MatchFusion is a learnable module that performs explicit-implicit instance matching for spatio‑temporal multimodal autonomous driving. It initializes pairwise affinities with geometric similarity and category consistency, then refines associations using instance embeddings to guide a residual aggregation operator for adaptive information exchange. Experiments on nuScenes show that MatchFusion improves perception accuracy, reduces FLOPs by 55.3% and GPU memory usage by 39.3%, and adds only 3.7% of total perception latency.
By Xiaoyu Li, Jiajia Fu, Long Shi, Tianyu Du, Ruihang Li, Xian Wu, Lijun Zhao, Yingtao Zhang, Lining Sun, Ruifeng Li
COVER is a new video watermarking method that targets codec compression as its primary design goal. It embeds the watermark payload in the latent space of a frozen generative video autoencoder and recovers it by re‑encoding the received video into the same latent space. Using a differentiable codec surrogate bank, COVER achieves high bit accuracy across multiple codecs while keeping marked videos visually close to the originals.
By Yuxin Cao, Hao Yang, Ziqi Ding, Jie Hao, Wei Song
The paper introduces a framework for estimating peer effects using multivariate behavioral measures extracted from written text via a large language model (LLM). It demonstrates that LLM embeddings enhance out‑of‑sample recidivism prediction by up to 30% compared to pre‑entry covariates alone, and presents a novel instrumental variable estimator that is √N‑consistent for sparse networks with multidimensional latent homophily. By combining limited human annotations with LLM zero‑shot vectors, the authors develop a prediction‑powered peer inference method that yields de‑biased estimates and reveals significant peer effects in behavioral profiles.
By Shanjukta Nath, Jiwon Hong, Jae Ho Chang, Keith Warren, Subhadeep Paul
BELXTR is a new biomedical entity linking model that uses a multi‑vector (late interaction) architecture to preserve token‑level matching information, unlike traditional embedding‑based approaches that compress mentions into a single vector. By extending the XTR model with a task‑specific training objective and active query expansion, BELXTR achieves state‑of‑the‑art performance on half of ten evaluated corpora, with an average 5‑percentage‑point gain in recall@1. The model shows especially strong results on cross‑species gene disambiguation, outperforming an LLM‑powered retrieve‑and‑rerank pipeline and approaching a specialized rule‑based system.
By Samuele Garda, Ulf Leser
The paper investigates why hierarchical image retrieval improves when using frozen DINOv2 features. It compares Euclidean and hyperbolic embeddings trained with taxonomy-distance regression or a taxonomy-aware supervised contrastive objective, finding that the choice of loss function (objective family) contributes more to hierarchy-aware performance than the geometry of the embedding space. Semantic alignment of the taxonomy also plays a significant role, while stronger negative curvature does not explain the gains.
By Ling Shi (Southeast University)
Annual Earth‑observation embeddings, specifically Tessera, can encode wildfire disturbance signals well enough to map burned areas without needing curated fire‑specific imagery or dense time‑series analysis. In tests, linear models using a single Tessera embedding matched or outperformed paired pre‑ and post‑fire HLS imagery and post‑fire imagery alone, achieving high F1 scores for burn‑scar delineation and regional mapping. The approach successfully mapped all same‑year fires in benchmark scenes, recovered 97% of California burned area without California training data, transferred to European fires with high accuracy, and even estimated ignition timing within a 13‑day error margin.
"whyItMatters":"The study demonstrates that pre‑trained annual embeddings can simplify and scale burned‑area mapping, reducing reliance on dense time‑series data and enabling more efficient wildfire monitoring."
By Jovana Knezevic, Clement Atzberger, Zhengpeng Feng, Adam F. A. Pellegrini, Srinivasan Keshav, David Coomes
arXiv:2609.23023v1 Announce Type: new
Abstract: Physics-informed neural networks (PINNs) require coordinated choices over network representation, sampling, loss construction, and optimization, while...
By Mingyang Yu, Xu Yang, Jun Zhang, Xiaolong Wang, Jing Xu, Keqian Li
arXiv:2609.22770v2 Announce Type: cross
Abstract: We study a parameterized hybrid ranker that fuses a dense embedding list and a sparse lexical list. The method has a small, explicit parameter vector...
By Satyanarayan Pati, Srikanth Patil
arXiv:2609.24688v1 Announce Type: cross
Abstract: Angular-margin speaker encoders are widely used in voice conversion, yet the geometry of their classifier prototypes remains poorly understood. We an...
By Mathilde Abrassart, Nicolas Obin, Axel Roebel
arXiv:2606.28798v2 Announce Type: replace
Abstract: The main objective of this paper is to propose a general framework for prediction based on different sources of multimodal data in the healthcare d...
By Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen
arXiv:2509.20702v3 Announce Type: replace-cross
Abstract: Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to...
By Hongqian Niu, Jordan Bryan, Jacob Williams, Hufeng Zhou, Zhun Deng, Haoyu Zhang, Xihao Li, Didong Li
arXiv:2609.25082v1 Announce Type: new
Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-fr...
By Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal, Georgios Kellaris, Joaquin del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria
arXiv:2609.25569v1 Announce Type: new
Abstract: Soccer tactics are interactive: an attacking action changes the opponent's defensive problem, and the observed response depends on the multi-agent matc...
By Abel A. Reyes-Angulo, Henry O. Velesaca, Steven Araujo
arXiv:2609.26270v1 Announce Type: cross
Abstract: We introduce the category $\mathbf{SSS}$ of sample-smooth spaces over a mixed site. The test objects are the products $\Omega_n := \mathbb{R}^n \time...
By Patrick Forr\'e
arXiv:2601.08358v2 Announce Type: replace
Abstract: Even though the ocean covers the majority of the planet's surface, it remains the least explored ecosystem. As light and radio waves do not propaga...
By Hilde I. Hummel, Sandjai Bhulai, Rob D. van der Mei, Burooj Ghani
arXiv:2602.02611v2 Announce Type: replace
Abstract: A prevailing paradigm in modern representation learning is the map-first approach, in which a representation map is learned from reconstruction, em...
By David Vigouroux (ANITI, IMT Atlantique - DSD, LaTIM), Lucas Drumetz (IMT Atlantique - MEE, Lab-STICC\_OSE, ODYSSEY), Ronan Fablet (IMT Atlantique - MEE, Lab-STICC\_OSE, ODYSSEY), Fran\c{c}ois Rousseau (IMT Atlantique - DSD, LaTIM)
arXiv:2605.00126v2 Announce Type: replace
Abstract: Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees, a critica...
By Arnaud Zinflou
arXiv:2603.29529v2 Announce Type: replace-cross
Abstract: Using a statistical mechanics framework, we investigate the parameter space of transformer models trained on protein sequence data. We sample...
By L. Ghiringhelli, A. Zambon, G. Tiana
arXiv:2605.28853v2 Announce Type: replace-cross
Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
By Rahul Fernandes, Travis Desell
arXiv:2609.26086v1 Announce Type: new
Abstract: An agentic retrieval system issues a sequence of search queries and must decide, at each step, whether the evidence collected so far is enough to stop....
By Daeyoung Roh, Donghee Han