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Artificial Intelligence
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The newest 5,000 papers, from 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 OpenAlex records.
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Newest papers, page 211
Agentic-Priors: Using Large Language Models to Address the Cold Start Problem in Preference Elicitation
An agent that learns a user's preferences from their choices faces a cold start problem: before any choices are observed, it must rely solely on a prior.Following Delegation Traces to Study Human Oversight in Interaction with Multimodal AI
As multimodal AI systems are becoming our everyday thought partners, the shift towards studying how users transfer control to these systems becomes especially urgent.Why Shallow Neural Networks Learn Sinusoidal Components First: An Orthogonal Polynomial Dynamics Framework
Neural networks often learn low-frequency components before high-frequency ones --- a phenomenon called spectral bias or termed F-principle.Time to Fake It: Continual Machine Learning Intensifies Unfairness in Deepfake Detection
Deepfake detectors safeguard against misinformation, rendering their failures as much a societal as a technical concern.OntoAct: Hypothesis-Based Neuro-Symbolic Agents through Ontological Abstraction
Learning from experience requires recovering reusable knowledge and determining when it applies to a new situation.GraphBEAT: Graph-based Bucketized Event Aggregation Over Time for Early Risk Stratification of Gastrointestinal Cancers
Longitudinal electronic health records (EHRs) contain complementary diagnostic and laboratory information about patient health.CARA: Causally Guided Retrieval Agent for Question Answering
Retrieval-augmented generation over text-rich knowledge graphs requires identifying evidence that contributes to answering a question, rather than merely matching its textual content.Priming Base Models for Agentic Post-Training: An Open Midtraining Recipe
Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been published about how this stage is built.DemoStream: Batched Task-and-Motion-Plan Optimization for Automated Learning from Demonstration
Learning from demonstration is an effective strategy for endowing robots with manipulation capabilities but requires training on datasets comprised of massive amounts of demonstrations.Selection Is Not Learning: Choosing Model Futures
Self-evolving language models train on tasks they generate, so every round must decide which updated model the next round inherits.GraphACM: Graph-based Action Consistency Measurement for Uncertainty Quantification in LLM Agents
Behavioral consistency across repeated executions provides an informative signal for assessing the reliability of large language model (LLM) agents.Momentum, Commentary Tone, and Earnings Surprise in LLM Financial Forecasts
Which inputs drive a language model's financial forecast?Decoding Naturalistic Speech from fMRI with Temporal Modeling and Multimodal Alignment
Reconstructing what a person perceives from their brain activity is a stringent test of whether neural recordings carry meaningful information.Progress Reward Model for Reinforcement Learning via Large Language Models
Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards.FAOPs: Normal Forms and an Algebra for AI Agent Workflows
Organizations increasingly encode operational procedures as multi-step agent skills, here called Agent Operating Procedures (AOPs), that must be traceable, auditable, reproducible…Prism-1: Position- and Rival-Independent Scoring for System One Models
Given a state and a set of options, such as intents, tools or interface elements, System One decision models return a distribution over the options in one forward pass, without generated reasoning.When Reasoning Is Not Enough: Proactive Inquiry for Cross-Modal Safety in Multimodal Large Language Models
Multimodal large language models (MLLMs) can act unsafely when individually benign images and texts are interpreted jointly.Memory as Weights: Internalizing Long-Term History for Streaming Videos
Streaming vision-language models (VLMs) must continuously integrate information from the past while remaining responsive to current observations and user queries.Learning from Unverified Peers via Shared Residual Distillation
A language model can generate several solutions to the same problem, some leading to correct answers and others to incorrect ones.Multi-SFT: Permissively-Licensed Multilingual Instruction-Following
Supervised fine-tuning datasets remain overwhelmingly English-centric and commercially restricted, as their content is frequently generated by proprietary models whose terms of service forbid…