This paper introduces SuperCond-GNN, a graph neural network surrogate model designed to predict voltage distribution in high-temperature superconducting magnets by mapping lumped-element circuits to graph representations. The model achieves a mean MAPE of 4.3% on tape stacks and enables fast inference of current redistribution across various circuit configurations.
This work employs Neural ODEs equipped with a curated collection of equilibrium points to perform classification tasks. The planted attractors serve as indicators for target classes, while the velocity field shapes the dynamical landscape to direct inputs toward their corresponding destinations.
Researchers propose Self-Aware Scheduling (SAS), a method that learns an optimal token unmasking order for masked diffusion language models to improve generation quality. By deriving a tractable upper bound on sequential decoding mismatch, the approach casts order selection as a policy optimization problem using Group Relative Policy Optimization.
A Reddit user is requesting feedback from individuals who have updated to the Minimax M3 model from version M2.7. The post seeks community insights on the differences and performance between these two iterations.
The author demonstrates running the GLM-5.2 NVFP4 model on four NVIDIA GB10 DGX Spark nodes with a 128K context window, achieving usable serving performance through aggressive system optimization.
A user demonstrates fine-tuning a 7B instruction model on Apple Silicon using MLX to shift its style to high-fantasy literature. The experiment shows that a small, curated dataset can significantly alter a model's register and diction with minimal computational resources.
Researchers have introduced SVD-Surgeon, a training-free method that applies the Optimal Brain Surgeon framework to singular-value decomposition for compressing large language models. This approach computes closed-form updates for retained singular values to compensate for truncation errors and determines which values to prune based on saliency.
The article addresses the challenge of contrastive representation learning on physiological signals where subject-specific baselines interfere with class-level objectives, causing models to lose individual variation necessary for generalization. The authors propose a patient-aware contrastive objective for Paroxysmal Atrial Fibrillation detection that forms positive pairs only from same-patient segments to preserve sinus rhythm baselines while separating classes.
This paper develops a spectral theory for normalized corrected Graph Neural Network (GNN) propagation, focusing on the symmetric normalized adjacency matrix with its degree-stationary component removed to isolate the direction tied to oversmoothing.
Researchers introduce MORL-A2C, a sequential decision-making extension to the MOPI-HFRS system that uses an Advantage Actor-Critic algorithm to optimize the trade-off between user preference and nutritional health in food recommendations.
The author developed a specialized agent harness designed to address the specific failure modes of small local models, such as failed tool calls and poor state tracking. This custom framework enables smaller models like Qwen 3.5 4b to effectively manage remote servers.
The author presents the 800M version of a model that converts images into controllable characters, designed to run comfortably on consumer GPUs. This iteration increases context to 12 latent frames and improves stability while maintaining high performance, achieving over 60 fps on an RTX 5090.
The author introduces HoLo-ToLk, a research project building speech-to-text (STT) and text-to-speech (TTS) models using the zero-parameter HSL byte substrate without tokenizers or learned input embeddings. The work demonstrates that raw HSL bytes can serve as a viable signal for audio processing when combined with specific architectural modifications.
The llama.cpp project has released version b9837, which introduces a new `--reasoning-preserve` flag for the Jinja chat template to retain reasoning tokens. This update also includes corrected help messages and provides pre-built binaries for macOS, Linux, Windows, Android, and openEuler across various hardware backends.
HP Inc. is scaling its strategic partnership with OpenAI following successful pilots, deploying AI across customer experiences, employee productivity, and software development. The company utilizes the OpenAI Frontier platform as a unified operating model to govern context, permissions, and evaluation as it moves from experimental use cases to enterprise-wide production.
The article introduces KORE, a method that determines optimal spline regression resolution in closed form rather than through exhaustive hyperparameter search. By leveraging classical approximation theory and the PRESS identity, it analytically balances bias and noise scales to achieve accuracy comparable to grid sweeps with significantly less compute.
This study demonstrates that neural networks can reliably learn Conway's Game of Life dynamics using minimal architectures by employing specific inductive biases rather than relying on large-scale search processes. The authors show that network variants with alternative activation functions significantly outperform standard Rectified Linear Units, particularly through the use of second-degree polynomial activations.
This study quantifies the agreement between data-similarity and data-influence measures used for tracing LLM outputs back to training data, revealing a significant overlap with an asymmetry where data-influence ranks top similar documents more consistently. Experiments across models including OLMo2-1B, Qwen3-1.7B, LlaMa3.2-1B, Gemma3-1B, and GPT2 demonstrate that this asymmetry allows for a favorable cost-accuracy trade-off by using data-influence to refine cheaper data-similarity results.
This article introduces neural networks to statisticians by demystifying the field through the lens of linear regression approximation.
Researchers propose a scalable framework for merging independently trained billion-parameter transformers using linear mode connectivity, addressing scalability limits in existing methods. The approach employs function-preserving weight transformations and a dual learning procedure where both models jointly optimize toward a shared linear interpolation path.