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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I. The “Complete System” Fallacy 1.1 The Problem The pursuit of ethical Artificial Intelligence (AI) has been defined by a single, implicit ambition: the creation of a “complete” ethical machine. This is the dream of building an autonomous system whose internal logic, training data, and reward functions are comprehensive enough to resolve any moral dilemma it encounters without human intervention. From the rigid rule-sets of symbolic AI to the “aligned” reinforcement learning models of today, the governing philosophy […]

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arXiv:2604.08556v1 Announce Type: new Abstract: What exactly do efficient sequence models gain over simple temporal averaging? We use exponential moving average (EMA) traces, the simplest recurrent context (no gating, no content-based retrieval), as a controlled probe to map the boundary between what fixed-coefficient accumulation can and cannot represent. EMA traces encode temporal structure: a Hebbian architecture with multi-timescale traces achieves 96% of a supervised BiGRU on grammatical role assignment with zero labels, surpassing the supervised model on structure-dependent […]

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Prediction markets reported their strongest week, with Kalshi hitting a notional volume of $3.54 billion while Polymarket recorded $2.48 billion; the industry’s combined notional volume crossed $6.5 billion, setting a new overall high, according to DeFi Rate. Image source: DeFi Rate This surge coincides with a positive outlook on the industry by Bank of America Analysts who expect sports-related events contracts to hit $1.1 trillion per year. Bernstein also estimates that total market volumes will reach $240 billion […]

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arXiv:2601.18901v1 Announce Type: new Abstract: Knowledge probing quantifies how much relational knowledge a language model (LM) has acquired during pre-training. Existing knowledge probes evaluate model capabilities through metrics like prediction accuracy and precision. Such evaluations fail to account for the model’s reliability, reflected in the calibration of its confidence scores. In this paper, we propose a novel calibration probing framework for relational knowledge, covering three modalities of model confidence: (1) intrinsic confidence, (2) structural consistency and (3) semantic […]

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Machine unlearning is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove harmful or corrupted training samples. Despite this, most existing methods tackle the problem purely from a post-hoc perspective. They attempt to erase the influence of targeted training samples through parameter updates that typically require access to the full training data. This creates a mismatch with real deployment scenarios where unlearning requests can be anticipated, revealing a fundamental limitation of […]

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Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previous knowledge. Existing methods typically treat parameters uniformly, failing to distinguish between specific task knowledge and shared capabilities. We introduce Mixture of Sparse Experts for Task-Agnostic Continual Learning, referred to as SETA, a framework that resolves the plasticity-stability conflict by decomposing the model into modular subspaces. Unlike standard updates, where tasks compete for the same […]

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