arXiv:2601.03268v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has shifted language model evaluation toward reasoning and problem-solving tasks as measures of general intelligence. Small Language Models (SLMs) — defined here as models under 10B parameters — typically score 3-4 times lower than LLMs on these metrics. However, we demonstrate that these evaluations fail to capture SLMs’ effectiveness in common industrial applications, such as tone modification tasks (e.g., funny, serious, professional). We propose an evaluation […]

Ver mais

A large number of children experience preventable developmental delays each year, yet the deployment of machine learning in new countries has been stymied by a data bottleneck: reliable models require thousands of samples, while new programs begin with fewer than 100. We introduce the first pre-trained encoder for global child development, trained on 357,709 children across 44 countries using UNICEF survey data. With only 50 training samples, the pre-trained encoder achieves an average AUC of 0.65 (95% CI: […]

Ver mais

Which country puts the largest share of its venture capital into deep tech? Not the United States. Not China. The Swiss Deep Tech Report 2026, published today by Deep Tech Nation Switzerland with Founderful, Kickfund, Startupticker.ch and Dealroom.co and launched at VivaTech in Paris, puts Switzerland first in the world. The rest of the data explains both why that lead is real and where it runs out. Deep tech is the slow part of the technology economy. It […]

Ver mais

arXiv:2602.11324v1 Announce Type: new Abstract: A key principle in string processing is local consistency: using short contexts to handle matching fragments of a string consistently. String synchronizing sets [Kempa, Kociumaka; STOC 2019] are an influential instantiation of this principle. A $tau$-synchronizing set of a length-$n$ string is a set of $O(n/tau)$ positions, chosen via their length-$2tau$ contexts, such that (outside highly periodic regions) at least one position in every length-$tau$ window is selected. Among their applications are faster […]

Ver mais

arXiv:2604.01259v1 Announce Type: new Abstract: With the rise of vision-language models (VLM), their application for autonomous driving (VLM4AD) has gained significant attention. Meanwhile, in autonomous driving, closed-loop evaluation has become widely recognized as a more reliable validation method than open-loop evaluation, as it can evaluate the performance of the model under cumulative errors and out-of-distribution inputs. However, existing VLM4AD benchmarks evaluate the model`s scene understanding ability under open-loop, i.e., via static question-answer (QA) dataset. This kind of evaluation […]

Ver mais

Organizations and enterprises across domains such as healthcare, finance, and scientific research are increasingly required to extract collective intelligence from distributed, siloed datasets while adhering to strict privacy, regulatory, and sovereignty requirements. Federated Learning (FL) enables collaborative model building without sharing sensitive raw data, but faces growing challenges posed by statistical heterogeneity, system diversity, and the computational burden from complex models. This study examines the potential of quantum-assisted federated learning, which could cut the number of parameters in […]

Ver mais

In this tutorial, we implement an end-to-end workflow for Salesforce CodeGen. We load a CodeGen model from Hugging Face, prepare it for code generation, and use it to generate Python functions from natural-language prompts. We then move beyond basic inference by adding function extraction, syntax checking, static safety checks, unit-test-based validation, best-of-N candidate reranking, multi-step program synthesis, prompt-style experimentation, benchmark visualization, and artifact export. Through this workflow, we learn how CodeGen can be used not only as a […]

Ver mais

arXiv:2601.05411v1 Announce Type: new Abstract: This work investigates how measuring information entropy of text can be used to estimate its readability. We propose a visualization framework that can be used to approximate information entropy of text using multiple language models and visualize the result. The end goal is to use this method to estimate and improve readability and clarity of administrative or bureaucratic texts. Our toolset is available as a libre software on https://github.com/ufal/Glitter.

Ver mais

There is a widening chasm in enterprise technology that is quietly undermining billions in AI investments. On one side, AI capabilities are advancing faster than most organizations can track. On the other side, most enterprises are still struggling to show meaningful returns from their AI investments. Between these two realities sits what I call the execution gap – the structural disconnect between AI capability demonstrated at the model layer and measurable enterprise transformation outcomes realized at the workflow […]

Ver mais