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Discover cutting-edge research papers in AI and machine learning. Stay ahead with the latest breakthroughs, insights, and discoveries from top researchers worldwide.

21,056 Research Papers
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arXivJan 18, 2026

Statistical-Neural Interaction Networks for Interpretable Mixed-Type Data Imputation

Ou Deng, Shoji Nishimura et al.

TLDR: The Statistical-Neural Interaction (SNI) framework offers an interpretable method for mixed-type data imputation by integrating statistical priors with neural attention, balancing accuracy and interpretability.

09,549
ArXivJan 16, 2026

Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems

Zixu Wang, Bingbing Xu et al.

TLDR: Query-level workflow generation in Multi-Agent Systems may not be necessary, as task-level workflows can be equally effective and more efficient using the proposed SCALE framework.

016,094
arXivJan 16, 2026

Building Production-Ready Probes For Gemini

János Kramár, Joshua Engels et al.

TLDR: New probe architectures improve misuse mitigation for language models like Gemini by handling long-context inputs and adapting to distribution shifts, enhancing safety and efficiency.

014,838
ArXivJan 16, 2026

IDDR-NGP: Incorporating Detectors for Distractor Removal with Instant Neural Radiance Field

Xianliang Huang, Jiajie Gou et al.

TLDR: IDDR-NGP is a novel method that efficiently removes a variety of 3D scene distractors using a combination of 3D representations and 2D detectors, outperforming existing solutions in versatility and robustness.

015,671
ArXivJan 16, 2026

Combating Spurious Correlations in Graph Interpretability via Self-Reflection

Kecheng Cai, Chenyang Xu et al.

TLDR: The paper proposes a self-reflection framework to improve graph interpretability by reducing spurious correlations, specifically targeting the challenging Spurious-Motif benchmark datasets.

06,687
arXivJan 16, 2026

Do explanations generalize across large reasoning models?

Koyena Pal, David Bau et al.

TLDR: The study finds that explanations from large reasoning models (LRMs) often generalize across different models, enhancing consistency and aligning with human preferences.

014,528
ArXivJan 16, 2026

Temporal Complexity and Self-Organization in an Exponential Dense Associative Memory Model

Marco Cafiso, Paolo Paradisi

TLDR: The study explores the self-organizing dynamics and temporal complexity of a stochastic exponential dense associative memory model, showing how noise intensity and memory load influence these behaviors.

017,031
ArXivJan 16, 2026

AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing

Zhenhua Xu, Dongsheng Chen et al.

TLDR: AdaMARP is an adaptive multi-agent role-playing framework that improves immersion and adaptability in interactive narratives by integrating dynamic scene management and character interactions.

015,250
ArXivJan 16, 2026

PubMed-OCR: PMC Open Access OCR Annotations

Hunter Heidenreich, Yosheb Getachew et al.

TLDR: PubMed-OCR is a large, annotated corpus of scientific articles from PubMed Central, designed to support OCR-related research and development.

010,855
ArXivJan 16, 2026

AJAR: Adaptive Jailbreak Architecture for Red-teaming

Yipu Dou, Wang Yang

TLDR: AJAR is a new framework for testing AI safety by simulating complex attacks on autonomous language models, bridging gaps in current red-teaming approaches.

016,512
ArXivJan 16, 2026

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development

Jie Yang, Honglin Guo et al.

TLDR: ABC-Bench is a new benchmark designed to evaluate the ability of AI models to handle real-world backend development tasks, revealing that current models struggle with these comprehensive challenges.

012,830
ArXivJan 16, 2026

Soft Bayesian Context Tree Models for Real-Valued Time Series

Shota Saito, Yuta Nakahara et al.

TLDR: The Soft-BCT model introduces a probabilistic approach to context tree models for real-valued time series, showing competitive performance with existing models.

014,730
ArXivJan 16, 2026

Backdoor Attacks on Multi-modal Contrastive Learning

Simi D Kuniyilh, Rita Machacy

TLDR: This paper reviews the vulnerabilities of contrastive learning to backdoor attacks and discusses potential defenses and future research directions.

014,452
ArXivJan 16, 2026

Steering Language Models Before They Speak: Logit-Level Interventions

Hyeseon An, Shinwoo Park et al.

TLDR: This paper introduces a new method for steering language models using logit-level interventions, which improves control over generated text without requiring model retraining or deep access to internal layers.

015,414
ArXivJan 16, 2026

Self-learned representation-guided latent diffusion model for breast cancer classification in deep ultraviolet whole surface images

Pouya Afshin, David Helminiak et al.

TLDR: A self-supervised learning approach using a latent diffusion model significantly improves breast cancer classification accuracy in deep ultraviolet images by generating high-quality synthetic training data.

014,877
ArXivJan 16, 2026

Membership Inference on LLMs in the Wild

Jiatong Yi, Yanyang Li

TLDR: SimMIA is a new framework for membership inference attacks on large language models that excels in black-box settings using only generated text, achieving state-of-the-art results.

014,828
arXivJan 16, 2026

Relational Linearity is a Predictor of Hallucinations

Yuetian Lu, Yihong Liu et al.

TLDR: Relational linearity in language models is strongly correlated with hallucination rates, suggesting that how models store relational data affects their ability to self-assess knowledge accuracy.

015,944
arXivJan 16, 2026

MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management

Miriam K. Wolff, Peter Calhoun et al.

TLDR: MetaboNet is a large, unified dataset for Type 1 Diabetes research, consolidating multiple datasets to improve algorithm development and accessibility.

015,460
ArXivJan 16, 2026

Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction

Van Thuy Hoang, O-Joun Lee

TLDR: CaMol, a context-aware graph causality inference framework, improves few-shot molecular property prediction by leveraging causal substructures and chemical knowledge.

013,280
ArXivJan 16, 2026

When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs

Zhongxiang Sun, Yi Zhan et al.

TLDR: This paper identifies and addresses the issue of personalized language models generating incorrect answers due to personalization, proposing a solution to maintain factual accuracy while preserving personalization.

015,765
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