We tested prompt repetition on engineering tasks with Claude Haiku 4.5 agents. Blind scored, pre-registeredrubrics. Both groups scored 100%. Nothing to improve. The surprise: in our experiments, treatment agents finished in fewer turns and used 13% fewer output tokens. submitted by /u/antidrugue [link] [comments]
In medieval philosophy, thinkers debated whether intelligence came from divine reason, innate forms, or logical structures built into the mind. Centuries later, early AI researchers tried to recreate intelligence through symbols and formal logic. Now, large models that are trained on simple prediction, just optimizing loss at scale, can reason, write code, and solve complex problems. Does this suggest intelligence was never about explicit rules or divine structure, but about compressing patterns in experience? If intelligence can emerge […]
arXiv:2602.22367v1 Announce Type: new Abstract: Modern forward electrocardiogram (ECG) computational models rely on an accurate representation of the torso domain. The lead-field method enables fast ECG simulations while preserving full geometric fidelity. Achieving high anatomical accuracy in torso representation is, however, challenging in clinical practice, as imaging protocols are typically focused on the heart and often do not include the entire torso. In addition, the computational cost of the lead-field method scales linearly with the number of electrodes, […]
Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 3. Preliminaries and Notations Herein, different from the typical setting of existing […]
Detecting LLM training data is generally framed as a membership inference attack (MIA) problem. However, conventional MIAs operate passively on fixed model weights, using log-likelihoods or text generations. In this work, we introduce textbf{Active Data Reconstruction Attack} (ADRA), a family of MIA that actively induces a model to reconstruct a given text through training. We hypothesize that training data are textit{more reconstructible} than non-members, and the difference in their reconstructibility can be exploited for membership inference. Motivated by […]
Glucose Goddess Jessie Inchauspé exposes the pregnancy diet rewriting your baby’s DNA, how sugar in pregnancy spikes insulin, and the simple protein and supplement fixes most mothers miss! Jessie Inchauspé is a world-renowned biochemist and founder of the Glucose Goddess movement. She is the international bestselling author of books such as ‘The Glucose Goddess Method, and her latest book is ‘9 Months That Count Forever’. She explains: ◼️How glucose spikes in the womb program a baby’s future diabetes […]
arXiv:2602.07215v1 Announce Type: new Abstract: Generative AI (GenAI) has transformed applications in natural language processing and content creation, yet centralized inference remains hindered by high latency, limited customizability, and privacy concerns. Deploying large models (LMs) in mobile edge networks emerges as a promising solution. However, it also poses new challenges, including heterogeneous multi-modal LMs with diverse resource demands and inference speeds, varied prompt/output modalities that complicate orchestration, and resource-limited infrastructure ill-suited for concurrent LM execution. In response, we […]
arXiv:2602.20336v1 Announce Type: new Abstract: This article presents an evaluation of several machine learning methods applied to automated text classification, alongside the design of a demonstrative system for unbalanced document categorization and distribution. The study focuses on balancing classification accuracy with computational efficiency, a key consideration when integrating AI into real world automation pipelines. Three models of varying complexity were examined: a Naive Bayes classifier, a bidirectional LSTM network, and a fine tuned transformer based BERT model. The […]
arXiv:2602.00880v1 Announce Type: new Abstract: Difficulty spillover and suboptimal help-seeking challenge the sequential, knowledge-intensive nature of digital tasks. In online surveys, tough questions can drain mental energy and hurt performance on later questions, while users often fail to recognize when they need assistance or may satisfy, lacking motivation to seek help. We developed a proactive, adaptive system using electrodermal activity and mouse movement to predict when respondents need support. Personalized classifiers with a rule-based threshold adaptation trigger timely […]
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