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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 139
OmniSimulator: Aligning Small Language Models for Authentic Heterogeneous Behavior Modeling
Large language models (LLMs) have shown great potential as general-purpose user simulators for interactive systems.Generative Modeling via Nonlinear Schrödinger Bridges and Branching Diffusions
We propose a new generative modeling framework grounded in forward-backward partial differential equations (PDEs).RubricOpt: Self-Revision of Semantic Rubrics for Preference Optimization
A model's failures can reveal gaps in the behavioral criteria guiding its training.An Exact 93-Move Solution to a Nonlocal Crystal-Exchange Puzzle
We determine the minimum number of directional commands needed to solve alevel of Order of the Sinking Star, modeled as a crystal-exchangepuzzle on an 11*11 board.A Strong MoE Is All You Need for Process Supervision
Process reward models (PRMs) provide fine-grained supervision for multi-step reasoning, yet obtaining reliable process labels remains a central bottleneck.Parallel Thinking for Large Language Model with Path Diversity
Parallel thinking has emerged as a promising paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs) by exploring multiple reasoning paths simultaneously.Rethinking Agentic Model Orchestration for Time Series Forecasting
Time series foundation models (TSFMs) perform unevenly across forecasting tasks.Keeping Time vs. Understanding Time: Neither Alone Explains Temporal Reasoning in LLMs
Temporal reasoning is widely assumed to decompose into two modes: temporal arithmetic (date and time calculations), and temporal semantics (understanding temporal and causal relations).Mitigating Query Shortcuts in Chain-of-Thought with Context-Aware Decoding
While Chain-of-Thought (CoT) prompting improves the reasoning performance of large language models (LLMs), recent studies have shown that final answers do not always depend on the generated…Are Joint-Embedding Predictive Objectives Effective for Longitudinal EHRs?
Masked language modelling (MLM) is the dominant pretraining objective for transformer encoders applied to longitudinal electronic health records (EHRs).Block Diffusion for Longitudinal EHR: Accuracy, Efficiency, and Representation Trade-offs
Autoregressive (AR) models have proved effective for zero-shot prediction from longitudinal electronic health records, but impose strictly sequential generation on trajectories containing temporally…Stochastic Pruning of the DPO Computation Graph: 1.4x Faster Training and Lower Peak Memory
Direct preference optimization (DPO) incurs training costs in output scoring, sequence execution, and backpropagation, so reducing supervision alone does not necessarily save computation.The Reflexivity Threshold: A Phase Transition for Multi-Agent Performative Prediction
Many learning systems are performative: once deployed, their predictions change the data distribution on which future models are trained.Improved Training of Strong Stochastic Flow Maps
Stochastic flow maps promise few-step sampling of stochastic differential equations (SDEs) by learning to directly jump between times along the solution path, yet training these models to learn this…Overview of CHIP 2026 Shared Task 1: Patient-Specific Phenotype Concept Recognition in Full-Text Biomedical Literature
Automatic phenotype information extraction plays an important role in rare disease diagnosis, genotype-phenotype analysis, and precision medicine.An interpretable deep learning method for early hepatocellular carcinoma diagnosis on multiphase CT
Early diagnosis of hepatocellular carcinoma is critical to preserve access to curative treatment options.FogTTA: Online Test-Time Adaptation for Robust Transformer-based Object Detection in Foggy Weather
Object detection models for autonomous driving commonly experience substantial performance drops when deployed under adverse weather due to the domain shift between training data and real-world…Formalizing Cooperation and Competition: An AI-Assisted Mathematical Addendum to a 2018 Planning Thesis
We present an AI-assisted mathematical addendum to Strategic Trip Planning: Striking a Balance Between Competition and Cooperation (Amar, 2018).From Association to Decision Value: Testing Paired Instruction Dependence for VLA Corpus Selection
Choosing among robot demonstration corpora is a ranking problem: an upstream diagnostic must preserve which candidates produce better downstream policies, not merely correlate with average…Lineage-Aware Search Control for LLM-Based Evolutionary Factor Mining
Formulaic alpha factor mining searches for symbolic expressions that transform market data into predictive signals.