From the 2 of 6 linked papers with an AI index.
6 papers
Benefits and Limitations of Communication in Multi-Agent Reasoning
Michael Rizvi-Martel, Satwik Bhattamishra, Neil Rathi +2
The paper introduces a theoretical framework for analyzing how communication among multiple agents affects their ability to perform complex reasoning tasks, providing bounds on req…
From Expressivity to Sample Complexity: Narrow Teachers for Transformers via C-RASP
Michael Rizvi-Martel, Satwik Bhattamishra, Guillaume Rabusseau +1
The paper derives preliminary sample complexity bounds for learning C‑RASP constructions with Transformer models, linking their expressive power to learnability.
The Illusion of Superposition? A Principled Analysis of Latent Thinking in Language Models
Michael Rizvi-Martel, Guillaume Rabusseau, Marius Mosbach
Latent reasoning via continuous chain-of-thoughts (Latent CoT) has emerged as a promising alternative to discrete CoT reasoning. Operating in continuous space increases expressivit…
On the Role of Depth in the Expressivity of RNNs
Maude Lizaire, Michael Rizvi-Martel, Ãric Dupuis +1
The benefits of depth in feedforward neural networks are well known: composing multiple layers of linear transformations with nonlinear activations enables complex computations. Wh…
FlowQ-Net: A Generative Framework for Automated Quantum Circuit Design
Jun Dai, Michael Rizvi-Martel, Guillaume Rabusseau
Designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing, particularly for noisy intermediate-scale quantum (NISQ) devices, wher…
Numerical PDE solvers outperform neural PDE solvers
Patrick Chatain, Michael Rizvi-Martel, Guillaume Rabusseau +1
We present DeepFDM, a differentiable finite-difference framework for learning spatially varying coefficients in time-dependent partial differential equations (PDEs). By embedding a…