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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 134
Conservative Meta-Agent Search: Architecture Adaptation under Non-Stationarity
Automated agentic-system design is typically evaluated on static task suites with immediate scalar rewards.DIAL: Steerable Language Models with Distilled Instruction-Adherence Levels
When a user says "Keep it short", how short should the LLM's response be?Multi-Token Prediction and Alternative Tokenization Improve Learning Latent Multi-Hop Graph Search at Scale
Given an unseen graph, a start node, $s$, and an end node, $t$, can a language model generate the shortest path from $s$ to $t$?RheOPT: Few-Shot Non-Newtonian Flow Prediction via Newtonian Pretraining of RheOFormer
Despite advances in numerical methods, non-Newtonian flow simulations remain computationally expensive because velocity and stress must be advanced together through rate- and history-dependent…MoG-VLM: Efficient Video Anomaly Detection through Motion-Guided Vision-Language Models
Vision-Language Models (VLMs) have shown strong potential for video anomaly detection, but dense frame-level inference makes them impractical for real-time deployment.Evaluating the Applicability of LLM Inference Optimizations on Apple Silicon
Apple Silicon is among the most widely used platforms for local LLM inference, yet nearly all inference optimizations are designed and validated on server-class NVIDIA GPUs under batched serving…Training Small Language Models for Transferable Tool Selection
As LLM agents operate over corpora of more than ten thousand tools, selecting the right tool for a complex multi-step user instruction becomes a central bottleneck.SABLE: Scalable Affine Bayesian Latent-space Exploration
Complex objects such as engineering designs and proteins are often difficult to optimise because their design spaces exhibit high-dimensional structure and objective evaluations are expensive.Structure-preserving reduced-order models of thin panels in supersonic and hypersonic flow
We build structure-preserving reduced-order models for both challenge problems of the AePW-4 High-Speed Working Group.Piano2Pop: Expanding Piano Demos into Pop Productions Without Aligned Supervision
We introduce Piano2Pop, a music-expansion task that transforms piano MIDI into instrumental pop productions.Nonparametric autoregressive estimation of probability distributions with parallel gradient boosting
Nonparametric estimation of joint probability distributions is a core problem in statistics, which is notoriously challenging with high-dimensional data.Direct Inference on Admissible Sets (Diona)
We propose a new regression method named Direct Inference on Admissible Sets (Diona) that directly uses samples to estimate the state and its derivatives at a query point, along with their…LACToS: Local Anomaly Detection via Conditional Typicality of Superpixels
Local anomaly detection is the task of identifying spatially confined anomalous or out-of-distribution (OOD) regions within an otherwise in-distribution (ID) image.PYTHIA, a transferable machine learning framework for simultaneous defect and adsorption site optimization
Machine learning has permeated material optimization, providing high value.Same Luck, Different Choice: Twin-World Credit Assignment for Agentic Reinforcement Learning
Agentic reinforcement learning rewards a complete episode, yet the policy needs credit for every decision inside it.Shift-aware score transport for privacy auditing without retraining
Privacy auditing empirically assesses privacy leakage by using membership inference attacks to establish lower bounds on privacy loss.Fine-Grained Analysis of Catastrophic Forgetting in Continual Learning via Algorithmic Stability
Understanding catastrophic forgetting, in which learning new tasks degrades performance on previously learned tasks, remains a fundamental challenge in continual learning (CL).Diffusion Language Models for Perplexity-Aware Black-Box Attacks
\emph{Continuous-space} attacks find prompts that trigger malicious behaviors, such as overthinking, in black-box LLMs by sampling or optimizing over a low-dimensional Gaussian latent space and…When to Follow the Teacher: Advantage-Based On-Policy Distillation
Online Policy Distillation (OPD) often outperforms offline methods such as Supervised Fine-Tuning (SFT) by training on trajectories sampled directly from the student.On the Token Collapse Phenomenon in Textual Graph Soft Prompting
Recent methods for injecting graph structure into LLM prompts include graph soft prompting, in which a graph neural network (GNN) learns graph-specific tokens that capture relational information…