If you think “tiering” started with SSD caches and cloud storage classes… congratulations: you’ve been successfully marketed to. Hierarchical Storage Management (HSM) has been doing the same core job for decades: Keep “hot” data on fast, expensive media… push “cold” data to slower, cheaper media… and make the user mostly forget you exist—until recall latency reminds them you do. What changed over 50-ish years isn’t the idea. It’s the media, the interfaces, the scale, and the **ways vendors […]

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The New Reality: AI Can Write Code, But Should It Ship Without You? Photo by Daniil Komov on Unsplash There is a lot of talk lately about AI “killing” the software developer role. Headlines claim that junior jobs are vanishing and that manual coding is a dead art. But as someone who has been deeply immersed in the latest AI advancements, my experience lately with AI has been that it isn’t a replacement for the engineer, it’s a powerful, albeit […]

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We develop a new form of reweighting (Wainwright et al., 2005b) to leverage the relationship between Ising spin glasses and perfect matchings into a novel technique for the exact computation of MAP states in hitherto intractable binary Markov random fields. Our method solves an $n times n$ lattice with external field and random couplings much faster, and for larger $n$, than the best competing algorithms. It empirically scales as $O(n^3)$ even though this problem is NP-hard and non-approximable […]

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A practical look at autonomy, system design, and what changes when software starts making decisions. Agentic AI is increasingly ubiquitous in technology discourse. While most discussions focus on models, frameworks, or orchestration patterns, that framing misses the more consequential shift. Agentic AI does not change how systems are built; it changes how decisions are made inside those systems. When software is allowed to plan, act, and adapt autonomously, the hardest problems move upstream. The core challenge is no […]

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Curriculum learning changes the order of pre-training data, but it remains unclear whether it changes the learning trajectory or mainly reorders exposure over a fixed trajectory. We train Pythia models (14M-410M parameters) for 300B tokens under three linguistically motivated curricula-Age-of-Acquisition, word frequency, and Verb Variation (VV)-and compare each against Random ordering; at 1B parameters we compare Random and VV. Across orderings, training follows a shared sequence of latent phases, while curricula mainly change within-phase data exposure. In smaller […]

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arXiv:2601.10801v1 Announce Type: new Abstract: We present a systematic study of Tensor Network (TN) models $unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $unicode{x2013}$ for real-time jet tagging in high-energy physics, with a focus on low-latency deployment on Field Programmable Gate Arrays (FPGAs). Motivated by the strict requirements of the HL-LHC Level-1 trigger system, we explore TNs as compact and interpretable alternatives to deep neural networks. Using low-level jet constituent features, our models achieve competitive performance […]

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Documentation culture is about behavioral traits that technical writers practice every day based on set of beliefs and organizational values. Introduction A documentation culture reflects the behavioral traits practiced by everyone guided by shared beliefs and organizational values where writing is treated as a core part of their job role. Organization with strong documentation culture embeds writing into their cultural DNA whereby knowledge is captured, documented and more importantly leveraged. The defining characteristic of such a culture is […]

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Hi All, First time submitting papers. When I was writing my paper, I only paid attention to the 9-page total limit, but after submitting, I realized it was actually 7 for the contents, 2 for the references. My paper has 9 pages in total, but 7 and 1/3 for contents. It’s already passed the submission deadlines, will I get desk rejected? What should I do? submitted by /u/d_edge_sword [link] [comments]

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arXiv:2601.07994v1 Announce Type: new Abstract: Large Language Models (LLMs) often exhibit increased response latency and degraded answer quality as dialogue length grows, making effective context management essential. However, existing methods rely on extra LLM calls to build memory or perform offline memory construction without considering the current user utterance, which can introduce inefficiencies or disrupt conversational continuity. We introduce DyCP, a lightweight context management method that dynamically segment and retrieve relevant memory at query time. It preserves the […]

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