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Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a different metric, but thereby introduce a Lipschitz constraint into the optimization problem. A simple ","authors":["Petzka, Henning","Fischer, Asja","Lukovnikov, Denis"],"url":"https://arxiv.org/abs/1709.08894","arxiv_id":"1709.08894","published":"2026-06-01"},{"title":"Advances and Challenges in Meta-Learning: A Technical Review","abstract":"Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expensive to obtain. The paper covers the state-of-the-art meta-learning approaches and explores the rela","authors":["Vettoruzzo, Anna","Bouguelia, Mohamed-Rafik","Vanschoren, Joaquin","Rögnvaldsson, Thorsteinn","Santosh, KC"],"url":"https://arxiv.org/abs/2307.04722","arxiv_id":"2307.04722","published":"2026-06-01"},{"title":"SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification","abstract":"Polarimetric synthetic aperture radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convoluti","authors":["Alkhatib, Mohammed Q.","Zitouni, M. 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Recently, LLMs have demonstrated unprecedented capabilities in language tasks a","authors":["Wang, Shijie","Huang, Jiani","Chen, Zhikai","Song, Yu","Tang, Wenzhuo","Mao, Haitao"],"url":"https://arxiv.org/abs/2404.14928","arxiv_id":"2404.14928","published":"2026-06-01"},{"title":"Forecasting with Hyper-Trees","abstract":"We introduce Hyper-Trees as a novel framework for modeling time series data using gradient boosted trees. Unlike conventional tree-based approaches that forecast time series directly, Hyper-Trees learn the parameters of a target time series model, such as ARIMA or Exponential Smoothing, as functions of features. These parameters are then used by the target model to generate the final forecasts. Our framework combines","authors":["März, Alexander","Rasul, Kashif"],"url":"https://arxiv.org/abs/2405.07836","arxiv_id":"2405.07836","published":"2026-06-01"},{"title":"Goldfish: Monolingual Language Models for 350 Languages","abstract":"For many low-resource languages, the only available language models are large multilingual models trained on many languages simultaneously. Despite state-of-the-art performance on reasoning tasks, we find that these models still struggle with basic grammatical text generation in many languages. First, large multilingual models perform worse than bigrams for many languages (e.g. 24% of languages in XGLM 4.5B; 43% in B","authors":["Chang, Tyler A.","Arnett, Catherine","Tu, Zhuowen","Bergen, Benjamin K."],"url":"https://arxiv.org/abs/2408.10441","arxiv_id":"2408.10441","published":"2026-06-01"},{"title":"LLM Bias Evaluation: Gender, Racial, and Age Disparities in Occupational and Crime Scenarios","abstract":"LLM bias evaluation is critical as large language models (LLMs) increasingly influence high-stakes decisions. This paper provides a comprehensive assessment of gender, racial, and age disparities in leading LLMs, revealing that debiasing efforts often create new fairness trade-offs. Recent advancements in LLMs have been notable, yet widespread enterprise adoption remains limited due to various constraints. This paper","authors":["Mirza, Vishal","Kulkarni, Rahul","Jadhav, Aakanksha"],"url":"https://arxiv.org/abs/2409.14583","arxiv_id":"2409.14583","published":"2026-06-01"},{"title":"Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning","abstract":"We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) (Pervez et al., 2024), we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. This sign","authors":["Chen, Jiale","Yao, Dingling","Pervez, Adeel","Alistarh, Dan","Locatello, Francesco"],"url":"https://arxiv.org/abs/2410.06074","arxiv_id":"2410.06074","published":"2026-06-01"},{"title":"Multimodal Fusion via Self-Consistent Task-Gradient Fields","abstract":"Multimodal learning aims to preserve as much task-related information as possible from different inputs. However, current fusion designs often distort the feedback loop to feature extractors. Aggressively merging modalities entangles their representations, making the feature extractors fragile to incomplete inputs. Meanwhile, attempting to separate features via auxiliary losses frequently introduces optimization