Knowledge catalogue
arXiv:2605.18794v1 Announce Type: cross Abstract: Decoupling is a powerful modeling paradigm for representing multivariate functions as compositions of linear transformations and univariate nonlinear
arXiv:2605.18852v1 Announce Type: cross Abstract: Checkpoint selection for multimodal large language models (MLLMs) presents significant challenges when performance differentials are marginal and eval
arXiv:2605.19230v1 Announce Type: new Abstract: Age dependent performance disparities in medical image classification often arise because age acts as a confounder, linking imaging morphology with dise
arXiv:2510.09174v3 Announce Type: replace Abstract: This paper takes a closer look at Git Re-Basin, an interesting new approach to merge trained models. We propose a hierarchical model merging scheme
arXiv:2605.19924v1 Announce Type: new Abstract: Human-in-the-loop reinforcement learning systems achieve near-perfect success on the workstation where they are trained, but collapse when the same robo
arXiv:2512.11234v2 Announce Type: replace Abstract: Generating controllable indoor scenes is fundamental to applications in game development, architectural visualization, and embodied AI. However, exi
arXiv:2605.19218v1 Announce Type: cross Abstract: Vision-Language Models suffer severe KV cache pressure at inference, as a single image often encodes into thousands of tokens. Most existing methods e
arXiv:2605.19678v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong performance on embodied manipulation, yet they remain brittle under visual observation changes, pa
arXiv:2605.18842v1 Announce Type: new Abstract: Safe reinforcement learning in nonstationary environments requires safety mechanisms that adapt as environmental conditions change. Standard safe reinfo
arXiv:2605.19524v1 Announce Type: cross Abstract: End-to-end autonomous driving systems excel in common scenarios but struggle with safety-critical long-tail cases. Vision-Language-Action (VLA) models
arXiv:2605.19014v1 Announce Type: new Abstract: Microsimulation models used by ministries of finance and central banks rely on parametric processes for lifetime earnings that capture only first and se
arXiv:2605.20157v1 Announce Type: new Abstract: Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat
arXiv:2605.18864v1 Announce Type: cross Abstract: Recent studies observe that reinforcement learning with verifiable rewards (RLVR) reliably improves pass@1 on reasoning tasks, yet often fails to yiel
arXiv:2605.19469v1 Announce Type: cross Abstract: Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sa
arXiv:2605.18913v1 Announce Type: cross Abstract: The U.S. financial system processes approximately 1.3 million interbank transactions daily, yet no system in the reviewed literature models fraud prop
arXiv:2605.19360v1 Announce Type: new Abstract: The rapid proliferation of AI-generated visual media has created an urgent need for efficient, trustworthy deepfake detection systems. However, existing
arXiv:2503.19877v2 Announce Type: replace Abstract: As language model (LM) outputs get more and more natural, it is becoming more difficult than ever to evaluate their quality. Simultaneously, increas
arXiv:2503.06310v4 Announce Type: replace Abstract: Generating coherent long-form video sequences from discrete text prompts remains challenging due to difficulties in maintaining temporal coherence,
arXiv:2605.19587v1 Announce Type: new Abstract: Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation-based policy evaluation, where a useful scene must specify not only w
arXiv:2605.19095v1 Announce Type: cross Abstract: Schedule-Free Learning has shown promise as a practical anytime training method for machine learning, showing success across dozens of standard benchm
arXiv:2605.19357v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabiliti
arXiv:2503.12172v4 Announce Type: replace-cross Abstract: Generative models have rapidly evolved to generate realistic outputs. However, their synthetic outputs increasingly challenge the clear distin
arXiv:2510.18821v3 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has become the mainstream technique for training LLM agents. However, RLVR highly depends on w
arXiv:2605.19340v1 Announce Type: new Abstract: Vision foundation models (VFMs) have achieved strong performance across various vision tasks. However, it still remains challenging to apply VFMs for cr
arXiv:2605.19431v1 Announce Type: new Abstract: Multirotor aerial robots excel at maneuvering in three-dimensional space, and recent advances enable nimble navigation in cluttered and confined environ
arXiv:2605.19554v1 Announce Type: new Abstract: Instilling creativity in text-to-image (T2I) generation presents a significant challenge, as it requires synthesized images to exhibit not only visual n
arXiv:2510.05431v4 Announce Type: replace Abstract: Organizing large-scale patent corpora according to classification schemes is a core information management task that determines the accuracy and eff
arXiv:2605.19342v1 Announce Type: new Abstract: Multimodal latent-space reasoning aims to replace explicit thinking with images by performing visual reasoning directly in a compact latent space. Howev
arXiv:2605.19193v1 Announce Type: new Abstract: Multi-agent LLM debate improves factuality and reasoning, but most recipes pick a fixed round count, over-spending on easy items and under-spending on h
arXiv:2605.19830v1 Announce Type: new Abstract: Conventional treatment policies map patient covariates to a single recommended intervention in order to maximize expected clinical outcomes. Although a
