Here's your daily roundup of the most relevant AI and ML news for September 26, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. AERIAL: Adversarial Evaluation of Robustness in Accuracy-Preserving Low-Precision EEG Decoders
arXiv:2609.30037v1 Announce Type: cross Abstract: Deployment-oriented compression is attractive for resource-constrained brain--computer interfaces (BCIs), but whether it changes adversarial vulnerability remains unclear. On BCI Competition IV-2a, we compare 32-bit floating-point (FP32) EEGNet a...
Source: arXiv - Machine Learning | 10 hours ago
2. Beyond Average Safety: Chance-Constrained LLM Fine-tuning
arXiv:2609.29960v1 Announce Type: new Abstract: Fine-tuning large language models on new objectives can improve helpfulness, instruction following, or domain-specific performance, but it can also induce regressions on safety-critical prompts. Existing safety-preserving fine-tuning methods typica...
Source: arXiv - Machine Learning | 10 hours ago
3. GridSFM: A Foundation Model for Solving AC Optimal Power Flow
arXiv:2609.30173v1 Announce Type: cross Abstract: We introduce GridSFM, a framework that combines a pretrained foundation model across grid topologies with physics-informed fine-tuning for solving AC Optimal Power Flow (AC-OPF) at scale. It is a $15$ million parameter physics-inspired graph neur...
Source: arXiv - Machine Learning | 10 hours ago
4. KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization
arXiv:2609.30059v1 Announce Type: cross Abstract: Deep learning inference and training performance depends critically on GPU kernel efficiency. Modern compilers such as PyTorch Inductor automatically generate GPU kernels from high-level model code, but frequently underperform expert-written impl...
Source: arXiv - Machine Learning | 10 hours ago
5. Physics and Data Driven Transformer-Mamba Framework for Flow Field
arXiv:2609.29087v1 Announce Type: new Abstract: While deep learning accelerates expensive partial differential equation solving in computational fluid dynamics (CFD), existing methods like PINNs and FNOs often struggle with generalization, noise robustness, and physical consistency. We introduce...
Source: arXiv - Machine Learning | 10 hours ago
6. RECLAIM: Can Agents Reproduce the Claims of Machine Learning Papers?
arXiv:2609.28850v1 Announce Type: cross Abstract: Reproducing a machine learning paper involves most research steps, from installing software and debugging to running experiments, work that AI agents increasingly do. We introduce RECLAIM, a benchmark of 100 NeurIPS 2025 papers that can be rebuil...
Source: arXiv - Machine Learning | 10 hours ago
7. Leakage-Safe Machine Learning for Hydrogen Embrittlement Detection in 316L Stainless Steel: A Region-Held-Out Evaluation of Texture and Deep Features in SEM Micrographs
arXiv:2609.28567v1 Announce Type: new Abstract: Scanning electron microscopy (SEM) is routinely used to characterize the microstructural changes caused by hydrogen embrittlement (HE) in structural steels. Machine learning can automate this characterization, but models are often evaluated using i...
Source: arXiv - Machine Learning | 10 hours ago
8. The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning
arXiv:2609.28782v1 Announce Type: new Abstract: In scientific machine learning, $\Delta$-learning trains models on residual errors relative to physical baselines, assuming that more accurate baselines with smaller residual scales inherently improve downstream performance. Here, we demonstrate th...
Source: arXiv - Machine Learning | 10 hours ago
About This Digest
This digest is automatically curated from leading AI and tech news sources, filtered for relevance to AI security and the ML ecosystem. Stories are scored and ranked based on their relevance to model security, supply chain safety, and the broader AI landscape.
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