arXiv:2603.24837v1 Announce Type: new Abstract: Large Language Models (LLMs) face critical challenges when analyzing security vulnerabilities in real world codebases: token limits prevent loading entire repositories, code embeddings fail to capture inter procedural data flows, and LLMs struggle to generate complex static analysis queries. These limitations force existing approaches to operate on isolated code snippets, missing vulnerabilities that span multiple functions and files. We introduce codebadger, an open source Model Context Protocol (MCP) server that integrates Joern’s Code […]

Ver mais

I’ve seen configuration managed on paper, with change notices stacked on drawings like a to-do pile. I’ve seen teams lose the ability to rebuild the same product in another country because they couldn’t produce a complete BOM. The problem wasn’t “conservatism.” The problem was a broken evidence supply chain. Certification-ready digital threads fix that. Here’s what changed when we rebuilt that supply chain: Audit pack assembly: 2 months → 2 weeks Evidence control: Google Drive (no traceability, low access control) […]

Ver mais

arXiv:2604.05005v1 Announce Type: new Abstract: Large language models are increasingly used as educational assistants, yet evaluation of their educational capabilities remains concentrated on question-answering and tutoring tasks. A critical gap exists for multimedia instructional content generation — the ability to produce coherent, diagram-rich explanations that combine geometrically accurate visuals with step-by-step reasoning. We present EduIllustrate, a benchmark for evaluating LLMs on interleaved text-diagram explanation generation for K-12 STEM problems. The benchmark comprises 230 problems spanning five subjects and […]

Ver mais

Whatever stage of growth you’re currently in, your north star should be the function you want to become synonymous with. What do you want your name to mean? Because after all is said and done, your product or service is a means to an end. All the technical excellence and expert architectural decisions that contribute to your unique service offering are interpreted by the end user in boring, simple thought processes—>Whenever I want to do xxxx, I think […]

Ver mais

Constrained autoencoders (cAE) provide a successful path towards interpretable dimensionality reduction by enforcing geometric structure on latent spaces. However, standard cAEs cannot adapt to varying physical parameters or environmental conditions without conflating these contextual shifts with the primary input. To address this, we integrated a neuromodulatory mechanism into the cAE framework to allow for context-dependent manifold learning. This paper introduces the Neuromodulated Constrained Autoencoder (NcAE), which adaptively parameterizes geometric constraints via gain and bias tuning conditioned on static […]

Ver mais

We introduce a Monte Carlo (MC) dataset of single- and two-track drift chamber events to advance Machine Learning (ML)-based track reconstruction. To enable standardized and comparable evaluation, we define track reconstruction specific metrics and report results for traditional track reconstruction algorithms and a Graph Neural Networks (GNNs) method, facilitating rigorous, reproducible validation for future research.

Ver mais

Membership inference attacks (MIAs) pose a serious threat to the privacy of machine learning models by allowing adversaries to determine whether a specific data sample was included in the training set. Although federated learning (FL) is widely regarded as a privacy-aware training paradigm due to its decentralized nature, recent evidence shows that the final global model can still leak sensitive membership information through black-box access. In this paper, we introduce Res-MIA, a novel training-free and black-box membership inference […]

Ver mais

Key Highlights: Anthropic acquiring the $400 million biotech startup marks a significant step into the life sciences space. Biology is emerging as a natural next border to cross after artificial intelligence companies’ search for high-impact applications beyond chatbots. Their ambition is simple; they want AI models to help reduce the cost, time, and uncertainty involved in developing new drugs.  The intent behind this deal is what makes it stand out. Coefficient Bio is an unusually early-stage startup with […]

Ver mais