Knowledge catalogue
arXiv:2604.18919v1 Announce Type: new Abstract: Analyzing topics extracted from text data in relation to external outcomes is important across fields such as computational social science and organizat
arXiv:2508.20467v2 Announce Type: replace-cross Abstract: In the highly volatile and uncertain global financial markets, traditional quantitative trading models relying on statistical modeling or empi
arXiv:2510.10866v3 Announce Type: replace-cross Abstract: Measuring dataset similarity is fundamental in machine learning, particularly for transfer learning and domain adaptation. In the context of s
arXiv:2604.19172v1 Announce Type: new Abstract: The rapid advancement and widespread adoption of Large Language Models (LLMs) have elevated the need for reliable AI-generated content (AIGC) detection,
arXiv:2604.19144v1 Announce Type: new Abstract: Recent years have witnessed growing interest in applying Large Reasoning Models (LRMs) to Machine Translation (MT). Existing approaches predominantly ad
arXiv:2604.18893v1 Announce Type: cross Abstract: A series of high-profile tragedies involving companion chatbots has triggered an unusually rapid regulatory response. Several jurisdictions, including
arXiv:2604.19060v1 Announce Type: new Abstract: Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improv
arXiv:2509.13281v5 Announce Type: replace Abstract: Current safety evaluations of language models rely on benchmark-based assessments that may miss localized vulnerabilities. We present RepIt, a simpl
arXiv:2503.16251v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has gained prominence in machine learning applications across critical domains by enabling collaborative model trainin
arXiv:2604.19146v1 Announce Type: new Abstract: Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We present RLA
arXiv:2604.18835v1 Announce Type: cross Abstract: We propose a scalable, multifactorial experimental framework that systematically probes LLM sensitivity to subtle semantic changes in pairwise documen
arXiv:2604.19219v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL:
Karen Freifeld / Reuters: Sources: Micron is pushing the US Congress to pass the “MATCH Act”, which would put new export restrictions on equipment its Chinese rivals use to make chips — Micron Technol
arXiv:2604.19710v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising autonomous driving paradigm for leveraging world knowledge and reasoning capabilities, especially
arXiv:2601.02993v4 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in Large Language Models (LLMs), yet little is kn
arXiv:2604.18976v1 Announce Type: new Abstract: While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses. Thi
arXiv:2604.19295v1 Announce Type: new Abstract: Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the r
Terrible This is insane… The Virginia redistricting amendment on the ballot today is framed as a vote to 'restore fairness in the upcoming elections.' In reality, it turns a state that Kamala barely w
arXiv:2412.01763v3 Announce Type: replace-cross Abstract: We study a censored variant of the data-driven newsvendor problem, where the decision-maker must select an ordering quantity that minimizes ex
arXiv:2601.09926v3 Announce Type: replace Abstract: Current approaches to proactive assistance move beyond the ask-and-respond paradigm by anticipating user needs. In practice, they either burden user
arXiv:2604.19645v1 Announce Type: new Abstract: An earlier paper (Hong, Potteiger, and Zapata 2026) established that an unoptimized GPT 4.1 prompt predicts fan-reported experience ratings within one p
arXiv:2604.18850v1 Announce Type: cross Abstract: AI systems are increasingly embedded in multi-user social environments, yet most alignment frameworks conceptualize interaction as a dyadic relationsh
Gary Marcus expresses skepticism or concern about an artificial intelligence claim or incident that he finds implausible, suggesting that even young children would recognize the flaw in the reasoning
arXiv:2604.18967v1 Announce Type: new Abstract: Chest X-ray (CXR) radiology report generation (RRG) models have shown rapid progress, yet their clinical utility remains uncertain due to limited evalua
arXiv:2604.19070v1 Announce Type: new Abstract: Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure wi
arXiv:2602.12735v2 Announce Type: replace-cross Abstract: Effectively retrieving, reasoning, and understanding multimodal information remains a critical challenge for agentic systems. Traditional Retr
arXiv:2507.21563v4 Announce Type: replace-cross Abstract: Recommendation systems often suffer from data sparsity caused by limited user-item interactions, which degrade their performance and amplify p
Ads Advisor is an agentic conversational experience built with Gemini in Google Ads designed to help maximize performance based on business goals. The tool can help users get personalized answers, und
arXiv:2604.16446v1 Announce Type: new Abstract: Optical Music Recognition (OMR) aims to convert printed or handwritten music score images into editable symbolic representations. This paper presents an
arXiv:2604.17763v1 Announce Type: cross Abstract: This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with impro
arXiv:2511.22003v2 Announce Type: replace-cross Abstract: Limited overlap between treated and control groups is a key challenge in observational analysis. Standard approaches like trimming importance
