arXiv:2601.02410v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into software engineering education has driven the emergence of “Vibe Coding,” a paradigm where developers articulate high-level intent through natural language and delegate implementation to AI agents. While proponents argue this approach modernizes pedagogy by emphasizing conceptual design over syntactic memorization, accumulating empirical evidence raises concerns regarding skill retention and deep conceptual understanding. This paper proposes a theoretical framework to investigate the research question: textit{Is Vibe […]

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Sorry, I know these requests can be annoying, but I’m a medical physicist and no one I know uses arXiv. The preprint: post-deployment sensitivity analysis of a MONAI RetinaNet lung nodule detector using physics-guided acquisition parameter perturbation (LIDC-IDRI dataset, LUNA16 weights). Key finding: 5mm slice thickness causes a 42% relative sensitivity drop vs baseline; dose reduction at 25-50% produces only ~4pp loss. Threshold sensitivity analysis confirms the result holds across confidence thresholds from 0.1–0.9. Looking for an endorser […]

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arXiv:2602.16814v1 Announce Type: new Abstract: The expansion of AI toward the edge increasingly exposes the cost and fragility of cen- tralised intelligence. Data transmission, latency, energy consumption, and dependence on large data centres create bottlenecks that scale poorly across heterogeneous, mobile, and resource-constrained environments. In this paper, we introduce Node Learning, a decen- tralised learning paradigm in which intelligence resides at individual edge nodes and expands through selective peer interaction. Nodes learn continuously from local data, maintain their […]

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arXiv:2601.14286v1 Announce Type: new Abstract: Traditional technology mapping suffers from systemic inaccuracies in delay estimation due to its reliance on abstract, technology-agnostic delay models that fail to capture the nuanced timing behavior behavior of real post-mapping circuits. To address this fundamental limitation, we introduce GPA(graph neural network (GNN)-based Path-Aware multi-view circuit learning), a novel GNN framework that learns precise, data-driven delay predictions by synergistically fusing three complementary views of circuit structure: And-Inverter Graphs (AIGs)-based functional encoding, post-mapping technology […]

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Dmitri Mendeleev is best known for creating the first periodic table of chemical elements. He also discovered an interesting mathematical theorem. Empirical research led him to a question about interpolation, which in turn led him to a theorem about polynomials and their derivatives. I ran across Mendeleev’s theorem via a paper by Boas [1]. The opening paragraph describes what Mendeleev was working on. Some years after the chemist Mendeleev invented the periodic table of the elements he made […]

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arXiv:2602.17667v1 Announce Type: new Abstract: In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, traditional methods utilizing implicit history features often suffer from signal dilution and delayed feedback. To address these challenges, we propose WeWrite, a novel Personalized Demand-aware Query Rewriting framework. Specifically, WeWrite tackles three key challenges: (1) When to Write: An automated posterior-based mining strategy extracts high-quality samples from user logs, identifying scenarios where personalization is strictly […]

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arXiv:2601.04236v1 Announce Type: new Abstract: Co-speech gesture generation is a critical area of research aimed at synthesizing speech-synchronized human-like gestures. Existing methods often suffer from issues such as rhythmic inconsistency, motion jitter, foot sliding and limited multi-sampling diversity. In this paper, we present SmoothSync, a novel framework that leverages quantized audio tokens in a novel dual-stream Diffusion Transformer (DiT) architecture to synthesis holistic gestures and enhance sampling variation. Specifically, we (1) fuse audio-motion features via complementary transformer streams […]

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Recent deployments of large language models (LLMs) as autonomous trading agents raise questions about whether financial decision-making competence generalizes beyond specific market patterns and how it should be trained and evaluated in noisy markets lacking ground truth. We propose a structured framework for training and evaluating such models. Central to our approach is a curated, multiple-choice question (MCQ) dataset derived from classic textbooks and historical markets, verified by an AI committee, enriched with structured reasoning traces, and augmented […]

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If you’ve ever stared at a broken attribution report at 2am wondering which step in your pipeline silently failed, this post is for you. Modern performance advertising runs on a stack of non-deterministic AI systems – real-time bidding models, CTR predictors, multi-touch attribution, all chained together and funneling data into massive warehouses like Snowflake or Redshift. When something goes wrong (and it will), traditional monitoring gives you almost nothing useful. You get a metric that’s off, but no […]

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arXiv:2601.03382v1 Announce Type: new Abstract: The advancements in the field of AI is increasingly giving rise to various threats. One of the most prominent of them is the synthesis and misuse of Deepfakes. To sustain trust in this digital age, detection and tagging of deepfakes is very necessary. In this paper, a novel architecture for Deepfake detection in images and videos is presented. The architecture uses cross attention between spatial and frequency domain features along with a blood […]

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