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Artificial Intelligence
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The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,998 arXiv preprints and 2 OpenAlex records.
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Newest papers, page 60
Using Machine Learning to Measure Self-Efficacy Belief Through Learners' Interaction Traces in Technology-Enhanced Learning
Self-efficacy beliefs play a critical role in learners' performance, academic choices, and, more broadly, in their future career trajectories.ReX-GG: a LLM Ensemble Pipeline for Relation-extraction and Graph Generation
Current LLM ensemble frameworks focus on multi-step setups with additional modules for answer ranking, often opting for token and span analysis rather than structured outputs, leading to heavyweight…Structure-Aware Skill Retrieval and Arbitration for Optimization Modeling
Large Language Models (LLMs) can turn natural-language optimization tasks into solver-checked answers, but current agents remain brittle under narrative rewrites and often store reusable experience…Observable-Specific Validity Tests for Scientific AI: Controlled Approximations and Intervention Response
An approximation can reproduce an energy while missing its response, or satisfy an error tolerance without being certified by a given bound.Sensorium: An Open-Source Multimodal Data Collection and Curation Library for Embodied Surgical AI
Embodied surgical AI requires reliable multimodal data, yet surgical data collection remains costly, difficult to repeat, and strongly dependent on the hardware setup.Optimizing Stochastic Policy Geometry for Few-Step Flow Reinforcement Learning
Few-step flow models generate samples through a handful of large denoising transitions.Aligning MLLMs with the Latent Structure of Human Cognition via Behavior-Derived Semantic Dimensions
Multimodal Large Language Models (MLLMs) have achieved impressive performance across a wide range of reasoning tasks, yet it remains unclear whether their internal representations and decision…Chronos-2: A foundation model for multivariate time series forecasting
Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and earth sciences.Prune Gradually, Adapt Sparsely: Efficient Pruning of Large Language Models at Extreme Sparsity
Weight pruning can reduce the storage and inference cost of large language models (LLMs), but preserving quality at high sparsity levels remains challenging.When Does Test-Time Search Help World Action Models? An Empirical Audit of Candidate Selection and Simulator Integrity
World-action models increasingly apply test-time scaling, spending extra inference computation on candidate selection and adaptive allocation to improve control.ID2Pattern: Pretraining Generalizable Identity Behavior Patterns for Transaction Risk Foundation Models
Large-scale behavioral pretraining offers a promising route to generalizable representations in online payment systems.DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrieval in Gated Linear Attention
Linear attention scales linearly with sequence length, but its finite-state memory can limit long-context retrieval.SecPO: Principled Adversarial Training for Prompt Injection Security
Recognized as a top threat to AI agents, prompt injection allows instructions from untrusted data (email, documents, websites) to manipulate the agent's operation.AutoAnalysis: An automated, self-updating meta-analysis of human-AI collaboration
As generative artificial intelligence (AI) becomes more capable and widely used, understanding when and how humans should work with these systems is becoming increasingly important.TradeWar: A Benchmark for Execution-Grounded Trade Negotiation with Adaptive LLM Agents
Multi-turn negotiation remains a challenging problem for large language model (LLM) agents.Know When the Equation Is Wrong: Structural Uncertainty for LLM-Guided Symbolic Regression under Extrapolation
Large language models (LLMs) can help discover mathematical equations from data, but most LLM-guided symbolic regression methods focus on a single best equation without quantifying uncertainty over…There Is No Free Loop: When Does Looping a Trained Model Pay for Its Inference Compute?
Looping re-applies a block of layers so that each token passes through the block more than once.Learning Clean Generative Processes from Noisy Measurements
We study how to learn a generative model of clean images using only noisy observations.Same Question, Different Answers: Auditing Persona-Dependent Variation in Six LLMs on Smoking-Cessation Advice
Hundreds of millions of people now ask large language model (LLM) chatbots for health advice, which raises a question that is simple to pose and hard to answer: does everyone get the same advice?Near-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness
We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $n$ components.