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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 211
Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving
This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, including: Fitted Value Iteration (FVI), Rollout…Learning to Select Features with Manifold Deep Unfolding PCA
Unsupervised feature selection (UFS) is of great significance in image processing and pattern recognition.Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents
While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantial token consumption and inference latency…Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent
Random reshuffling stochastic gradient descent (RR-SGD) is widely used for finite-sum optimization in machine learning.MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces
Expected-reward maximization in reinforcement learning concentrates probability mass on a few outcomes, even in tasks that require many distinct, high-quality solutions, a failure known as mode…State-Matched Sequence Conformal Prediction
Sequential conformal prediction needs to decide which past errors should calibrate uncertainty for the current target.Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators
Digital-domain self-interference cancellation is essential in in-band full-duplex radios after passive suppression and analog cancellation.What You Sign Is Not What You Run: The Verifiability Gap in Quantized LLM Supply Chains
Model signing authenticates the full-precision weights W an LLM is released with, yet users run a quantized artifact Q whose producing transformation is not uniquely determined under current…SKG: A Law-Induced Engine from Inference to Induction
This paper proposes the SKG (Symplectic-Kernel Gradient) reasoning engine for law-induced inference and induction.CktHGVAE: A Symmetric Design/State Heterogeneous Graph Variational Autoencoder for Transistor-level Analog/Mixed-signal Circuit Design and Analysis
We propose CktHGVAE, a heterogeneous graph variational autoencoder for transistor-level analog/mixedsignal CMOS circuits that takes not only design variables but also state variables as its inputs…High-Probability Regret Bounds for Feel-Good Thompson Sampling
Feel-good Thompson sampling (FGTS) augments the posterior of vanilla Thompson sampling with an exploration term that favors reward functions with high predicted optimal values.HankelKANet: Lightweight Path Loss Estimation via Physics-Inspired Hankel Convolution
Accurate path loss (PL) estimation is essential for wireless network planning and optimization.Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following
Verifiable instruction-following benchmarks emphasize output-constraint satisfaction but can overlook whether responses meaningfully answer the underlying request, creating an evaluation blind spot.A Unified Response Framework for Questions and Tutoring Dialogues
Existing student models in educational applications largely focus on student responses to questions.Typed Decision Models on Tabular Data
Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead of generating text.Two-stage Residual Learning for Modelling Strength-Informed Optimisation of Thermal Systems
Aggregate performance metrics do not identify local modelling inaccuracies in machine learning (ML) surrogates used for thermal-system optimisation.Noisy-Label Calibration with Multiple Unreliable Labels
In medical annotation and crowdsourcing, multiple annotators assign labels to each example, but these labels may be incorrect.Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction
Foundation models for dynamical systems can forecast unseen partial differential equations (PDEs), ordinary differential equations (ODEs), and time series during inference.Evidence-Anchored Overdose Death Prediction from Death Investigation Reports
Confirmation of overdose deaths in the United States is often delayed due to the complexities of certifying drug-involved deaths.TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series
Contrastive learning for time series depends on deciding which pairs of inputs should be treated as similar and dissimilar.