arXiv:2601.10957v1 Announce Type: new Abstract: Social media platforms are important venues for identity expression, and the Human-Computer Interaction community has been paying growing attention to how marginalized groups express their identities on these platforms. Joining the emerging literature on intersectional experiences, we study blind TikTokers (“BlindTokers”) who are also women and/or LGBTQ+. Using interview data from rev{41} participants, we identify their intersectional experiences as mediated by TikTok’s socio-technical affordances. We argue that BlindTokers’ intersectional marginalization is infrastructural: TikTok’s […]

Ver mais

When working on Spark jobs (in Scala), we often sequentially write the code in a single class, giving more attention to the transformations we do and forgetting how our code is structured or even if it’s tested. Today I’ll be talking about how I personally like to structure and design my Spark jobs, such as they are highly maintainable and testable.

Ver mais

Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are private, scarce and structurally heterogeneous, making internet-scale pre-training infeasible. To overcome this data scarcity, We introduce $textbf{RDB-PFN}$, the first relational foundation model trained purely via $textbf{synthetic data}$. Inspired by Prior-Data Fitted Networks (PFNs) where synthetic data generated from Structural Causal Models (SCMs) enables reasoning on single tables, we design […]

Ver mais

arXiv:2602.13848v1 Announce Type: cross Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a fixed, reference-conditional setting. Existing CTM detectors construct test martingales by continually growing a reference set with each incoming sample, using it to assess how atypical the new sample is relative to past observations. While this design yields anytime-valid type-I error control, it suffers from test-time contamination: after a change, post-shift observations enter the […]

Ver mais

The current state of AI agent development is characterized by significant architectural fragmentation. Software devs building autonomous systems must generally commit to one of several competing ecosystems: LangChain, AutoGen, CrewAI, OpenAI Assistants, or the more recent Claude Code. Each of these ‘Five Frameworks’ utilizes a proprietary method for defining agent logic, memory persistence, and tool execution. This lack of a common standard creates high switching costs and technical debt, as moving an agent from one framework to another […]

Ver mais

arXiv:2603.06610v1 Announce Type: new Abstract: Large language model (LLM) post-training enhances latent skills, unlocks value alignment, improves performance, and enables domain adaptation. Unfortunately, post-training is known to induce forgetting, especially in the ubiquitous use-case of leveraging third-party pre-trained models, which is typically understood as a loss of parametric or factual knowledge. We argue that this accuracy-centric view is insufficient for modern foundation models and instead define forgetting as systematic model drift that degrades behavior and user experience. In […]

Ver mais

6G facilitates deployment of Federated Learning (FL) in the Space-Air-Ground Integrated Network (SAGIN), yet FL confronts challenges such as resource constrained and unbalanced data distribution. To address these issues, this paper proposes a Hierarchical Split Federated Learning (HSFL) framework and derives its upper bound of loss function. To minimize the weighted sum of training loss and latency, we formulate a joint optimization problem that integrates device association, model split layer selection, and resource allocation. We decompose the original […]

Ver mais

arXiv:2604.18767v1 Announce Type: new Abstract: Recent disruptions at major maritime chokepoints have exposed the structural fragility of liner shipping networks. Existing indicators measure connectivity, but none quantify its structural vulnerability from a supply-side perspective. We propose the Maritime Connectivity Vulnerability Index (MCVI), capturing three dimensions mapped to distinct UNCTAD sources: low overall connectivity (LSCI), weak bilateral integration (LSBCI), and port infrastructure concentration (PLSCI). The index covers 185 economies over 2006-2025 using pooled fractional rank normalization and equal-weight aggregation […]

Ver mais