3 citations · 5 across the 22 of their papers we have counts for
23 papers
Mining the Design Space of Multi-Agent Workflows
Xavier Cadet, Junyan Cheng, Peter Chin +1
Designing large language model (LLM) multi-agent systems (MAS) still relies on trial and error and informal "folk wisdom." Manual studies test only a few communication structures o…
BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense
Simona Boboila, Xavier Cadet, Edward Koh +4
Cyber attacks are increasingly automated, narrowing the time available for human analysts to detect, reason about, and respond to intrusions. Large language models (LLMs) offer a p…
DNQ: Deep Nash Q-Network for Partially Observable n-Player Games
Qintong Xie, Edward Koh, Xavier Cadet +1
Many real-world competitive systems require multiple decision-makers to act simultaneously under shared constraints, limited information, and repeated interaction, as in auctions,…
Algebraic Representability as the Limiting Regime of Grokking: An Exactly Solvable Model with Holomorphic Activations
Chon-Fai Kam, Xavier Cadet, Miloud Bessafi +1
Neural networks trained on modular arithmetic exhibit grokking, a delayed transition from memorisation to generalisation known to depend on model capacity: too little and the netwo…
RADAR: Relative Angular Divergence Across Representations
Xavier Cadet, Mateusz Nowak, Peter Chin
Machine learning methods rely on data. However, gathering suitable data can be challenging due to availability constraints, cost, or the need for domain expertise. Expanding datase…
Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability
Chon-Fai Kam, Xavier Cadet, Miloud Bessafi +1
While world models learn compact representations of complex environments, they lack a physics-grounded metric to assess the structural fidelity of their latent spaces. We identify…
Retrieval-Augmented LLMs for Security Incident Analysis
Xavier Cadet, Aditya Vikram Singh, Harsh Mamania +6
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts, network traffic records, and authe…
ABCD: All Biases Come Disguised
Mateusz Nowak, Xavier Cadet, Peter Chin
Multiple-choice question (MCQ) benchmarks have been a standard evaluation practice for measuring LLMs' ability to reason and answer knowledge-based questions. Through a synthetic N…
AsymPuzl: An Asymmetric Puzzle for multi-agent cooperation
Xavier Cadet, Edward Koh, Peter Chin
Large Language Model (LLM) agents are increasingly studied in multi-turn, multi-agent scenarios, yet most existing setups emphasize open-ended role-play rather than controlled eval…
Hermitian Matrix Function Synthesis without Block-Encoding
Anuradha Mahasinghe, Kaushika De Silva, Xavier Cadet +3
Implementing polynomial functions of Hermitian matrices on quantum hardware is a foundational task in quantum computing, critical for accurate Hamiltonian simulation, quantum linea…
WARP: Weight Teleportation for Attack-Resilient Unlearning Protocols
Mohammad M Maheri, Xavier Cadet, Peter Chin +1
Approximate machine unlearning aims to efficiently remove the influence of specific data points from a trained model, offering a practical alternative to full retraining. However,…
WAN3DNS: Weak Adversarial Networks for Solving 3D Incompressible Navier-Stokes Equations
Wenran Li, Xavier Cadet, Miloud Bessafi +7
The 3D incompressible Navier-Stokes equations model essential fluid phenomena, including turbulence and aerodynamics, but are challenging to solve due to nonlinearity and limited s…
Quantum Autoencoder: An efficient approach to quantum feature map generation
Shengxin Zhuang, Yusen Wu, Xavier F. Cadet +6
Quantum machine learning methods often rely on fixed, hand-crafted quantum encodings that may not capture optimal features for downstream tasks. In this work, we study the power of…
Nash Q-Network for Multi-Agent Cybersecurity Simulation
Qintong Xie, Edward Koh, Xavier Cadet +1
Cybersecurity defense involves interactions between adversarial parties (namely defenders and hackers), making multi-agent reinforcement learning (MARL) an ideal approach for model…
PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense
Xavier Cadet, Simona Boboila, Sie Hendrata Dharmawan +2
Cyber defense requires automating defensive decision-making under stealthy, deceptive, and continuously evolving adversarial strategies. The FlipIt game provides a foundational fra…
Quantitative Resilience Modeling for Autonomous Cyber Defense
Xavier Cadet, Simona Boboila, Edward Koh +2
Cyber resilience is the ability of a system to recover from an attack with minimal impact on system operations. However, characterizing a network's resilience under a cyber attack…
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
Wen-ran Li, Xavier F. Cadet, David Medina-Ortiz +6
Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great succes…
Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
Xavier F. Cadet, Anastasia Borovykh, Mohammad Malekzadeh +2
Machine unlearning (MU) aims to remove the influence of particular data points from the learnable parameters of a trained machine learning model. This is a crucial capability in li…
Low-Energy On-Device Personalization for MCUs
Yushan Huang, Ranya Aloufi, Xavier Cadet +3
Microcontroller Units (MCUs) are ideal platforms for edge applications due to their low cost and energy consumption, and are widely used in various applications, including personal…
Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine
Shengxin Zhuang, John Tanner, Yusen Wu +7
Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a…
Evaluating The Robustness of Self-Supervised Representations to Background/Foreground Removal
Xavier F. Cadet, Ranya Aloufi, Alain Miranville +2
Despite impressive empirical advances of SSL in solving various tasks, the problem of understanding and characterizing SSL representations learned from input data remains relativel…
A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment
Xavier F. Cadet, Ranya Aloufi, Sara Ahmadi-Abhari +1
Automating dysarthria assessments offers the opportunity to develop practical, low-cost tools that address the current limitations of manual and subjective assessments. Nonetheless…
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data
Xavier F. Cadet, Sara Ahmadi-Abhari, Hamed Haddadi
Errors might not have the same consequences depending on the task at hand. Nevertheless, there is limited research investigating the impact of imbalance in the contribution of diff…