February 2026

arXiv:2602.22359v1 Announce Type: new Abstract: This paper tests whether large language models (LLMs) can support interpretative citation context analysis (CCA) by scaling in thick, text-grounded readings of a single hard case rather than scaling up typological labels. It foregrounds prompt-sensitivity analysis as a methodological issue by varying prompt scaffolding and framing in a balanced 2×3 design. Using footnote 6 in Chubin and Moitra (1975) and Gilbert’s (1977) reconstruction as a probe, I implement a two-stage GPT-5 pipeline: a […]

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arXiv:2602.22352v1 Announce Type: new Abstract: With the continuous growth of neural network scales, low-precision quantization is widely used in edge accelerators. Classic multi-threshold activation hardware requires 2^n thresholds for n-bit outputs, causing a rapid increase in hardware cost as precision increases. We propose a reconfigurable activation hardware, GRAU, based on piecewise linear fitting, where the segment slopes are approximated by powers of two. Our design requires only basic comparators and 1-bit right shifters, supporting mixed-precision quantization and nonlinear […]

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arXiv:2602.22351v1 Announce Type: new Abstract: Large language models (LLMs) learn contextual embeddings that capture rich semantic information, yet they often overlook structured lexical knowledge such as word senses and relationships. Prior work has shown that incorporating sense dictionaries can improve knowledge distillation for encoder models, but their application to decoder as generative models remains challenging. In this paper, we introduce Decoder-based Sense Knowledge Distillation (DSKD), a framework that integrates lexical resources into the training of decoder-style LLMs without […]

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arXiv:2602.22350v1 Announce Type: new Abstract: We introduce the concept of engineered simultaneity: a system design that (1) requires comparing events at spacelike-separated locations, (2) implements this comparison via an implicit simultaneity convention, and (3) represents the result as objective rather than conventional. The United States National Best Bid and Offer (NBBO), mandated by SEC Regulation NMS Rule 611, is shown to be an instance of engineered simultaneity. We prove that the NBBO is frame-dependent: its value depends on […]

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arXiv:2602.22347v1 Announce Type: new Abstract: Foundation models in histopathology are expected to facilitate the development of high-performing and generalisable deep learning systems. However, current models capture not only biologically relevant features, but also pre-analytic and scanner-specific variation that bias the predictions of task-specific models trained from the foundation model features. Here we show that introducing novel robustness losses during training of downstream task-specific models reduces sensitivity to technical variability. A purpose-designed comprehensive experimentation setup with 27,042 WSIs from […]

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arXiv:2602.22346v1 Announce Type: new Abstract: Autonomous mobile service robots, like lawnmowers or cleaning robots, operating in human-populated environments need to reason about local human-human interactions to support safe and socially aware navigation while fulfilling their tasks. For such robots, interaction understanding is not primarily a fine-grained recognition problem, but a perception problem under limited sensing quality and computational resources. Many existing approaches focus on holistic group activity recognition, which often requires complex and large models which may not […]

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arXiv:2602.22345v1 Announce Type: new Abstract: This thesis addresses two persistent and closely related challenges in modern deep learning, reliability and efficiency, through a unified framework grounded in Spectral Geometry and Random Matrix Theory (RMT). As deep networks and large language models continue to scale, their internal behavior becomes increasingly opaque, leading to hallucinations, fragile generalization under distribution shift, and growing computational and energy demands. By analyzing the eigenvalue dynamics of hidden activations across layers and inputs, this work […]

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arXiv:2602.22343v1 Announce Type: new Abstract: As interfaces evolve from static user pathways to dynamic human-AI collaboration, no standard methods exist for selecting appropriate interface patterns based on user needs and task complexity. Existing frameworks only provide guiding principles for designing AI agent capabilities. We propose a dimensional framework based on workflow complexity, AI autonomy, and AI reasoning to guide the design of context-aware, scalable AI interfaces aka modalities (e.g., prompt bars, split screens, full screens, etc.). The framework […]

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arXiv:2602.22340v1 Announce Type: new Abstract: Non-Display Smart Glasses hold the potential to support everyday activities by combining continuous environmental sensing with voice-only interaction powered by large language models (LLMs). Understanding how conversational successes and breakdowns arise in everyday contexts can better inform the design of future voice-only interfaces. To investigate this, we conducted a month-long collaborative autoethnography (n=2) to identify patterns of successes and breakdowns when using such devices. We then compare these patterns with prior findings on […]

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arXiv:2602.22339v1 Announce Type: new Abstract: This paper examines the analysis of package power consumption using Intel’s telemetry data. It challenges the prevailing belief that hardware choice is the primary determinant of a device’s power consumption and instead emphasizes the significant role of user behavior. The paper includes two sections: Exploratory Data Analysis (EDA) and a linear model for power consumption. The EDA section provides valuable insights from Intel’s telemetry data, comparing power consumption across countries, with a specific […]

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