The engineer had been at the company for three weeks. Smart and experienced, he had shipped production systems at two previous companies and came with strong references. And she was visibly miserable. Not because the work was uninteresting or the team was difficult, but because getting anything done required navigating a gauntlet that nobody had bothered to document: a local dev environment that only worked on one specific version of macOS, a deployment pipeline that required manually triggering […]

Ver mais

arXiv:2601.16372v1 Announce Type: new Abstract: Community detection (CD) on signed networks is crucial for understanding how positive and negative relations jointly shape network structure. However, existing CD methods often yield inconsistent communities due to noisy or conflicting edge signs. In this paper, we propose ReCon, a model-agnostic post-processing framework that progressively refines community structures through four iterative steps: (1) structural refinement, (2) boundary refinement, (3) contrastive learning, and (4) clustering. Extensive experiments on eighteen synthetic and four real-world […]

Ver mais

The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

Ver mais

arXiv:2602.12323v1 Announce Type: new Abstract: The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs to improve performance. These methods typically include some way of selecting LoRAs from a pool and tune merging coefficients based on a task-specific dataset. While adaptive merging methods have demonstrated improvements in some settings, no past work has attempted to recycle LoRAs found “in the wild” on model repositories like […]

Ver mais

In this tutorial, we build a workflow that combines Magika’s deep-learning-based file type detection with OpenAI’s language intelligence to create a practical and insightful analysis pipeline. We begin by setting up the required libraries, securely connecting to the OpenAI API, and initializing Magika to classify files directly from raw bytes rather than relying on filenames or extensions. As we move through the tutorial, we explore batch scanning, confidence modes, spoofed-file detection, forensic-style analysis, upload-pipeline risk scoring, and structured […]

Ver mais

arXiv:2601.21067v1 Announce Type: new Abstract: Graphs are a fundamental data structure for representing relational information in domains such as social networks, molecular systems, and knowledge graphs. However, graph learning models often suffer from limited generalization when applied beyond their training distributions. In practice, distribution shifts may arise from changes in graph structure, domain semantics, available modalities, or task formulations. To address these challenges, graph foundation models (GFMs) have recently emerged, aiming to learn general-purpose representations through large-scale pretraining […]

Ver mais

GPT-5.5 prompting guide Now that GPT-5.5 is available in the API, OpenAI have released a wealth of useful tips on how best to prompt the new model. Here’s a neat trick they recommend for applications that might spend considerable time thinking before returning a user-visible response: Before any tool calls for a multi-step task, send a short user-visible update that acknowledges the request and states the first step. Keep it to one or two sentences. I’ve already noticed […]

Ver mais

arXiv:2603.23667v1 Announce Type: new Abstract: We introduce Echoes, a new dataset for music deepfake detection designed for training and benchmarking detectors under realistic and provider-diverse conditions. Echoes comprises 3,577 tracks (110 hours of audio) spanning multiple genres (pop, rock, electronic), and includes content generated by ten popular AI music generation systems. To prevent shortcut learning and promote robust generalization, the dataset is deliberately constructed to be challenging, enforcing semantic-level alignment between spoofed audio and bona fide references. This […]

Ver mais

arXiv:2601.00107v1 Announce Type: new Abstract: We introduce an affine invariant Langevin dynamics (ALDI) framework for the efficient estimation of rare events in nonlinear dynamical systems. Rare events are formulated as Bayesian inverse problems through a nonsmooth limit-state function whose zero level set characterises the event of interest. To overcome the nondifferentiability of this function, we propose a smooth approximation that preserves the failure set and yields a posterior distribution satisfying the small-noise limit. The resulting potential is sampled […]

Ver mais