Knowledge catalogue
arXiv:2604.21291v1 Announce Type: cross Abstract: Controllable human video generation aims to produce realistic videos of humans with explicitly guided motions and appearances,serving as a foundation
arXiv:2510.18914v4 Announce Type: replace-cross Abstract: Large language models often display undesirable behaviors embedded in their internal representations, undermining fairness, inconsistency drif
arXiv:2604.21711v1 Announce Type: cross Abstract: Fair machine learning (ML) methods help identify and mitigate the risk that algorithms encode or automate social injustices. Algorithmic approaches al
arXiv:2604.21420v1 Announce Type: new Abstract: Quality Estimation (QE) aims to assess machine translation quality without reference translations, but recent studies have shown that existing QE models
arXiv:2604.20913v1 Announce Type: new Abstract: Large language models are increasingly deployed on CPU-only platforms where memory bandwidth is the primary bottleneck for autoregressive generation. We
arXiv:2506.19579v3 Announce Type: replace-cross Abstract: Robotic scene understanding increasingly relies on Vision-Language Models (VLMs) to generate natural language descriptions of the environment.
arXiv:2411.11707v3 Announce Type: replace-cross Abstract: By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the c
arXiv:2510.04772v2 Announce Type: replace-cross Abstract: Developing generalizable surgical AI requires multi-institutional data, yet patient privacy constraints preclude direct data sharing, making F
arXiv:2604.21555v1 Announce Type: new Abstract: Sentence embedding techniques aim to encode key concepts of a sentence's meaning in a vector space. However, the majority of evaluation approaches for s
arXiv:2604.21667v1 Announce Type: cross Abstract: Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this
arXiv:2604.21927v1 Announce Type: new Abstract: Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benc
arXiv:2604.21331v1 Announce Type: new Abstract: The current practice of dexterous manipulation generally relies on a single wrist-mounted view, which is often occluded and limits performance on tasks
arXiv:2604.21698v1 Announce Type: new Abstract: Persistent homology, a method from topological data analysis, extracts robust, multi-scale features from data. It produces stable representations of tim
arXiv:2506.09998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can often accurately describe probability distributions using natural language, yet they still struggle to genera
arXiv:2510.04823v2 Announce Type: replace Abstract: Generating synthetic CT (sCT) from MRI or CBCT plays a crucial role in enabling MRI-only and CBCT-based adaptive radiotherapy, improving treatment p
arXiv:2601.02438v3 Announce Type: replace-cross Abstract: Software vulnerability detection can be formulated as a binary classification problem that determines whether a given code snippet contains se
arXiv:2604.20920v1 Announce Type: new Abstract: Scaling large language models to long contexts is challenging due to the quadratic computational cost of full attention. Mitigation approaches include K
arXiv:2604.21079v1 Announce Type: new Abstract: Vision-language models benefit from high-resolution images, but the increase in visual-token count incurs high compute overhead. Humans resolve this ten
arXiv:2604.20902v1 Announce Type: cross Abstract: While standard flow-matching models transport noise to data uniformly, incorporating an explicit generation order - specifically, establishing coarse,
arXiv:2604.21786v1 Announce Type: new Abstract: Social media platforms have become primary arenas for climate communication, generating millions of images and posts that - if systematically analysed -
arXiv:2604.21716v1 Announce Type: new Abstract: Prior work evaluates code generation bias primarily through simple conditional statements, which represent only a narrow slice of real-world programming
arXiv:2604.21391v1 Announce Type: cross Abstract: Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the
arXiv:2509.23649v2 Announce Type: replace-cross Abstract: Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However,
arXiv:2604.21910v1 Announce Type: new Abstract: Scientific workflow systems automate execution -- scheduling, fault tolerance, resource management -- but not the semantic translation that precedes it.
