TabJoinBench is a benchmark designed to evaluate join discovery methods across semantic, relational, and hybrid data lake scenarios. It constructs query‑candidate pairs using source‑specific validation strategies and introduces systematic perturbations—structural, representation, and semantic—while preserving reliable ground truth. The benchmark evaluates set‑based, feature‑based, learned, and language‑model embedding approaches, and publicly releases processed datasets, annotations, and a generation pipeline for reproducible research.
By Sandipan De, Jin Wang, Vivek Gupta
JoinGR is a join‑aware table retrieval method that treats a database’s join graph as the retrieval space. It represents columns as graph nodes and both intra‑table and foreign‑key relationships as typed edges, then selects anchor tables, traverses join edges with a query‑conditioned scorer, and aggregates edge deposits into table scores. Experiments on BIRD, Spider, and BEAVER show that JoinGR matches or surpasses strong retrieval baselines, especially improving recall on multi‑hop table requirements, and its learned scorer transfers across benchmarks, indicating reusable join‑graph traversal behavior.
By Sandipan De, Abhijit Chakraborty, Sambaran Bandyopadhyay, Vivek Gupta
Cog-VADU is a training‑free framework that transforms video anomaly detection into a sequential cognitive reasoning task. It uses Chain‑of‑Anomaly Detection Thought Prompting (CoADTP) to create a recurrent reasoning chain across video segments, preserving temporal memory and distinguishing complex anomalies from high‑motion normal activities. A cross‑modal re‑ranking stage aligns textual rationales with visual embeddings to enforce semantic consistency and temporal coherence, yielding competitive zero‑shot performance on multiple VAD benchmarks.
By Mohd Ubaid Wani, Sara Atito, Josef Kittler, Muhammad Awais
arXiv:2601.09879v2 Announce Type: replace-cross
Abstract: Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report gen...
By Yang Xing, Jiong Wu, Savas Ozdemir, Ying Zhang, Yang Yang, Wei Shao, Kuang Gong
arXiv:2602.20294v2 Announce Type: replace-cross
Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approa...
By Yu Li, Pranav Narayanan Venkit, Yada Pruksachatkun, Chien-Sheng Wu
arXiv:2609.38932v1 Announce Type: new
Abstract: Surface electromyography (sEMG) provides a wearable, camera-free signal for continuous hand-motion inference. Mapping muscle activity to joint kinemati...
By JiaCheng Ge, SiYu Zhang
arXiv:2609.39547v1 Announce Type: new
Abstract: GUI agents built on large language and vision-language models still struggle on unseen applications and complex multi-step tasks, as completing real GU...
By Bo Han, Qianyi Wang, Shuai Liu, Xiong Zifan, Changqiao Wu, Yuanfa Li, Pengzhi Gao, Wei Liu, Jian Luan, Heng Qu, Yunpeng Song, Zhongmin Cai
arXiv:2609.40335v1 Announce Type: new
Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is a leading approach for privacy-preserving fine-tuning of large language models (LLMs). M...
By Razan El Mais, Ali Chehab, Ibrahim Issa, Razane Tajeddine
arXiv:2609.38312v1 Announce Type: cross
Abstract: The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each...
By Michael J. Smith, Shashwat Sourav
arXiv:2609.38545v1 Announce Type: cross
Abstract: Machine-learning signals built from financial text treat what institutions say, and what the media repeat, as evidence about value. But whoever shape...
By Ali Atiah Alzahrani
arXiv:2609.39162v1 Announce Type: cross
Abstract: Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermed...
By Yehoshua Dissen, Joseph Keshet, Eduard Golshtein
arXiv:2508.12145v5 Announce Type: replace
Abstract: Recently, autoencoders (AEs) have gained interest for creating parametric and invertible projections of multidimensional data. Parametric projectio...
By Frederik L. Dennig, Daniel A. Keim
arXiv:2601.22028v2 Announce Type: replace
Abstract: Most LLM unlearning methods aim to approximate retrain-from-scratch behaviors with minimal distribution shift, often via alignment-style objectives...
By Haoran Tang, Rajiv Khanna
arXiv:2605.07938v2 Announce Type: replace
Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular funct...
By Sachini Weerasekara, Natasha Darras, Sagar Kamarthi, Colles Price, Jacqueline Isaacs
arXiv:2609.19011v2 Announce Type: replace
Abstract: Knowledge distillation can copy a deployed model by training a student on its logits or features. The student inherits a watermark only through the...
By Redwanul Karim, Tobias Feigl, Christopher Mutschler, Felix Ott
arXiv:2610.01385v1 Announce Type: cross
Abstract: This work aims to answer the question of whether it is possible to generate irreversible protected templates when the PolyProtect biometric template...
By Vedrana Krivoku\'ca Hahn, J\'er\'emy Maceiras, S\'ebastien Marcel
Build2SPARQL is a large-scale benchmark dataset for translating natural-language questions into SPARQL queries over building knowledge graphs. The dataset is generated by a KG‑grounded pipeline that produces 6,136 executable SPARQL queries and 30,680 corresponding natural-language questions across six query-pattern families and five vocabulary registers, covering 201 building KGs. Human validation shows high semantic fidelity, naturalness, and operational plausibility, and retrieval‑augmented evaluation demonstrates significant accuracy gains for open‑weight language models.
By Wooyoung Jung
NextMe-800 is an approximately 800‑hour first‑person video dataset collected from a single volunteer over 126 days, featuring 1 Hz images, gaze, and audio. The data are captioned at five hierarchical abstraction levels—from atomic actions to major activities—enabling personalized action anticipation as an open‑vocabulary K‑step sequence prediction task. The authors also introduce NextAct, a 1,500‑point benchmark that combines NextMe‑800 with the multi‑person EgoLife dataset, and evaluate models using an embedding‑based soft edit distance to assess how well personal behavior can be anticipated across abstraction levels and prediction horizons.
By Zhaoxu Meng, Yiming Sun, Mingyuan Gao, Jiachang Zhang, Zhuhan Dai, Yipeng Du, Zheng Lian, Jian-Qiao Zhu
ScholarCatalyst is a new benchmark that evaluates how well AI systems can retrieve research papers that inspire new work. The dataset was created by having 184 lead authors of 207 recent computer science papers annotate which earlier papers helped their projects, providing detailed rationales. The benchmark tests retrieval from the literature available at the start of a project, revealing that current agentic search and even advanced models like Claude Fable 5.1 perform only modestly better than simple embedding retrieval.
By Sohyeon Kim, Yoonho Lee, Bo Liu, Dayoon Ko, Rulin Shao, Seungone Kim, Graham Neubig, Pang Wei Koh, Aakanksha Chowdhery, Akari Asai, Omar Khattab, Yejin Choi, Gunhee Kim, Chelsea Finn
arXiv:2610.00604v1 Announce Type: cross
Abstract: Vision-language-action policies often see only one or a few recent frames, which makes it difficult to evaluate how they use information that disappe...
By Egor Cherepanov, Nikita Kachaev, Aleksandr I. Panov, Alexey K. Kovalev