arXiv:2509.24478v2 Announce Type: replace Abstract: Modern neural networks have greatly improved performance across speech recognition benchmarks. However, gains are often driven by frequent words wit
arXiv:2510.16458v2 Announce Type: replace Abstract: Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches a
arXiv:2604.17325v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated conte
arXiv:2604.16622v1 Announce Type: new Abstract: Backchannels (e.g., `yeah', `mhm', and `right') are short, non-interruptive feedback signals whose lexical form and prosody jointly convey pragmatic mea
arXiv:2604.18489v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise in lyric-to-melody generation, but models trained with Supervised Fine-Tuning (SFT) often produce musically
arXiv:2604.18130v1 Announce Type: new Abstract: Digital marketplaces processing billions of dollars annually represent critical infrastructure in sociotechnical ecosystems, yet their performance optim
arXiv:2507.20993v3 Announce Type: replace Abstract: We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These poli
arXiv:2603.29068v3 Announce Type: replace Abstract: This paper presents ARCS (Autoregressive Circuit Synthesis), a system for amortized analog circuit generation. ARCS produces complete, SPICE-simulat
arXiv:2601.13707v2 Announce Type: replace Abstract: Hallucinations in large vision--language models (LVLMs) often arise when language priors dominate over visual evidence, leading to object misidentif
arXiv:2604.18187v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have made significant progress in audio understanding, yet they primarily operate as perception-and-answer systems
arXiv:2510.15339v3 Announce Type: replace Abstract: Building effective knowledge graphs (KGs) for Retrieval-Augmented Generation (RAG) is pivotal for advancing question answering (QA) systems. However
arXiv:2604.18572v1 Announce Type: new Abstract: The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.g., text and images) align and eventually conver
arXiv:2604.16324v1 Announce Type: new Abstract: The activation memory required for exact backpropagation scales linearly with network depth, context length, and feature dimensionality, forming an O(L
arXiv:2502.10605v3 Announce Type: replace-cross Abstract: Estimating the causal effects of interventions is crucial to policy and decision-making, yet outcome data are often missing or subject to non-
arXiv:2604.16952v1 Announce Type: new Abstract: Learning robust representations across extremely heterogeneous modalities remains a fundamental challenge in multi-modal vision. As a critical and profo
arXiv:2604.17188v1 Announce Type: new Abstract: Multi-role dialogue summarization requires modeling complex interactions among multiple speakers while preserving role-specific information and factual
arXiv:2604.16884v1 Announce Type: new Abstract: The integration of medical imaging and clinical text has enabled the emergence of generalist artificial intelligence (AI) systems for healthcare. Howeve
arXiv:2604.18022v1 Announce Type: cross Abstract: The inverse Potts problem for estimating evolutionary single-site fields and pairwise couplings in homologous protein sequences from their single-site
arXiv:2604.18578v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) has become the predominant algorithm for on-policy reinforcement learning due to its scalability and empirical robust
arXiv:2602.23580v2 Announce Type: replace Abstract: In the field of educational assessment, automated scoring systems increasingly rely on deep learning and large language models (LLMs). However, thes
arXiv:2604.16680v1 Announce Type: new Abstract: We introduce C-GenReg, a training-free framework for 3D point cloud registration that leverages the complementary strengths of world-scale generative pr
arXiv:2604.17896v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map multimodal inputs directly to robot actions and are typically trained through large-scale imitation learning. Wh
arXiv:2604.17693v1 Announce Type: new Abstract: In cooperative teams where agents act in a fixed order and share a single team reward, it is hard to know how much each agent contributed, and harder st
arXiv:2604.16861v1 Announce Type: cross Abstract: Standard supervised learning optimizes for predictive accuracy but remains agnostic to the internal geometry of learned features, often yielding repre
arXiv:2604.16411v1 Announce Type: new Abstract: We study asynchronous alignment, a first-class multimodal learning setting in which a dense primary stream must be fused with sporadic external context
arXiv:2602.19577v2 Announce Type: replace Abstract: Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, an
arXiv:2601.05543v2 Announce Type: replace Abstract: Although Speech Large Language Models have achieved notable progress, a substantial modality reasoning gap remains: their reasoning performance on s
arXiv:2604.18236v1 Announce Type: new Abstract: In the context of robot learning for manipulation, curated datasets are an important resource for advancing the state of the art; however, available dat
arXiv:2603.09108v2 Announce Type: replace Abstract: Medical image retrieval aims to identify clinically relevant lesion cases to support diagnostic decision making, education, and quality control. In