arXiv:2605.20110v1 Announce Type: new Abstract: Referring segmentation grounds natural-language queries to pixel-level masks, but extending it to complex scenarios with multiple instances, cross-categ
arXiv:2512.07068v3 Announce Type: replace Abstract: Uniform Meaning Representation (UMR) is a novel graph-based semantic representation which captures the core meaning of a text, with flexibility inco
arXiv:2605.18971v1 Announce Type: cross Abstract: What determines the quality of a tabular foundation model? Unlike language or vision, tabular foundation models acquire their inductive biases almost
arXiv:2408.12385v3 Announce Type: replace-cross Abstract: We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. This
arXiv:2605.19219v1 Announce Type: new Abstract: A/B testing remains the gold standard for evaluating modifications to e-commerce storefronts, yet it diverts traffic, requires weeks to reach statistica
arXiv:2605.18797v1 Announce Type: cross Abstract: Scaling model performance typically requires increasing model size. Looped Transformer offers a compelling alternative by iteratively reusing the same
arXiv:2605.19355v1 Announce Type: cross Abstract: Retargeting motion across characters with varying body shapes while preserving interaction semantics, such as self-contact and near-body proximity, re
arXiv:2505.17726v3 Announce Type: replace-cross Abstract: Recently, multimodal large language models (MLLMs) have emerged as a key approach in achieving artificial general intelligence. In particular,
arXiv:2507.18902v2 Announce Type: replace Abstract: There are more than 7,000 languages around the world, and current Large Language Models (LLMs) only support hundreds of languages. Dictionary-based
arXiv:2605.19213v1 Announce Type: new Abstract: Circular sample plots are a cornerstone of forest inventory, yet accurate measurement of tree diameter at breast height (DBH) and spatial location withi
arXiv:2605.20069v1 Announce Type: new Abstract: Competitive selection processes, from scientific funding to admissions and hiring, use evaluations to score candidates, and eventually choose a subset o
arXiv:2605.19823v1 Announce Type: cross Abstract: Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently contin
arXiv:2605.18889v1 Announce Type: cross Abstract: Modern machine learning forces practitioners to choose between powerful but expensive deep networks and fast but limited classical algorithms. Here we
arXiv:2505.09067v2 Announce Type: replace-cross Abstract: In this article, we consider the infinite-horizon reach-avoid (RA) and stabilize-avoid (SA) zero-sum game problems for general nonlinear conti
arXiv:2602.17001v2 Announce Type: replace Abstract: Natural Language Querying for Time Series Databases (NLQ4TSDB) aims to assist non-expert users retrieve meaningful events, intervals, and summaries
arXiv:2605.06270v2 Announce Type: replace Abstract: Feed-forward 3D reconstruction models based on Vision Transformers can directly estimate scene geometry and camera poses from a small set of input i
arXiv:2605.19378v1 Announce Type: new Abstract: This paper systematically diagnoses the training failure modes of Token-Choice sparse Mixture-of-Experts (MoE) on video Diffusion Transformers. Starting
arXiv:2505.23747v2 Announce Type: replace-cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced performance on 2D visual tasks. However, improving
arXiv:2605.20085v1 Announce Type: new Abstract: Robotic manipulation is often specified through language instructions or task identifiers, yet cluttered environments with similar objects are better ha
arXiv:2605.18836v1 Announce Type: cross Abstract: Dataset Distillation (DD) synthesizes a compact synthetic dataset that preserves the training utility of a full dataset. However, its standard formula
arXiv:2605.19607v1 Announce Type: cross Abstract: Integrated Gradients (IG) is a widely adopted feature attribution method that satisfies desirable axiomatic properties. However, the choice of integra
arXiv:2605.18791v1 Announce Type: cross Abstract: Existing spectral benchmarks are limited in scale, modality alignment, and evaluation scope, and typically focus on either specialized models or multi
arXiv:2605.18856v1 Announce Type: cross Abstract: Long-context inference is increasingly constrained by the KV cache: resident memory grows with context length, and decoding becomes limited by repeate
arXiv:2605.19974v1 Announce Type: new Abstract: The generation of immersive and navigable 3D environments is increasingly prevalent with the growing adoption of virtual reality and 3D content. However
arXiv:2605.19822v1 Announce Type: cross Abstract: Temporal graph neural networks (TGNNs) have gained significant traction for solving real-world temporal graph tasks. However, their interpretability r
arXiv:2605.18905v1 Announce Type: cross Abstract: Neural operators have emerged as a powerful, discretization-invariant framework for solving partial differential equations (PDEs). Although establishe
arXiv:2605.19856v1 Announce Type: cross Abstract: Training very deep neural networks requires controlling the propagation of magnitudes across depth. Without such control, activations and gradients ma
arXiv:2605.20035v1 Announce Type: new Abstract: Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequence
arXiv:2605.18835v1 Announce Type: new Abstract: Traditional sheet metal forming relies on time-consuming and expensive Finite Element Analysis (FEA) for design validation, a process that significantly
arXiv:2605.19338v1 Announce Type: cross Abstract: Frontier AI models and multi-agent systems have led to significant improvements in mathematical reasoning. However, for problems requiring extended, l