3 papers
cs.CV2026
Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation
Marina Chagas Bulach Gapski, Vinicius Atsushi Sato Kawai, Gustavo Rosseto Leticio +3
Feature extraction involves the identification and extraction of salient characteristics or patterns, including edges, textures, shapes, and color attributes. Contemporary feature…
cs.CL2026
AbstRAG: Learning to Abstract for Retrieval Problems
Lei Xu, Xin Quan, Daniel Pedronette +1
Retrieval-augmented generation often fails when the query, the document evidence, and the user's intent are expressed at different levels of abstraction. A query may ask about a cl…
cs.CL2025
Factuality and Transparency Are All RAG Needs! Self-Explaining Contrastive Evidence Re-ranking
Francielle Vargas, Daniel Pedronette
This extended abstract introduces Self-Explaining Contrastive Evidence Re-Ranking (CER), a novel method that restructures retrieval around factual evidence by fine-tuning embedding…