conf","authors":["Xiong, Jiayu","Wang, Jing","Xue, Jun","Wang, Wanlong","Kwan, Jianlong","Lyu, Xiaosen"],"url":"https://arxiv.org/abs/2410.15475","arxiv_id":"2410.15475","published":"2026-06-01"},{"title":"Learning Coupled Subspaces for Multi-Condition Spike Data","abstract":"In neuroscience, numerous studies conduct sensory or behavioral experiments under multiple conditions to acquire neural responses in the form of high-dimensional spike train datasets. Analyzing high-dimensional spike data is a challenging statistical problem. To this end, Gaussian process factor analysis (GPFA), a popular class of latent variable models, has been proposed for data collected under a single experimenta","authors":["Nadew, Yididiya Y.","Fan, Xuhui","Quinn, Christopher J."],"url":"https://arxiv.org/abs/2410.19153","arxiv_id":"2410.19153","published":"2026-06-01"},{"title":"Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching","abstract":"Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unlike continuous flow matching, the rectification strategy cannot be applied due to the stochasticity of discrete paths, necessitating alternative methods to minimize state transitions. We propose a dynamic-optimal-transport-like minimization objective and derive its Kantor","authors":["Haxholli, Etrit","Gurbuz, Yeti Z.","Can, Ogul","Waxman, Eli"],"url":"https://arxiv.org/abs/2411.00759","arxiv_id":"2411.00759","published":"2026-06-01"},{"title":"Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems","abstract":"Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. The central challenge is to balance content exploration and exploitation while allowing users to adjust their recommendation preferences. Ideally, this balance can be captured with a hierarchical representation, where depth search facilitates exploitation and breadth search enables exploration. However, ex","authors":["Ma, Qiyao","Yang, Menglin","Ju, Mingxuan","Zhao, Tong","Shah, Neil","Ying, Rex"],"url":"https://arxiv.org/abs/2411.13865","arxiv_id":"2411.13865","published":"2026-06-01"},{"title":"Understanding the Fundamental Design Decisions of Retrieval-Augmented Generation Systems","abstract":"Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, practitioners face significant challenges when making RAG deployment decisions. While existing research prioritizes algorithmic innovations, a systematic gap persists in understanding fundamental engineering trade-offs that determine RAG success. We present the first comprehensive s","authors":["Zhao, Shengming","Shao, Yuchen","Huang, Yuheng","Song, Jiayang","Wang, Zhijie","Wan, Chengcheng"],"url":"https://arxiv.org/abs/2411.19463","arxiv_id":"2411.19463","published":"2026-06-01"},{"title":"Safeguarding Text-to-Image Generation via Inference-Time Prompt-Noise Optimization","abstract":"Text-to-Image (T2I) diffusion models are widely recognized for their ability to generate high-quality and diverse images based on text prompts. However, despite recent advances, these models are still prone to generating unsafe images containing sensitive or inappropriate content, which can be harmful to users. Current efforts to prevent inappropriate image generation for diffusion models are easy to bypass and vulne","authors":["Peng, Jiangweizhi","Tang, Zhiwei","Liu, Gaowen","Fleming, Charles","Hong, Mingyi"],"url":"https://arxiv.org/abs/2412.03876","arxiv_id":"2412.03876","published":"2026-06-01"},{"title":"Cross-Modal Attention Calibration for LVLM Hallucination Mitigation","abstract":"Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding. Despite their success, LVLMs still suffer from generating hallucinations in complex generation tasks, leading to inconsistencies between visual inputs and generated content. To address this issue, some approaches have introduced inference-time interventions, such as contrastive decoding, to reduce overreliance on","authors":["Li, Jiaming","Zhang, Jiacheng","Jie, Zequn","Ma, Lin","Li, Guanbin"],"url":"https://arxiv.org/abs/2501.01926","arxiv_id":"2501.01926","published":"2026-06-01"},{"title":"Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions","abstract":"The web-scale of pretraining data has created an important evaluation challenge: to disentangle linguistic competence on cases well-represented in pretraining