2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2026
A Storage-Retrieval Gap in Parametric Knowledge Graph Memory
Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov +1
Graph retrieval-augmented generation places retrieved subgraphs into the model's context window at query time, paying a recurring token cost and exposing source data on every call.…
cs.LG2026
MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models
Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov +3
Large language models achieve strong reasoning performance, but often at prohibitive training cost - a challenge that is especially acute for compact models (…
cs.CL2024★ 2 cited
Do LLMs Really Adapt to Domains? An Ontology Learning Perspective
Huu Tan Mai, Cuong Xuan Chu, Heiko Paulheim
Large Language Models (LLMs) have demonstrated unprecedented prowess across various natural language processing tasks in various application domains. Recent studies show that LLMs…