Author

Cuong Xuan Chu

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Preprint Aug 2026

A Storage-Retrieval Gap in Parametric Knowledge Graph Memory

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. We study an alternative: compiling a knowledge graph offline into a bank of LoRA adapters, one per entity, that serve as a parametric knowl...

Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passing

This work extends Relational Sparse Matrix Multiplication (RSPMM), originally designed to losslessly lower the space complexity of composition-based MP with pointwise composition functions, to support more expressive functions (e.g., 2x2 block-diagonal matrix multiplication, Givens rotation, circular correlation).

Huu Tan Mai, Cuong Xuan Chu, Heiko Paulheim et al. · 0 citations

#artificial intelligence Preprint Sep 2026

APOLO: Automatic Prompt Optimization for Ontology Learning

This work casts OL as an explicit prompt optimization problem over LLM modules, and proposes two ontology learner architectures: a greedy and an autoregressive learner, and optimize both using GEPA, a greedy evolutionary prompt optimizer built on DSPy.

Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu et al. · 0 citations

#machine learning Preprint Aug 2026

A Storage–Retrieval Gap in Parametric Knowledge Graph Memory

The results establish that parametric knowledge graph memory is feasible for storing knowledge, and identify selecting and composing the right adapters by a mechanism other than semantic similarity as the central open problem—motivating a learned, query-conditioned composition mechanism.

Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov et al. · 0 citations

Jul 2026

MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models

A controlled study isolates the source of MADA-RL's gains: the counterfactual advantage produces the highest critic improvement rate of any model evaluated, indicating that trained critics learn to correct generator errors rather than to imitate them.

Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov et al. · arXiv.org · 0 citations