data from generalization to out-of-domain language, specifically the dynamic, real-world instances less common in pretraining data. To this end, we construct a diagnostic evaluation to systematically assess natural language understanding in LLMs by leveraging C","authors":["Scivetti, Wesley","Torgbi, Melissa","Blodgett, Austin","Shichman, Mollie","Hudson, Taylor","Bonial, Claire"],"url":"https://arxiv.org/abs/2501.04661","arxiv_id":"2501.04661","published":"2026-06-01"},{"title":"On the Illusion of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes","abstract":"Face recognition systems (FRS) exhibit significant accuracy differences based on the user's gender. Since such a gender gap reduces the trustworthiness of FRS, more recent efforts have tried to find the causes. However, these studies make use of manually selected, correlated, and small-sized sets of facial features to support their claims. In this work, we analyze gender bias in face recognition by successfully e","authors":["Kurz, Paul Jonas","Wu, Haiyu","Al-Refai, Rouqaiah","Bowyer, Kevin W.","Terhörst, Philipp"],"url":"https://arxiv.org/abs/2501.12020","arxiv_id":"2501.12020","published":"2026-06-01"},{"title":"Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis","abstract":"Understanding climate dynamics requires going beyond correlations in observational data to uncover the underlying causal process. Latent drivers such as atmospheric processes play a central role in temporal dynamics, while direct causal influences also exist among geographically proximate observed variables. Traditional Causal Representation Learning (CRL) typically focuses on latent factors but overlooks such observ","authors":["Fu, Minghao","Huang, Biwei","Li, Zijian","Zheng, Yujia","Ng, Ignavier","Chen, Guangyi"],"url":"https://arxiv.org/abs/2501.12500","arxiv_id":"2501.12500","published":"2026-06-01"},{"title":"Unifying and Optimizing Data Values for Selection via Sequential Decision-Making","abstract":"Data selection has emerged as a crucial downstream application of data valuation, yet the theoretical foundations for using data values in selection remain underexplored. We reformulate data selection as a sequential decision-making problem where the optimal selection sequence arises from dynamic programming, and data values can be understood as encodings of this optimal sequence. This framework unifies and reinterpr","authors":["Chi, Hongliang","Wu, Qiong","Zhou, Zhengyi","Light, Jonathan","Dodwell, Emily","Ma, Yao"],"url":"https://arxiv.org/abs/2502.04554","arxiv_id":"2502.04554","published":"2026-06-01"},{"title":"ProofWala: A Framework for Multilingual Proof Data Synthesis and Theorem-Proving","abstract":"Neural approaches to theorem proving require robust infrastructure for interfacing with interactive theorem provers (ITPs), extracting structured proof data, and executing proof search at scale. However, existing tooling is often assistant-specific and oriented toward file-level execution, making repository-scale analysis and parallel experimentation challenging. We present ProofWala, a multilingual proof engineering","authors":["Thakur, Amitayush","Tsoukalas, George","Durrett, Greg","Chaudhuri, Swarat"],"url":"https://arxiv.org/abs/2502.04671","arxiv_id":"2502.04671","published":"2026-06-01"},{"title":"PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection","abstract":"Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to follow human instructions for real-world applications. However, the rapid growth of these datasets introduces significant redundancy, leading to increased computational costs. Existing methods for selecting instruction data aim to prune this redundancy, but predominantly rely on computationally demanding techniques such as proxy-","authors":["Bi, Jinhe","Aniri","Jin, Zengjie","Wang, Yifan","Yan, Danqi","Huang, Wenke"],"url":"https://arxiv.org/abs/2502.12119","arxiv_id":"2502.12119","published":"2026-06-01"},{"title":"MeMo: Towards Language Models with Associative Memory Mechanisms","abstract":"Memorization is a fundamental ability of Transformer-based Large Language Models, achieved through learning. In this paper, we propose a paradigm shift by designing an architecture to memorize text directly, bearing in mind the principle that