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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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Using Transformers for Teaching and Learning via Symbolic Distillation of Neural A-life Agents
Neural networks controlling behavior of A-life agents can evolve over multiple generations.TriAttention: Unified Sparse Mixed-Precision Attention for Long-Context LLM Inference
Sparsity and quantization reduce the cost of attention by skipping interactions and lowering the precision of those retained.Evolutionary Adaptive Neighborhood Learning: A Genetic Algorithm for Instance-Dependent k Optimization in Nearest Neighbor Classification
The number of neighbours k is the central hyperparameter of the k-nearest neighbour (k-NN) classifier and is usually selected globally through cross-validation or evolutionary search.Reweighting is not Harmless: Understanding the Role of Dataset Characteristics
Reweighting is a widely used pre-processing method for promoting discrimination-free model behavior by assigning different weights to data points during model training.Energy Measurement and Optimization of TinyML in Resource-Constrained Embedded Systems: A Systematic Mapping Study
The rapid expansion of the Internet of Things (IoT) has increased the volume of data generated at the network edge, where many devices operate under severe memory, processing, and energy constraints.How Deep is the Signal? Time-Context up to 30 Years for Colorectal Cancer Prediction in a Primary-Care EHR Model
EHR foundation models read medical events within a fixed context window, and long context has so far been measured only in tokens.Satisficing as competitive Bayesian weighting: a minimal model of goal-directed choice under an unattained target
Animals and humans often do not maximize; they act relative to goals (aspiration levels) that are frequently not yet met.Graph Neural Networks for Resource Allocation in Integrated Sensing and Communication
In vehicular integrated sensing and communication (ISAC) networks, each service-provider vehicle uses a limited power budget to serve multiple sensing targets and communication receivers.Scenario Augmentation for Rare Financial Events via Multimodal Latent Diffusion
Extreme market movements are scarce, heterogeneous, and difficult to learn from imbalanced historical data.How do Transformers Perform Low-Level Control In-Context?
One of the remarkable abilities of Transformer-based neural networks is in-context learning.Provably Efficient Representation Learning for Low-Rank CMDPs
We study representation learning in low-rank Constrained Markov Decision Processes (CMDPs), where both value and reward functions are approximated by a set of unknown representation vectors, and the…WeightLoop: Weight-Space Programming with Experimental Feedback
Weight-space programming (WSP) shows that language models can reason about and rewrite neural network weights; recursive self-improvement (RSI) motivates feedback-driven improvement.Rethinking Data Selection: The Importance of Coverage over Difficulty in LLM Fine-Tuning
Selecting high-quality training data can reduce computation cost for LLM fine-tuning.Permanent and Transient Representations for Continual Reinforcement Learning
Continual Reinforcement Learning (CRL) agents struggle to adapt to new situations while retaining prior knowledge, leading to a stability-plasticity trade-off.Last-Iterate Convergence of Stochastic Gradient Methods for Unbounded Problems
Last-iterate convergence guarantees for stochastic gradient methods have attracted significant attention as practical implementations typically return the last iterate instead of an average of the…AtlasULP: Domain-aware Universal Link Prediction via Relation Atlas
Link prediction (LP) is a widely applied task in graph learning.From What Is Used to What Is Missing: Rethinking Dynamic Rank in LoRA
Parameter-efficient fine-tuning with LoRA is governed by rank, which controls parameter budget and locally accessible task updates.Acoustic Anomaly Detection for Early Identification of Drill Jamming
In the context of Industry 4.0, predictive maintenance is essential to minimize unplanned downtime and ensure operational safety.Asymptotic-Preserving Tensor Neural Networks for 3D High-Field Kinetic Equations with Applications to Uncertainty Quantification
Kinetic equations in the high-field regime describe particle transport dominated by electric-field acceleration and collisional relaxation.Test-Time Self-Play: Scaling without a Verifier
Test-time scaling methods spend additional compute at deployment to improve a large language model's performance on a hard target problem.