AI & Machine Learning · Computer Science & AI
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 69
IMRouter: Imbalance-aware Temporal Pattern Routing for LLM-based Time Series Forecasting
Accurate time series forecasting (TSF) is essential for Web applications, such as traffic monitoring and online resource management.Strongly Adaptive Online Learning Achieves Uniform Discounted Regret
Discounted regret measures the performance of an online learner by assigning exponentially decaying weights to past losses, reflecting the greater importance of recent losses in non-stationary…Unlocking Any-Order Generation in Pretrained Autoregressive Image Models
Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fixed raster-scan order hardcodes a single factorization of the image distribution into the…There Is No Free Lunch in RLVR: Every pass@1 Gain Has a pass@k Price
Reinforcement learning with verifiable rewards (RLVR) makes a language model solve more problems on the first try and fewer when given many tries: it raises pass@1 while the base model overtakes it…Multi-Solution Inverse Design of Pixelated Patch Antennas Using an Autoregressive Transformer
Deep-learning-based inverse design has emerged as a promising framework for pixelated patch antenna synthesis by enabling broader exploration of discrete geometry spaces and reducing design time…Learning to Bound: Monotonic, Calibrated Neural Upper Bounds for MPE Inference
Probabilistic graphical models (PGMs) compactly encode structured joint distributions, but Most Probable Explanation (MPE), which finds the highest-probability assignment to unobserved variables…TaxoMFM: A Taxonomy-Aware Foundation Model for the Human Gut Microbiome
Existing gut microbiome foundation models typically use flat taxon representations, limiting their ability to exploit known taxonomic hierarchies.BEVLight: Generalizable Traffic Signal Control from UAV Bird's-Eye Views
Learning-based traffic signal control (TSC) has achieved promising performance, but most methods rely on structured traffic states that must be estimated from sensor observations in real deployments.Satisficing Regret Minimization in Bandits with Knapsacks
We study satisficing regret minimization in bandits with knapsacks (BwK), where a learner sequentially selects randomized policies, i.e., distributions over arms, to obtain mean rewards above a…Class-aware Latent Attention Replay (CLAR): Class Incremental Hazard Diagnosis for Resilient AI in the Manufacturing Industrial Internet
Artificial Intelligence (AI) systems provide critical computation services in the Manufacturing Industrial Internet (MII), and their resilience depends on timely diagnosis of hazards across the…RetroChessBench: A Benchmark for Reconstructing and Proving Chess Histories
Frontier language models such as GPT 6 Astra have contributed to research mathematics, demonstrating substantial reasoning capabilities.MulClip: A table-free activation function with monotonic and folding regimes for reservoir computing on microcontrollers
This letter proposes MulClip, a lightweight activation function for reservoir computing (RC) on microcontrollers (MCUs).Semantic Mismapping in Generalized Category Discovery: Evaluation and Mitigation
Generalized Category Discovery (GCD) aims to perform old-category recognition and novel-category discovery in the same unlabeled set.More Is Better? Exploring Latent Correspondence in Federated Hypergraph Learning
Federated hypergraph learning with latent cross-client hyperedge correspondence poses fundamental identifiability and computational challenges, as the same local release can correspond to multiple…Safe Multi-Objective Reinforcement Learning: Zero Constraint Violation and Primal-Averaging Convergence
Many applications of multi-objective reinforcement learning (MORL) require policies that balance conflicting objectives while satisfying safety constraints.Dobb: Shard-Local Preconditioning in Block-Diagonal Eigenbases
Training language models whose parameters and optimizer state exceed a single node's memory requires sharding these tensors across devices.Accuracy is not compatibility: two Evo 2 checkpoints hinge on one FP8 matrix multiplication
FP8 formats specify how values are stored, not how products are summed; this gap can break checkpoints.LinQuant: Functional Invariances for Low-Precision Linear Recurrences
Linear recurrences are emerging as practical alternatives to softmax attention in large language models, appearing in both standalone and hybrid architectures.FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss
Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing methods cast pre-training as classification on discretized labels, discarding continuity in…Softmax Routing Dynamics in Mixture-of-Experts Models: Collapse and Recovery
Softmax routing in mixture-of-experts models can concentrate probability on a subset of experts, suppressing access to the others.