arXiv:2604.21511v1 Announce Type: cross Abstract: Learned Sparse IR models, such as SPLADE, offer an excellent efficiency-effectiveness tradeoff. However, they rely on the underlying backbone vocabula
arXiv:2604.21479v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated strong reasoning capabilities and attracted increasing research attention in the field of autono
arXiv:2604.21321v1 Announce Type: new Abstract: Monitoring frying oil degradation is critical for food safety, yet current practice relies on destructive wet-chemistry assays that provide no spatial i
arXiv:2604.21189v1 Announce Type: new Abstract: Collision avoidance for robotic manipulators requires enforcing full-body safety constraints in high-dimensional configuration spaces. Control Barrier F
arXiv:2508.11354v3 Announce Type: replace-cross Abstract: Purpose: This study introduces the first adaptation of RETFound for joint optic disc (OD) and optic cup (OC) segmentation. RETFound is a well-
arXiv:2506.04292v3 Announce Type: replace-cross Abstract: Purpose: We introduce GARG-AML, a fast and transparent graph-based method to catch `smurfing', a common money-laundering tactic. It assigns a
arXiv:2512.22274v2 Announce Type: replace Abstract: We introduce GeCo, a geometry-grounded metric for jointly detecting geometric deformation and occlusion-inconsistency artifacts in static scenes. By
arXiv:2603.03700v2 Announce Type: replace-cross Abstract: Despite the remarkable empirical success of score-based diffusion models, their statistical guarantees remain underdeveloped. Existing analyse
arXiv:2604.21495v1 Announce Type: cross Abstract: Numerical reasoning over expert-domain tables often exhibits high in-domain accuracy but limited robustness to domain shift. Models trained with super
arXiv:2604.21073v1 Announce Type: cross Abstract: Discovering materials that must simultaneously satisfy multiple competing constraints remains a central challenge in computational materials design, p
arXiv:2509.21976v3 Announce Type: replace-cross Abstract: Referring expression understanding in remote sensing poses unique challenges, as it requires reasoning over complex object-context relationshi
arXiv:2604.21638v1 Announce Type: new Abstract: Dataset condensation constructs compact synthetic datasets that retain the training utility of large real-world datasets, enabling efficient model devel
arXiv:2604.21677v1 Announce Type: cross Abstract: The choice of activation function plays a crucial role in the optimization and performance of deep neural networks. While the Rectified Linear Unit (R
arXiv:2502.09795v3 Announce Type: replace Abstract: Planetary exploration using aerial assets has the potential for unprecedented scientific discoveries on Mars. While NASA's Mars helicopter Ingenuity
arXiv:2604.21501v1 Announce Type: new Abstract: Lithology classification in well logs is a fundamental geoscience data mining task that aims to infer rock types from multi dimensional geophysical sequ
arXiv:2601.09361v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a key paradigm for improving large-scale reasoning models. Unlike supervised fine-tun
arXiv:2601.13711v2 Announce Type: replace Abstract: Authorship verification (AV) is the task of determining whether two texts were written by the same author and has been studied extensively, predomin
arXiv:2604.21830v1 Announce Type: new Abstract: We present GFlowState, a visual analytics system designed to illuminate the training process of Generative Flow Networks (GFlowNets or GFNs). GFlowNets
arXiv:2604.21901v1 Announce Type: cross Abstract: As model sizes continue to grow, parameter-efficient fine-tuning has emerged as a powerful alternative to full fine-tuning. While LoRA is widely adopt
arXiv:2604.21519v1 Announce Type: new Abstract: In the automatic reassembly of fragments acquired using laser scanners to reconstruct objects, a crucial step is the matching of fractured surfaces. In
arXiv:2604.21175v1 Announce Type: new Abstract: We propose a learning-augmented framework for accelerating max-flow computation and image segmentation by integrating Graph Neural Networks (GNNs) with
arXiv:2604.21290v1 Announce Type: new Abstract: Vision Graph Neural Networks (ViGs) represent an image as a graph of patch tokens, enabling adaptive, feature-driven neighborhoods. Unlike CNNs with fix
arXiv:2604.21133v1 Announce Type: new Abstract: We present GRISP (Guided Recurrent IRI Selection over SPARQL Skeletons), a novel SPARQL-based question-answering method over knowledge graphs based on f
arXiv:2603.07101v3 Announce Type: replace Abstract: Creatively translating complex gameplay ideas into executable artifacts (e.g., games as Unity projects and code) remains a central challenge in comp
arXiv:2604.21873v1 Announce Type: new Abstract: Physical video understanding requires more than naming an event correctly. A model can answer a question about pouring, sliding, or collision from textu
arXiv:2604.21649v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph em
arXiv:2511.22793v2 Announce Type: replace Abstract: Channel state information (CSI) is essential for adaptive beamforming and maintaining robust links in wireless communication systems. However, acqui
arXiv:2601.10479v2 Announce Type: replace-cross Abstract: Variational Quantum Algorithms (VQAs) are critically threatened by the Barren Plateau (BP) phenomenon. In this work, we introduce the H-EFT Va
arXiv:2601.05019v2 Announce Type: replace-cross Abstract: Recent Large Reasoning Models trained via reinforcement learning exhibit a 'natural' alignment with human cognitive costs. However, we show th
arXiv:2604.20938v1 Announce Type: cross Abstract: Long-horizon language-model agents are dominated, in lines of code and in operational complexity, not by their underlying model but by the harness tha
arXiv:2604.20882v1 Announce Type: cross Abstract: Quantum algorithms with a proven theoretical speedup over classical computation are rare. Among the most prominent is the Harrow-Hassidim-Lloyd (HHL)
arXiv:2604.21741v1 Announce Type: new Abstract: Post-training is essential for turning pretrained generalist robot policies into reliable task-specific controllers, but existing human-in-the-loop pipe
arXiv:2604.21444v1 Announce Type: new Abstract: Long-form video understanding remains fundamentally challenged by pervasive spatiotemporal redundancy and intricate narrative dependencies that span ext
arXiv:2604.21045v1 Announce Type: new Abstract: Simultaneous speech translation (SST) generates translations while receiving partial speech input. Recent advances show that large language models (LLMs
arXiv:2501.11275v2 Announce Type: replace Abstract: This paper investigates the L_p approximation error for higher order Korobov functions using deep convolutional neural networks (CNNs) with ReLU act
arXiv:2604.21496v1 Announce Type: new Abstract: Human-elephant conflict (HEC) is rising across India as habitat loss and expanding human settlements force elephants into closer contact with people. Wh