Xiana Carrera

We propose an algorithm to reconstruct explicit polygonal meshes from discretely sampled Signed Distance Function (SDF) data, which is especially effective at recovering sharp features. Building on the traditional Dual Contouring of Hermite Data method, we design and solve a quadratic optimization problem to decide the optimal placement of the mesh’s vertices within each cell of a regular grid. Critically, this optimization relies solely on discretely sampled SDF data, without requiring arbitrary access to the function, gradient information, or training on large-scale datasets. Our method sets a new state of the art in surface reconstruction from SDFs at medium and high resolutions, and opens the door for applications in 3D modeling and design.

This paper introduces Query Harmfulness Prediction (QHP), a novel extension of Query Performance Prediction that focuses on predicting the potential harmfulness of search results. While traditional QPP methods predict standard retrieval metrics, QHP addresses the growing need for safer information retrieval by anticipating when queries might return harmful but topically relevant results. We investigate three families of predictors: classical pre-retrieval QPP methods, LLM-based strategies leveraging signals such as controversy and misinformation, and a query quality classifier adapted from prior work. Using datasets from TREC and CLEF campaigns, we evaluate these approaches with compatibility harmful as the target measure. Our results show that while traditional QPP predictors capture limited signals of harmfulness, LLM-based methods consistently provide stronger correlations, especially on high-risk queries. These findings establish QHP as a timely research direction for developing safer retrieval systems that balance relevance with user safety.

Misinformation on the Internet poses significant risks to users seeking health information. This paper addresses the challenge of generating effective health-related queries to promote reliable search results. We propose a method leveraging Large Language Models to generate synthetic narratives that guide the creation of alternative queries. These queries are designed to retrieve more helpful and fewer harmful documents compared to those retrieved by the original user queries. We evaluate the effectiveness of these queries using classic and neural retrieval models across multiple datasets, demonstrating promising improvements in retrieving reputable content.