #P-hard even for planar graphs. The classification has an explicit criterion. In (2), we discover new P-time computable eight-vertex models on planar graphs […]

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Recursive (looped) Transformers decouple computational depth from parameter depth by repeatedly applying shared layers, providing an explicit architectural primitive for iterative refinement and latent reasoning. However, early looped Transformers often underperform non-recursive baselines of equal compute. While recent literature has introduced more effective recursion mechanisms to mitigate this gap, existing architectures still operate at a fixed, full-token resolution, neglecting the potential efficiency of computing over compressed latent representations. In this paper, we propose SpiralFormer, a looped Transformer that […]

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arXiv:2507.21429v2 Announce Type: replace Abstract: Gradient descent (GD) on deep neural network loss landscapes is non-convex, yet often converges far faster in practice than classical guarantees suggest. Prior work shows that within locally quasi-convex regions (LQCRs), GD converges to stationary points at sublinear rates, leaving the commonly observed near-exponential training dynamics unexplained. We show that, under a mild local Neural Tangent Kernel (NTK) stability assumption, the loss satisfies a PL-type error bound within these regions, yielding a Locally […]

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arXiv:2602.13485v1 Announce Type: cross Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal patterns at one subsystem relate to others. This is challenging in decentralized settings where raw measurements cannot be shared and client observations are heterogeneous. In practical deployments each subsystem (client) operates a fixed proprietary model that cannot be […]

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arXiv:2601.22289v1 Announce Type: new Abstract: We focus on multi-object rearrangement planning in densely cluttered environments using a car-like robot pusher. The combination of kinematic, geometric and physics constraints underlying this domain results in challenging nonmonotone problem instances which demand breaking each manipulation action into multiple parts to achieve a desired object rearrangement. Prior work tackles such instances by planning prerelocations, temporary object displacements that enable constraint satisfaction, but deciding where to prerelocate remains difficult due to local minima […]

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Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Networks (GNNs) to generalize to unseen domains or label spaces. While recent advancements have transitioned toward leveraging Large Language Models (LLMs) as predictors to enhance GNNs, these methods often suffer from cross-modal alignment issues. A recent paradigm (i.e., Graph-R1) overcomes the aforementioned architectural dependencies by adopting a purely text-based format and utilizing LLM-based graph reasoning, showing improved […]

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Identity and Access Management (IAM) increasingly relies on adaptive controls—step-up challenges, recovery verification, device and behavior signals, and continuous authorization—to reduce account takeover and misuse. At the same time, IAM systems must prepare for post-quantum cryptography (PQC) transitions that affect credentials, signing, and verification paths. These shifts create a practical governance problem: when an identity action is allowed, challenged, denied, or escalated (e.g., passwordless enrollment, recovery credential release, privileged step-up, or machine key rotation), teams must be able […]

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I have got 3x4090s and I was wondering what is the best open source model that I can run keeping in mind different quantizations that are available and different attention mechanisms that will affect the amount of memory needed for the context line itself. So combining all of these things, what is the best open source model that I can run on this hardware with a context length of say 128k. submitted by /u/Raise_Fickle [link] [comments]

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In my previous analysis of Power Stranding, I explored the multi-billion-dollar art of ensuring that massive power infrastructure doesn’t sit idle. But for a data center financial analyst, solving the power equation isn’t just about building better substations. It’s about what sits inside the racks. Looking at a modern data center’s balance sheet in early 2026, the “Hardware” line item is undergoing a silent coup. For decades, the industry operated as a one-stop shop: cloud providers bought their […]

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