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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 173
VampPrior Unleashed
Deep latent variable models (DLVMs) underpin many modern analysis pipelines, for example in single-cell genomics, yet their prior distributions have received markedly little attention.EMBER: Predicting TCR--pMHC Binding Without a Docked Complex
We propose EMBER, a structure-based framework for TCR--pMHC binding prediction that eliminates the need for a predicted docked complex, and with it the placement error that dominates such predictions.Automated Brick Schema Knowledge Graph Completion Across Buildings
The Brick schema represents building equipment, sensors, spaces, and the relationships between them as typed, multi-relational graphs, providing machine-readable metadata that supports generalizable…Robust Lightweight IoT Device Fingerprinting Under Network Traffic Anomalies
Accurate IoT device identification is essential for network security, yet many existing approaches rely on payload inspection, feature engineering, or long observation windows that limit scalability…LiBRA: Logit Fingerprints for Byzantine-Robust Client Selection in Federated Learning
Byzantine-robust aggregation shields federated learning from malicious updates, but the rules that filter out suspect updates do so by comparing clients' parameter vectors.Predictive Closure: A Set-Level Characterization of JEPA's Feature Selection
In joint-embedding predictive architectures (JEPA), an encoder and a predictor are trained together to produce a representation that minimizes the error in predicting the representation of the next…Trail-Weighted Influence Scores for Approximate Outcome Prediction in Bayesian Networks
We study trail-weighted pruning of evidence for Bayesian-network prediction.Shaped SAEs: Expanding the Toolkit for Mechanistic Interpretability
Sparse autoencoders (SAEs) seek to decompose the dense activations of large language models (LLMs) into sparse, potentially interpretable features using a learned overcomplete dictionary.DStill: Trajectory Consistency Distillation for Accelerated One-Step Drafting in Block Diffusion Speculative Decoding
Block-diffusion speculative decoding accelerates large language model inference by drafting multiple tokens in parallel.Sample-efficient learning with Dense Associative Memories
We analytically study the evolution of the energy function of Dense Associative Memories (DenseAMs) as they train on randomly sampled codewords of random expander low-density parity-check codes…Training Controllable World Models via Action Influence Maximization
Action-conditioned video generative models trained on successful, goal-directed demonstrations tend to ignore the commanded action, rendering a plausible expert-like future regardless of what is…Fixed-Size Active Statistical Inference
Variance reduction techniques improve the efficiency of drawing conclusions from data.Communication Complexity of Universal Features
Motivated by the notion of features from machine learning, we study the complexity of representing data that allows for evaluation with a given family of functions.Steering intonational tunes in text-to-speech synthesis
Text-to-speech systems built on residual vector quantization often generate audio in two stages: a transformer backbone emits the first level of each frame's token stack, and a smaller depth model…Detecting AI Training in Datacentre Power
AI governance agreements require methods to detect large model training runs on a monitored computing cluster, without trusting the operator.DF-PALM: Dynamic Fusion Prompt Learning for Audio--Language Models
Few-shot audio classification requires audio--language models to distinguish variable acoustic events from limited labeled data.Means-Ends Analysis Helps LLMs Plan in Continuous-State Domains
LLM planning research has traditionally focused on discrete-state domains, where actions form a finite, enumerable set.TrustAgent-RFF: Agentic Diagnostic Planning for Reliable RF Fingerprinting under Domain Shift
Radio frequency fingerprinting (RFF) models can achieve accuracy under source domain conditions but become unreliable when the receiver, distance, collection day, or environment changes.MLFont: Machine Learning-Driven Font Subsetting and Range-Compact Unicode-Range Slicing for Low-Latency Web Typography
CSS3 @font-face rules with unicode-range descriptors partition web fonts into downloadable slices.T-MUON: Tensorizing Muon via t-SVD for Large Language Model Pre-training
Muon has recently emerged as a strong optimizer for large language model pretraining by exploiting matrix structure through polar orthogonalization.