Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv Machine Learning

Jun 5

Bridging the Semantic-Collaborative Gap: An Asymmetric Graph Architecture for Cold-Start Item Recommendation

arXiv:2606. 06225v1 Announce Type: cross Abstract: Collaborative filtering and graph-based recommendation models are highly effective because they leverage observed user interactions, but this dependence creates a fundamental cold-start challenge when newly added content has no interaction history.

By Anh Truong, John Trenkle, Yuanbo Chen, Honghong Zhao, Abdullah Alchihabi, Effy Fang, Michael Tamir

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arXiv Machine Learning

Jun 5

GridPE: A Grid Cell-Inspired Unified Position Embedding for Arbitrary-Dimensional Spaces

arXiv:2406. 07049v3 Announce Type: replace-cross Abstract: Understanding spatial relationships across all dimensions is fundamental for intelligent systems.

By Boyang Li, Yulin Wu, Nuoxian Huang, Wenjia Zhang

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arXiv Machine Learning

Jun 5

$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences

arXiv:2606. 06117v1 Announce Type: cross Abstract: We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis.

By Tirtharaj Dash, Gunja Sachdeva

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arXiv Machine Learning

Jun 5

MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry

arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.

By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han

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arXiv Machine Learning

Jun 5

Central Description Length (CDL) Clustering Validation Index

arXiv:2606. 05230v1 Announce Type: cross Abstract: Selecting a clustering algorithm and its hyperparameters without labels is a common difficulty in engineering machine learning pipelines that work with unsupervised analysis of sensor, image, or process data.

By Mahdi Shamsi, Soosan Beheshti

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arXiv Machine Learning

Jun 5

Generative Criticality in Large Language Model Temperature Scaling

arXiv:2606. 06238v1 Announce Type: new Abstract: We propose a statistical-field framework for text generated by large language models (LLMs), treating token embeddings as continuous spin variables on a one-dimensional chain.

By Huajian Ruan, Jinyang Li, Xingyu Guo, Lingxiao Wang

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arXiv Machine Learning

Jun 5

Generalized TV--$\ell_p$ Structured Priors for Bayesian $T_1$ Mapping

arXiv:2606. 05381v1 Announce Type: new Abstract: We propose an extended family of structured spatial priors that incorporates the total variation (TV) function with $\ell_p$ norms.

By Disi Lin, Martin Berggren, Tommy L\"ofstedt

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arXiv Machine Learning

Jun 5

DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables

arXiv:2606. 05247v1 Announce Type: new Abstract: Enforcing nonlinear inequality constraints in neural networks remains challenging, especially when the output is subject to many coupled constraints.

By Ziqian Wang, Chenxi Fang, Zhen Zhang

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arXiv Machine Learning

Jun 5

LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

arXiv:2603. 19312v3 Announce Type: replace Abstract: Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pre-trained encoders, or auxiliary supervision to avoid representation collapse.

By Lucas Maes, Quentin Le Lidec, Damien Scieur, Yann LeCun, Randall Balestriero

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arXiv Machine Learning

Jun 5

Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series

arXiv:2506. 00188v2 Announce Type: replace Abstract: Early and accurate detection of anomalies in time-series data is critical due to the substantial risks associated with false or missed detections.

By Md Mahmuddun Nabi Murad, Yasin Yilmaz

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arXiv Machine Learning

Jun 5

Geodesic Semantic Search: Cartographic Navigation of Citation Graphs with Learned Local Riemannian Maps

arXiv:2602. 23665v5 Announce Type: replace-cross Abstract: We present Geodesic Semantic Search (GSS), a retrieval system that learns node-specific Riemannian metrics on citation graphs to enable geometry-aware semantic search.

By Brandon Yee, Lucas Wang, Kundana Kommini

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arXiv Machine Learning

Jun 5

HyFAD: Hybrid Time-Frequency Diffusion with Frequency-Aware Embedding for Time Series Imputation

arXiv:2606. 05239v1 Announce Type: cross Abstract: Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising.

By Hongfan Gao, Wangmeng Shen, Bin Yang, Jilin Hu

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arXiv Machine Learning

Jun 5

Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution

arXiv:2505. 11766v4 Announce Type: replace Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces.

By Haoze Song, Zhihao Li, Xiaobo Zhang, Zecheng Gan, Zhilu Lai, Wei Wang

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arXiv Machine Learning

Jun 5

Two-Way Is Better Than One: Bidirectional Alignment with Cycle Consistency for Exemplar-Free Class-Incremental Learning

arXiv:2606. 05675v1 Announce Type: new Abstract: Continual learning (CL) seeks models that acquire new skills without erasing prior knowledge.

By Hongye Xu, Bartosz Krawczyk

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arXiv Machine Learning

Jun 5

Deciphering Two Training Clocks in Grokking via Deep Linear Network Theory with Conditional ReLU Reduction

arXiv:2606. 05863v1 Announce Type: new Abstract: Grokking suggests that fitting the training data and learning a simple underlying rule may occur on different time scales.

By Hu Tan, Kuo Gai, Shihua Zhang

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arXiv Machine Learning

Jun 5

HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery

arXiv:2606. 05587v1 Announce Type: cross Abstract: Multi-object tracking (MOT) from UAV imagery presents unique challenges: altitude varies across sequences, objects are small and densely packed, and frequent occlusion causes identity switches.

By Phillip Jiang

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arXiv Machine Learning

Jun 5

NIV: Neural Axis Variations for Variable Font Generation

arXiv:2606. 05261v1 Announce Type: cross Abstract: Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size.

By Nadav Benedek, Ariel Shamir, Ohad Fried

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arXiv Machine Learning

Jun 5

Expand Neurons, Not Parameters

arXiv:2510. 04500v3 Announce Type: replace Abstract: This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves performance.

By Linghao Kong, Inimai Subramanian, Yonadav Shavit, Micah Adler, Dan Alistarh, Nir Shavit

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arXiv Machine Learning

Jun 5

Anchor PCA

arXiv:2606. 06233v1 Announce Type: cross Abstract: Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques.

By Benedikt Seiter, Anya Fries, Julius von K\"ugelgen, Jonas Peters

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arXiv Machine Learning

Jun 5

Algebraic Diversity: Group-Theoretic Spectral Estimation from Single Observations

arXiv:2604. 03634v5 Announce Type: replace Abstract: We establish that temporal averaging over multiple observations is the degenerate case of algebraic group action with the trivial group $G=\{e\}$.

By Mitchell A. Thornton

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