memorization precedes learning. We introduce MeMo, a novel architecture for language modeling that explicitly memorizes sequences of tokens in layered associative memories. By d","authors":["Zanzotto, Fabio Massimo","Ruzzetti, Elena Sofia","Xompero, Giancarlo A.","Ranaldi, Leonardo","Venditti, Davide","Ranaldi, Federico"],"url":"https://arxiv.org/abs/2502.12851","arxiv_id":"2502.12851","published":"2026-06-01"},{"title":"Auto-Discovery-Bench: Diagnosing Structured State Tracking in Oracle-Guided Discovery","abstract":"Interactive discovery requires agents to maintain and update structured beliefs over many rounds of feedback. Before evaluating agents in noisy, open-ended scientific environments, it is useful to isolate this prerequisite capability under controlled conditions. We introduce Auto-Discovery-Bench, a deterministic oracle-guided diagnostic benchmark in which agents recover hidden structures through repeated hypothesis--","authors":["Chen, Tingting","Lin, Beibei","Anumasa, Srinivas","Shah, Vedant","Yuan, Zifeng","Zou, Qiran"],"url":"https://arxiv.org/abs/2502.15224","arxiv_id":"2502.15224","published":"2026-06-01"},{"title":"EMCEE: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context","abstract":"Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric training data leads to significant performance degradation in non-English languages. While existing multilingual prompting methods emphasize reformulating queries into English or enhancing reasoning capabilities, they often fail to incorporate the language- and culture-specific grou","authors":["Koo, Hamin","Kim, Jaehyung"],"url":"https://arxiv.org/abs/2503.05846","arxiv_id":"2503.05846","published":"2026-06-01"},{"title":"How does Bayesian Sampling help Membership Inference Attacks?","abstract":"Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Existing state-of-the-art attacks typically rely on training multiple reference models to approximate the conditional score distribution for individual data points, which leads to significant computational overhead and limits their practical applicability. In this work, we propose a novel appro","authors":["Liu, Zhenlong","Jiang, Wenyu","Zhou, Feng","Wei, Hongxin"],"url":"https://arxiv.org/abs/2503.07482","arxiv_id":"2503.07482","published":"2026-06-01"}],"history":{"events":[{"year":2018,"text":"For the first time since its transition to democracy, the Congress of Deputies in Spain passed a motion of no confidence against the government.","url":"https://en.wikipedia.org/wiki/Congress_of_Deputies"},{"year":2015,"text":"The river cruise ship Dongfang zhi Xing capsized in the Yangtze, resulting in 442 deaths in China's worst peacetime maritime disaster.","url":"https://en.wikipedia.org/wiki/Sinking_of_Dongfang_zhi_Xing"},{"year":2008,"text":"A fire on the back lot of Universal Studios Hollywood destroyed the King Kong Encounter and a large archive of music and film master recordings.","url":"https://en.wikipedia.org/wiki/2008_Universal_Studios_fire"},{"year":2007,"text":"Cyclone Gonu develops from an area of convection in the Arabian Sea, later becoming the worst recorded natural disaster in Oman.","url":"https://en.wikipedia.org/wiki/Cyclone_Gonu"},{"year":2001,"text":"A Hamas-affiliated Islamist militant blew himself up outside a nightclub in Tel Aviv, Israel, killing 21 people, most of whom were teenage girls.","url":"https://en.wikipedia.org/wiki/Hamas"},{"year":1999,"text":"On landing at Little Rock National Airport in the U.S. state of Arkansas, American Airlines Flight 1420 overran the runway and crashed (wreckage pictured), resulting in 11 deaths.","url":"https://en.wikipedia.org/wiki/Clinton_National_Airport"},{"year":1988,"text":"The Intermediate-Range Nuclear Forces Treaty was ratified, banning all American and Soviet land-based missiles with a range of 500 to 5,500 km (310 to 3,420 mi).","url":"https://en.wikipedia.org/wiki/Intermediate-Range_Nuclear_Forces_Treaty"},{"year":1988,"text":"Group representation constituencies were introduced to the parliament of Singapore.","url":"https://en.wikipedia.org/wiki/Group_representation_constituency"},{"year":1974,"text":"In an informal article in a medical journal, Henry Heimlich introduced the concept of abdominal thrusts, commonly known as the Heimlich maneuver, to treat victims of choking.","url":"https://en.wikipedia.org/wiki/Henry_Heimlich"},{"year":1943,"text":"Eight German Junkers Ju 88s shot down British Overseas Airways Corporation Flight 777 over the Bay of Biscay off the coast of Spain and France, killing actor Leslie Howard and several other notable passengers.","url":"https://en.wikipedia.org/wiki/Junkers_Ju_88"},{"year":1942,"text":"World War II: The crews of three Japanese submarines scuttled their vessels and committed suicide after entering Sydney Harbour and launching a failed attack.