Knowledge catalogue
arXiv:2507.12336v2 Announce Type: replace Abstract: Most existing 3D keypoint estimation methods rely on manual annotations or calibrated multi-view images, both of which are expensive to collect. Thi
arXiv:2606.06444v1 Announce Type: cross Abstract: Audio encoders are critical to modern audio applications as large language models (LLMs) increasingly rely on a single encoder for diverse inputs. Whi
arXiv:2603.14210v2 Announce Type: replace Abstract: We present Vavanagi, a community-run platform for Hula (Vula'a), an Austronesian language of Papua New Guinea with approximately 10,000 speakers. Va
arXiv:2606.06369v1 Announce Type: new Abstract: Learning-driven Scene Graph Generation (SGG) models excel on frequent relation types but degrade sharply under annotation sparsity, failing to capture r
arXiv:2606.05572v1 Announce Type: cross Abstract: We address the challenge of engineering distributed haptic displays capable of reproducing multiple localized, independently addressable vibrations --
arXiv:2406.12620v3 Announce Type: replace Abstract: Do architectural and training differences influence the way models represent and process language? Traditional similarity metrics tell us whether tw
arXiv:2606.05533v1 Announce Type: cross Abstract: Existing robot planning systems rely on appearance-based reasoning, where visual observations are encoded into latent spaces organized around object a
arXiv:2606.05626v1 Announce Type: new Abstract: Machine-generated text (MGT) attribution aims to identify the specific generator responsible for a given text, thereby providing fine-grained evidence f
arXiv:2606.06160v1 Announce Type: cross Abstract: RoPE-trained transformers distinguish absolute position in their attention patterns, even though RoPE encodes only relative offsets in the inner produ
arXiv:2606.06467v1 Announce Type: new Abstract: Long-context inference in modern LLMs is increasingly constrained by decoding efficiency, especially in reasoning-heavy settings where models generate l
arXiv:2606.04836v1 Announce Type: new Abstract: Accurate Autism Spectrum Disorder (ASD) screening for school-age children is crucial to identify cases that may have been missed earlier and to enable t
arXiv:2606.04828v1 Announce Type: new Abstract: This paper presents a French corpus annotated for multiword expressions (MWEs) with adverbial function. This corpus is designed for investigation on inf
arXiv:2606.04946v1 Announce Type: cross Abstract: Consistency is an important property in dynamic submodular maximization and entails maintaining a near-optimal solution at all times, making only a sm
arXiv:2512.06553v2 Announce Type: replace-cross Abstract: We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs). Our work is motivated b
arXiv:2606.04619v1 Announce Type: new Abstract: We propose MONIR, a Modalized-Output Normative Intermediate Representation for ASP-based compliance reasoning. Its core fragment has a staged operationa
arXiv:2606.04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert us
arXiv:2606.04877v1 Announce Type: cross Abstract: Proof assistants based on expressive logics suffer limited automation for proof search, raising the cost of formal verification based on proof assista
arXiv:2604.08438v2 Announce Type: replace Abstract: The Shapley value, and its broader family of semi-values, has received much attention in various attribution problems. A fundamental and long-standi
arXiv:2006.04013v6 Announce Type: cross Abstract: Artificial Intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to develop means to endow common people with
arXiv:2606.04980v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures scale model capacity through sparse expert activation, but their deployment remains memory-bound because all expe
arXiv:2606.04323v1 Announce Type: new Abstract: In this report, we present our solution for Track 2 of the CVPR 2026 VidLLMs Challenge. This track evaluates visual relational reasoning in videos, wher
arXiv:2511.18454v3 Announce Type: replace-cross Abstract: Embryo fragmentation is a morphological indicator critical for evaluating developmental potential in In Vitro Fertilization (IVF). However, ma
arXiv:2606.05121v1 Announce Type: cross Abstract: Audio is an inherently interactive modality, yet today's Large Audio Language Models (LALMs) are offline, and streaming audio models each handle only
arXiv:2512.15552v2 Announce Type: replace Abstract: A General Service List (GSL) is a commonly used resource for language learners to identify important English words. Traditional GSL creation is reso
arXiv:2312.08472v2 Announce Type: replace-cross Abstract: Transcendental functions, such as the exponential, are central to scientific computing, yet they cannot be natively calculated by digital hard
arXiv:2205.08609v3 Announce Type: replace-cross Abstract: Climate and environmental applications increasingly rely on high-dimensional prediction from remote sensing and other scientific data. Neural
arXiv:2606.04045v1 Announce Type: cross Abstract: Representation learning is often described as preserving the information in an input that is relevant for prediction. This work asks what relevance me
Gemini 3.1 Pro and Gemini 3-Pro lead visual reasoning benchmarks , with GPT-5.2, Kimi-K2.5, and GPT-5.2-Pro following . A 2026 evaluation benchmarked 15 leading multimodal models on visual reasoning a
