technocracy

arXiv:2603.12461v1 Announce Type: new Abstract: 3D DRAM has emerged as a promising approach for continued density scaling, but its viability is limited by routing and hybrid bonding constraints to periphery, which may degrade sensing margin, latency, and array efficiency. With device characteristics and array parasitics extracted from TCAD, SPICE simulations are performed with peri logic in a CMOS-Bonded-Array (CBA). The analysis shows that the bitline strap architecture with amorphous oxide semiconductor (AOS) selectors is essential to manage routing […]

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Data leakage affected 294 published papers across 17 scientific fields (Kapoor & Narayanan, 2023). The dominant response has been documentation: checklists, linters, best-practice guides. Documentation does not prevent these failures. This paper proposes a structural remedy: a grammar that decomposes the supervised learning lifecycle into 7 kernel primitives connected by a typed directed acyclic graph (DAG), with four hard constraints that reject the two most damaging leakage classes at call time. The grammar’s core contribution is the terminal […]

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In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint. The energy demands of generative AI are expected to continue increasing dramatically over the next decade. For instance, an April 2025 report from the International Energy Agency predicts that the global electricity demand from data centers, which house the computing infrastructure to train and deploy AI models, will more than double by […]

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arXiv:2603.22325v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) and self-attention are both widely used sequence-mixing layers that maintain an internal memory. However, this memory is constructed using two orthogonal mechanisms: RNNs compress the entire past into a fixed-size state, whereas self-attention’s state stores every past time step growing its state (the KV cache) linearly with the sequence length. This results in orthogonal strengths and weaknesses. Self-attention layers excel at retrieving information in the context but have large […]

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Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on time graduation. In educational settings, AI powered systems have become essential for predicting student performance due to their advanced analytical capabilities. However, effectively leveraging diverse student data to uncover latent and complex patterns remains a key challenge. While prior studies have explored this area, the potential of dynamic data features and multi category entities has been largely overlooked. To address this […]

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I’m working on a project where I’ll be fine-tuning LLMs with GRPO on a 170K-sample dataset for explainable LJP (legal judgment prediction, where the model predicts case outcomes and generates step-by-step reasoning citing the facts). I’m considering models like GPT OSS 20B or Qwen 3.5 27B, with a slight preference for Qwen 3.5 27B because of its strong reasoning capabilities. I recently obtained a 96GB VRAM workstation (RTX PRO 6000) to handle the RL rollouts, which should give […]

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Reinforcement learning drives recent advances in LLM reasoning and agentic capabilities, yet current approaches struggle with both exploration and exploitation. Exploration suffers from low success rates on difficult tasks and high costs of repeated rollouts from scratch. Exploitation suffers from coarse credit assignment and training instability: Trajectory-level rewards penalize valid prefixes for later errors, and failure-dominated groups overwhelm the few positive signals, leaving optimization without constructive direction. To this end, we propose R$^3$L, Reflect-then-Retry Reinforcement Learning with Language-Guided […]

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Machine learning has opened new frontiers in purely data-driven algorithms for data assimilation in, and for forecasting of, dynamical systems; the resulting methods are showing some promise. However, in contrast to model-driven algorithms, analysis of these data-driven methods is poorly developed. In this paper we address this issue, developing a theory to underpin data-driven methods to solve smoothing problems arising in data assimilation and forecasting problems. The theoretical framework relies on two key components: (i) establishing the existence […]

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