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1868,"text":"The Navajo and the U.S. government signed the Treaty of Bosque Redondo, which allowed those interned at Fort Sumner to return to their ancestral lands.","url":"https://en.wikipedia.org/wiki/Navajo"},{"year":1861,"text":"The first land battle of the American Civil War after Fort Sumter took place in the village of Fairfax, Virginia.","url":"https://en.wikipedia.org/wiki/Battle_of_Fairfax_Court_House_(1861)"},{"year":1857,"text":"The Revolution of the Ganhadores, the first general strike in Brazil, began in Salvador, Bahia.","url":"https://en.wikipedia.org/wiki/Revolution_of_the_Ganhadores"},{"year":1813,"text":"War of 1812: Mortally wounded, U.S. Navy captain James Lawrence ordered his crew \"Don't give up the ship!\" as USS Chesapeake was captured by HMS Shannon off the coast of Boston.","url":"https://en.wikipedia.org/wiki/War_of_1812"},{"year":1796,"text":"Tennessee was admitted as the 16th state of the United States.","url":"https://en.wikipedia.org/wiki/Tennessee"},{"year":1794,"text":"French Revolutionary Wars: A British fleet captured six French ships of the line in a naval battle off Ushant that came to be known as the Glorious First of June.","url":"https://en.wikipedia.org/wiki/French_Revolutionary_Wars"},{"year":1676,"text":"Scanian War: The Swedish warship Kronan, one of the largest ships in the world at the time, sank at the Battle of Öland (depicted) with the loss of around 800 men.","url":"https://en.wikipedia.org/wiki/Scanian_War"}],"births":[{"year":1999,"text":"Technoblade, American YouTuber and streamer (died 2022)","url":"https://en.wikipedia.org/wiki/Technoblade"},{"year":1999,"text":"Dmytro Udovychenko, Ukrainian violinist","url":"https://en.wikipedia.org/wiki/Dmytro_Udovychenko"},{"year":1996,"text":"Edvinas Gertmonas, Lithuanian footballer","url":"https://en.wikipedia.org/wiki/Edvinas_Gertmonas"},{"year":1996,"text":"Tom Holland, English actor","url":"https://en.wikipedia.org/wiki/Tom_Holland"},{"year":1994,"text":"Kagayaki Taishi, Japanese sumo wrestler","url":"https://en.wikipedia.org/wiki/Kagayaki_Taishi"},{"year":1991,"text":"Zazie Beetz, German-American actress","url":"https://en.wikipedia.org/wiki/Zazie_Beetz"},{"year":1991,"text":"Tyrone Roberts, Australian rugby league player","url":"https://en.wikipedia.org/wiki/Tyrone_Roberts"},{"year":1990,"text":"Miller Bolaños, Ecuadoran footballer","url":"https://en.wikipedia.org/wiki/Miller_Bola%C3%B1os"}],"deaths":[{"year":2025,"text":"Jonathan Joss, American actor (born 1965)","url":"https://en.wikipedia.org/wiki/Jonathan_Joss"},{"year":2024,"text":"Tin Oo, Burmese general and politician (born 1927)","url":"https://en.wikipedia.org/wiki/Tin_Oo"},{"year":2019,"text":"Ani Yudhoyono, Indonesian politician, 6th First Lady of Indonesia. (born 1952)","url":"https://en.wikipedia.org/wiki/Ani_Yudhoyono"},{"year":2015,"text":"Charles Kennedy, Scottish journalist and politician (born 1959)","url":"https://en.wikipedia.org/wiki/Charles_Kennedy"},{"year":2015,"text":"Joan Kirner, Australian educator and politician, 42nd Premier of Victoria (born 1938)","url":"https://en.wikipedia.org/wiki/Joan_Kirner"},{"year":2015,"text":"Nicholas Liverpool, Dominican lawyer and politician, 6th President of Dominica (born 1934)","url":"https://en.wikipedia.org/wiki/Nicholas_Liverpool"},{"year":2015,"text":"Jacques Parizeau, Canadian economist and politician, 26th Premier of Quebec (born 1930)","url":"https://en.wikipedia.org/wiki/Jacques_Parizeau"},{"year":2015,"text":"Jean Ritchie, American singer-songwriter (born 1922)","url":"https://en.wikipedia.org/wiki/Jean_Ritchie"}]},"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. Every item links to its source."}