Model Detail
Matrix-Game-3.0
—OISMA: On-the-fly In-memory Stochastic Multiplication Architecture for Matrix-Multiplication Workloads
arXiv:2508.08822v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) models are currently driven by a significant upscaling of their complexity, with massive matrix-multiplication workloads representing the major computational bottleneck. In-memory computing (IMC) architectures are
Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training
arXiv:2509.11983v2 Announce Type: replace Abstract: Neural network (NN) training is inherently a large-scale matrix optimization problem, yet the matrix structure of NN parameters has long been overlooked. Recently, the optimizer Muon \citep{jordanmuon}, which explicitly exploits this structure, has
Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
arXiv:2511.21686v2 Announce Type: replace Abstract: Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sensitive. Many such generation tasks require coordinated multi-agent workflows, where specialized agent
A Framework for Human-AI Q-Matrix Refinement: A NeuralCDM Evaluation
arXiv:2604.16398v1 Announce Type: cross Abstract: Q-matrices are a cornerstone of theory-driven assessment and learning analytics, making item demands and students' underlying knowledge components and misconceptions explicit and actionable. However, Q-matrices are typically crafted by experts, makin
Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario
arXiv:2604.18450v1 Announce Type: cross Abstract: Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before overfitting dominates. We provide an analytically tractable random-matrix model that reproduces this phe
Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations
arXiv:2604.18539v1 Announce Type: new Abstract: This paper studies how empirical dialogue-flow statistics can be incorporated into Next Dialogue Act Prediction (NDAP). A KL regularization term is proposed that aligns predicted act distributions with corpus-derived transition patterns. Evaluated on a