arXiv:2603.03482v2 Announce Type: replace-cross Abstract: Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities. However, e
arXiv:2606.05161v1 Announce Type: cross Abstract: Audio-language models (ALMs) often follow text that conflicts with audio, even when the audio evidence is clear. This raises a basic question: is the
arXiv:2606.04648v1 Announce Type: new Abstract: Geometry problem solving poses distinct challenges in artificial intelligence. Existing approaches typically fall into two paradigms: symbolic methods,
arXiv:2606.05054v1 Announce Type: new Abstract: Self-consistency improves large language models by sampling multiple reasoning paths and selecting the most frequent answer, but majority voting often f
arXiv:2601.09719v3 Announce Type: replace-cross Abstract: Pre-Layer Normalization (Pre-LN) is the de facto choice for large language models (LLMs) and is crucial for stable pretraining and effective t
arXiv:2606.04165v1 Announce Type: cross Abstract: High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of mac
arXiv:2510.08647v2 Announce Type: replace-cross Abstract: Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during
arXiv:2606.04273v1 Announce Type: new Abstract: For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the valid
arXiv:2407.03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and
arXiv:2606.05011v1 Announce Type: new Abstract: Cross-view geo-localization estimates the geographic location of a ground image by matching it against an aerial image database. Existing methods tackle
arXiv:2606.04130v1 Announce Type: new Abstract: We introduce CLAW, a fully end-to-end self-supervised framework for learning a world model jointly with continuous latent action representations directl
arXiv:2606.04418v1 Announce Type: cross Abstract: Neural audio codecs are a key component of speech processing pipelines, compressing audio into discrete tokens for downstream modeling. However, exist
arXiv:2606.04952v1 Announce Type: cross Abstract: CARE-link is an open-source, web-based clinical support platform designed to improve the management of gestational diabetes by linking clinicians and
arXiv:2601.20800v3 Announce Type: replace-cross Abstract: We propose conditional PED-ANOVA (condPED-ANOVA), a principled framework for estimating hyperparameter importance (HPI) in conditional search
arXiv:2606.04695v1 Announce Type: new Abstract: High-dimensional optimal transport is seldom available in closed form. The one-dimensional case is exceptional because the order of the real line is com
arXiv:2510.01902v2 Announce Type: replace Abstract: Language Models (LMs) are increasingly used in applications where generated outputs must satisfy strict semantic or syntactic constraints. Existing
arXiv:2601.04493v3 Announce Type: replace Abstract: Continuum robots are flexible, slender manipulators well suited for confined surgical environments. In these settings, unknown interaction forces an
arXiv:2606.04733v1 Announce Type: new Abstract: Understanding activities of Internet scanners is challenging; it often requires identifying relationships between sources, a task for which semantic ann
arXiv:2606.05162v1 Announce Type: new Abstract: We introduce T2Mo, a feed-forward framework for controllable dynamic 3D shape generation conditioned on 3D trajectories and text. Due to the inherent am
arXiv:2606.04518v1 Announce Type: new Abstract: This paper proposes a cooperative target circumnavigation framework for multiple unmanned surface vehicles (USVs) operating without external localizatio
arXiv:2407.13922v3 Announce Type: replace-cross Abstract: Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations
arXiv:2606.04009v1 Announce Type: cross Abstract: Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-bas
arXiv:2606.04661v1 Announce Type: new Abstract: Prompts tuned for accuracy often grow long, raising inference cost on every model call. The best accuracy-cost trade-off depends on the task and the bud
arXiv:2606.04199v1 Announce Type: new Abstract: The increasing use of large language models has raised concerns about the spread of AI-generated fake news, particularly under varying prompting strateg
arXiv:2606.04446v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model
arXiv:2606.04928v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and dat
arXiv:2606.04710v1 Announce Type: new Abstract: This work presents a data-efficient variant of the Attention-Based Dual-Branch Complex Feature Fusion Network (CFFN) for hyperspectral image classificat
arXiv:2411.05591v2 Announce Type: replace-cross Abstract: We systematically study several network-based Expectation-Maximization (EM) algorithms for the Gaussian mixture model within decentralized fed
arXiv:2511.01192v2 Announce Type: replace Abstract: Detecting machine-generated text has become a critical challenge amid the rapid advancement of LLMs, yet existing detectors degrade severely under d
arXiv:2606.04987v1 Announce Type: cross Abstract: Multi-party dialogue is a critical setting for studying collaborative reasoning and decision-making, yet existing datasets rarely focus on structured,
arXiv:2606.05014v1 Announce Type: new Abstract: Self-attention selects information freely across the sequence, but across depth, Transformers merely add each layer's output to the residual stream, so
arXiv:2606.04279v1 Announce Type: new Abstract: Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from ref