Machine learning news — deep learning, reinforcement learning, neural architecture search, diffusion models, and new ML frameworks and libraries.
Researchers developed SAMPA, a Whisper-based segmenter that transcribes Brazilian Portuguese speech while inserting markers for terminal prosodic boundaries. Fine-tuned on the NURC-SP dataset, SAMPA achieved competitive …
A new theoretical analysis of in-context search in large language models shows that when self-reflection reliably localizes early mistakes, sequential reasoning can yield exponential improvements over the base model, sol…
Researchers introduced TriRoute, a unified controller that jointly optimizes attention resolution, expert selection, and KV-cache bit-width for language models, achieving Pareto-dominant performance over independent meth…
Researchers propose D2PO, a framework that optimizes diffusion sampling policies by reformulating sampler optimization as a preference-based alignment problem using Direct Preference Optimization (DPO). D2PO introduces d…
Researchers propose STAGformer, a Spatio-Temporal Agent Graph Transformer for micro mobility demand forecasting in bike-sharing systems. The model achieves efficient global modeling with linear computational complexity b…
Researchers propose Task-Semantic Field Factorization (TSF), a large language model-guided framework that integrates variable semantics from process documents into time-series forecasting for industrial processes. TSF re…
Researchers propose ROAM, a framework that uses LLM reasoning to adapt frozen specialist models to new scenarios without retraining, reducing MAE by over 20% in industrial processes with minimal overhead.
Researchers found that formal robustness certificates for embedded neural-interface models can pass even when task accuracy collapses, with EEGNet classification accuracy dropping 25.7% under attack while the certificate…
A new audit of grokking representation metrics reveals that effective rank measurements at the grokking transition overstate converged values by 3-5x on MLPs and 1.3-1.5x on transformers, with compression lagging accurac…
Researchers developed a physics-guided machine learning framework that integrates domain knowledge into deep learning models to predict fuel density for wildfire management, outperforming purely data-driven approaches in…
Researchers developed a multimodal generative framework for structural and functional MRI features, evaluating VAEs, transformers, GANs, and diffusion models. Their proposed graph VAE (gMMVAE) outperformed other variants…
Researchers introduced Gimitest, an open-source framework for testing reinforcement learning policies across diverse environments and algorithms. The tool supports single- and multi-agent RL policies, aiming to improve r…
Researchers introduced CoFINN (Conservation Flux Informed Neural Networks), a physics-informed deep learning framework that embeds finite-volume conservation physics into CNN training for compressible flow prediction. Te…
Researchers introduced SpaR3D-MoE, a framework that enhances multimodal large language models with adaptive 3D spatial reasoning from sparse RGB inputs. It uses a spatiotemporal manifold sampling mechanism and a geometry…
Researchers introduced a method for extracting rail tracks from classified 3D point clouds using a fully convolutional recurrent neural network trained on synthetic data. The approach rasterizes points, applies the netwo…
Researchers at arXiv introduced Scrambled Edges, a counterfactual test that injects physically implausible edge cues into images, causing vision models to produce geometrically distorted depth predictions. The study foun…
Researchers from the GRAPES-3 collaboration at the Tata Institute of Fundamental Research have developed a machine learning pipeline to distinguish secondary muon and punch-through hadron tracks in the GRAPES-3 muon tele…
Machine learning has fundamentally reoriented scientific practice from inference toward prediction, according to a new analysis of 4.9 million publications and 255 ML techniques by Malena Méndez Isla et al. The study ide…
Researchers propose PRoVeFL, a federated learning framework that combines privacy preservation, Byzantine robustness, and verifiable aggregation using multi-key fully homomorphic encryption across multiple servers. The f…
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