Portrait of Cédric Caruzzo

Cédric Caruzzo

AI Research Scientist at Lunit

Reliable foundation models under distribution shift

Representation learning · Post-training · Evidence-grounded biomedical AI

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Research Profile

I study how foundation models behave when their data, tasks, and evidence change. My research spans representation learning, post-training, retrieval, and evaluation, with a focus on reliable biomedical AI.

At Lunit, I work on self-supervised vision models for whole-slide pathology and post-training and evaluation of large medical language models. My recent first-author work introduced deceptive grounding, a clinical RAG failure in which a model can faithfully cite real evidence while assigning it to the wrong medical entity.

At KAIST, I developed CellPainTR, a Transformer for cross-dataset Cell Painting analysis that generalizes to unseen datasets without target-dataset fine-tuning. Earlier, at Institut Pasteur, I built machine-learning systems for phenotypic drug discovery and infectious-disease research.

I am particularly interested in adaptive and multimodal learning, reliable post-training, and medical AI systems whose evidence and intermediate reasoning can be independently verified.

Selected Research

Deceptive Grounding failure structure
First-author preprint · 2026

Deceptive Grounding

Clinical RAG can relay real evidence, cite a genuine source, and still attribute that evidence to the wrong medical entity. Standard hallucination and faithfulness checks miss the failure.

New Failure Mode 13 models 740 production pairs

My contribution: I conceived and led the study from the initial failure observation through benchmark design, software, formal analysis, production validation, visualization, and writing.

Paper Project page
CellPainTR cross-dataset representation-learning concept
First-author preprint and code · 2025

CellPainTR

A Transformer for Cell Painting representations that preserves biological signal across batch and feature shifts, including generalization to an entirely unseen dataset without fine-tuning.

Cross-dataset OOD No target fine-tuning Open source

Research contribution: Source-specific context tokens and a staged self-supervised and contrastive curriculum make heterogeneous biological datasets usable through one representation model.

Paper Code Project page

Publications and Research Outputs

arXiv 2026
Deceptive Grounding: Entity Attribution Failure in Clinical Retrieval-Augmented Generation
Cédric Caruzzo, Donggeun Yoo, Tae Soo Kim
arXiv preprint arXiv:2607.09349, 2026

Introduces an entity-attribution failure missed by standard RAG evaluation, characterizes it in controlled and production settings, and proposes entity-attribution verification.

PDF Project
arXiv 2025
CellPainTR: Generalizable Representation Learning for Cross-Dataset Cell Painting Analysis
Cédric Caruzzo, Jong Chul Ye
arXiv preprint arXiv:2509.06986, 2025

Learns batch-robust cellular representations across heterogeneous studies and generalizes to an unseen dataset without target-dataset fine-tuning.

PDF Code Project
SLAS Europe 2024 Poster
Cellular Phenotypic Profiling: Combining Live Imaging and Cell Painting Techniques
Soonju Park, Cédric Caruzzo, Nakyung Lee,
Poster presented at SLAS Europe 2024, Barcelona, Spain

Combines longitudinal live-cell imaging with endpoint Cell Painting for richer phenotypic profiling in high-content drug discovery.

Poster
Scientific Reports 2021
Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence
Antsa Rakotonirina, Cédric Caruzzo, Valentine Ballan,
Scientific Reports 11, 21355, 2021

Uses machine learning with MALDI-TOF mass spectrometry to detect Wolbachia infection in Aedes aegypti mosquitoes for vector-control research.

Paper

Selected Experience

Lunit, Seoul
Mar 2025 - Present
AI Research Scientist
  • Own post-training and evaluation pipelines for large medical language models, including benchmark design, retrieval, and knowledge-grounded reasoning.
  • Develop self-supervised Vision Transformers for whole-slide pathology and distributed inference systems for clinical-scale image collections.
  • Lead research on evaluation blind spots in medical RAG, including the first-author Deceptive Grounding study.
KAIST, Kim Jaechul Graduate School of AI
Feb 2023 - Feb 2025
M.S. Researcher, advisor: Prof. Jong Chul Ye

Full research experience

Selected Technical Writing

All research and writing