February 2026

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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arXiv:2602.22303v1 Announce Type: new Abstract: Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by reinforcing the correct goals, but alignment may not always succeed and can lead to unwanted side effects. We propose self-incrimination training, which instead trains agents to produce a visible signal when they covertly misbehave. We train GPT-4.1 and Gemini-2.0 agents to call a report_scheming() tool when behaving deceptively and measure their ability […]

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arXiv:2602.22302v1 Announce Type: new Abstract: Traditional software relies on contracts — APIs, type systems, assertions — to specify and enforce correct behavior. AI agents, by contrast, operate on prompts and natural language instructions with no formal behavioral specification. This gap is the root cause of drift, governance failures, and frequent project failures in agentic AI deployments. We introduce Agent Behavioral Contracts (ABC), a formal framework that brings Design-by-Contract principles to autonomous AI agents. An ABC contract C = […]

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arXiv:2602.22300v1 Announce Type: new Abstract: We study the algorithmic task of testably learning general Massart halfspaces under the Gaussian distribution. In the testable learning setting, the aim is the design of a tester-learner pair satisfying the following properties: (1) if the tester accepts, the learner outputs a hypothesis and a certificate that it achieves near-optimal error, and (2) it is highly unlikely that the tester rejects if the data satisfies the underlying assumptions. Our main result is the […]

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arXiv:2602.22299v1 Announce Type: new Abstract: Video-based ads are a vital medium for brands to engage consumers, with social media platforms leveraging user data to optimize ad delivery and boost engagement. A crucial but under-explored aspect is the ‘hooking period’, the first three seconds that capture viewer attention and influence engagement metrics. Analyzing this brief window is challenging due to the multimodal nature of video content, which blends visual, auditory, and textual elements. Traditional methods often miss the nuanced […]

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