Publications

GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning

Published in EMNLP 2025 (Findings), 2025

We propose GRAD, a dynamic demonstration-based approach where an LLM model is trained to generate input-specific concise demonstrations. By tailoring demonstrations to each input, our method offers better contextual support than traditional RAG approaches.

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Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration

Published in bioRxiv, 2025

We introduce a generative framework for constructing large-scale kinetic metabolic models through latent space exploration. By repurposing pretrained neural network generators across different physiological contexts, our method enables efficient and interpretable inference of kinetic parameters, facilitating targeted model design for diverse metabolic behaviors.

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Oussama Gabouj

Publications

GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning

Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration