Portrait de Alejandro Salinas-Medina

Alejandro Salinas-Medina

alejandro.salinas-medina@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Xue (Steve) Liu
Sujets de recherche
Apprentissage profond
Théorie de l'apprentissage automatique
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

RAG-Safe: A Recall-First Safety Framework Comparing Open-Source and Commercial LLM Moderation Pipelines
Alejandro Salinas-Medina
Xue Liu
False negatives—missed detections of harmful content—remain the dominant risk in safety-critical moderation pipelines. We introduce RAG-… (voir plus)Safe, a recall-first framework that integrates distribution-preserving contrastive augmentation, committee-diverse retrieval, and a recall-oriented decision policy into a unified moderation architecture. The framework is evaluated using a compact, fully auditable testbed designed to enforce strict leakage control: original samples alone determine the train–test split, and all paraphrases inherit their parent assignment. Within this controlled setting, conventional retrieval-augmented pipelines—both commercial (API embeddings + hosted LLM) and open-source (FAISS + local LLaMA-3)—consistently under-detect unsafe content (FLAGGED recall 0.44). Applying RAG-Safe raises FLAGGED recall to approximately 0.56 across both stacks while preserving overall accuracy ( 0.66) and macro-F1 ( 0.65). A non-RAG classifier baseline provided in our public repository shows similar recallfirst behaviour, reinforcing that these gains are not architecture-specific. Rather than comparing individual model components, we interpret the results as pipeline-level evidence that boundary-focused augmentation, retrieval diversity, and calibrated thresholds jointly shift LLM moderation into a safer operating regime. We conclude by discussing limitations—particularly domain transferability and adversarial robustness—and outline directions for scaling RAG-Safe to broader moderation contexts. Keywords: Content moderation, Recall-first classification, Distribution-preserving data augmentation, Committee-based retrieval, Retrieval-augmented large language models, Safety-critical AI
2026-06-28
Canadian Conference on Artificial Intelligence (publié)
proceedings.mlr.press
proceedings.mlr.press
Bifurcation Preservation as a Physics Diagnostic for Neural Phase-Field Surrogates
Alejandro Salinas-Medina
Anisleidy Gonzalez-Mitjans
Xue Liu
A common approach for evaluating neural surrogates of phase-field equations is aggregate field error against a reference solver, a measure t… (voir plus)hat can overlook bifurcations: abrupt shifts between qualitatively distinct outcomes, e.g., whether a phase-field droplet dissolves or persists. We propose evaluating neural phase-field surrogates in terms of their capacity for bifurcation preservation. We demonstrate the diagnostic on the Cahn-Hilliard (CH) critical droplet boundary using a droplet-aware Fourier Neural Operator, which reaches a moderate held-out rollout error, with relative
2026-05-29
AI4Physics @ International Conference on Machine Learning (publié)
openreview.net
openreview.net