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Machine Learning
The newest papers in Machine Learning that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 202
Harmful Partial Interventions and Benign Leakage in Concept-based Models
Artificial intelligence systems deployed in high-risk settings increasingly require models that support meaningful human oversight.DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents
Large Language Model agents operate through closed-loop action policies whose decision space is defined by an agent harness.Q($γ_H$): Entropy-Guided Horizon Mixing in Multi-Discount Q-Learning
A fixed discount factor in Q-learning imposes a single planning horizon throughout training, despite the differing demands of early exploration and long-term credit assignment.Same Spectrum, Harder First-Order Training: Phase-Locking in Single- versus Multi-Qubit Re-Uploading Circuits
Angle-encoded variational quantum circuits implement truncated Fourier series.Single-stage joint diffusion of sky images and clear-sky index for ultra-short-term irradiance forecasting: what the frame-to-irradiance stage discards
Sky-image irradiance forecasting is dominated by a two-stage design: a video-prediction model forecasts future sky frames, and a separately trained regressor maps those frames to irradiance.CIM-Embed: Learning Instruction-Conditioned Multimodal Embeddings for Clustering
Multimodal objects can require different partitions under different instructions, as when products in an e-commerce catalog are grouped by color, material, or both.N-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models
Conditional memory expands language-model capacity through selective lookup, but the full embedding table must remain available for every possible input.Measuring the local shape of learning-rate transfer curves
Understanding the learning-rate (LR) dependence of trained model performance is essential for better optimization of neural networks, but the appropriate size of the LR depends on learning dynamics…A Closed Form Depth-Width Law for Neural Collapse in Deep Linear Networks
How depth and width of neural networks shape learned representations is usually studied only after training.Compiling Multi-Threshold Operators for Quantized Non-Monotonic and Fine-Granular Activations in Custom-Tailored DNN Accelerators
Aggressive quantization of neural networks can expose activation functions as bottlenecks for efficient hardware acceleration.Local Epochs as Optimal Stopping: Theory and Evidence in Federated Learning
How long should each client train before communicating?Physics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference
Grid foundation models (GridFMs) are graph transformers pretrained on a masked power-flow objective, and evaluate power flow orders of magnitude faster than an iterative solver.Temporal Periodic Image Registration with Implicit Neural Representations
Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks.Lethe: Link Inference Attacks For Evaluation of Edge Unlearning Methods
Graph Unlearning (GU) aims to remove the influence of specific training data from a Graph Neural Network without retraining anew.Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents
Risk-sensitive reinforcement learning (RL) addresses decision-making under uncertainty and adverse outcomes.Learning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs
Training language models for formal theorem proving requires large numbers of verifier calls.Graph Dirichlet Energy for Fitting-Free Bivariate Causal Inference
We propose the Graph Dirichlet Energy Causal Inference (GDECI) criterion for distinguishing cause from effect in bivariate settings.Self-Calibrated Subspace Robust PCA for Parameter-Free Removal of Mixed Noise and Line Stripes in Hyperspectral Images
Hyperspectral images (HSIs) are often corrupted by Gaussian noise, impulses and stripes whose intensities vary across bands.In-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model
Sequence models can generalise in fundamentally distinct ways: extrapolating structures seen in training, or using their prompt to adapt to novel tasks.The Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret
Many modern learning systems are used not to select a single best action, but to sample diverse high-quality outputs.