Knowledge catalogue
arXiv:2512.15420v2 Announce Type: replace Abstract: Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. Despite recent success, e
arXiv:2604.11083v1 Announce Type: cross Abstract: Text-to-motion generation is driven by learning motion representations for semantic alignment with language. Existing methods rely on either continuou
arXiv:2604.09651v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are emerging as a cornerstone for robotics, with flow-matching policies like pi_0 showing great promise in generatin
arXiv:2604.09989v1 Announce Type: cross Abstract: Recently, synthetic palmprints have been increasingly used as substitutes for real data to train recognition models. To be effective, such synthetic d
arXiv:2604.11556v1 Announce Type: cross Abstract: LLM-assisted software development has become increasingly prevalent, and can generate large-scale systems, such as compilers. It becomes crucial to st
arXiv:2509.23438v3 Announce Type: replace Abstract: Existing periodic activation-based implicit neural representation (INR) networks, such as SIREN and FINER, suffer from hidden feature redundancy, wh
arXiv:2603.22911v2 Announce Type: replace-cross Abstract: Due to the great saving of computation and memory overhead, token compression has become a research hot-spot for MLLMs and achieved remarkable
arXiv:2511.15083v2 Announce Type: replace Abstract: Time-series anomaly detection plays a critical role in numerous real-world applications, including industrial monitoring and fault diagnosis. Recent
arXiv:2512.18073v2 Announce Type: replace Abstract: Multimodal LLMs (MLLMs) are capable of performing complex data analysis, visual question answering, generation, and reasoning tasks. However, their
arXiv:2512.01390v3 Announce Type: replace Abstract: Real-image super-resolution (Real-ISR) seeks to recover HR images from LR inputs with mixed, unknown degradations. While diffusion models surpass GA
arXiv:2604.10023v1 Announce Type: cross Abstract: With the growing availability of open-sourced adapters trained on the same diffusion backbone for diverse scenes and objects, combining these pretrain
arXiv:2604.10512v1 Announce Type: new Abstract: The development of generalizable Novel View Synthesis (NVS) models is critically limited by the scarcity of large-scale training data featuring diverse
arXiv:2604.11378v1 Announce Type: new Abstract: The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by readi
arXiv:2604.11137v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into clinical decision support is critically obstructed by their opaque and often unreliable reasoning.
arXiv:2604.11467v1 Announce Type: new Abstract: Explainable AI (XAI) methods reveal which features influence model predictions, yet provide limited means for practitioners to act on these explanations
arXiv:2511.09376v2 Announce Type: replace Abstract: SHapley Additive exPlanations (SHAP) is a key tool for interpreting decision tree ensembles by assigning contribution values to features. It is wide
arXiv:2604.10332v1 Announce Type: new Abstract: We present the progress of the GPT family from GPT-3 through GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o, GPT-4.1, and the GPT-5 family. Our work is comparative
arXiv:2604.10300v1 Announce Type: cross Abstract: LLM-based coding agents are increasingly used to generate code, tests, and documentation. Still, their outputs can be plausible yet misaligned with de
arXiv:2604.06788v2 Announce Type: replace-cross Abstract: We present a solver-agnostic framework in which coordinated large language model (LLM) agents autonomously execute the complete computational
arXiv:2604.10517v1 Announce Type: new Abstract: Modern vision-language models achieve strong performance in static perception, but remain limited in the complex spatiotemporal reasoning required for e
arXiv:2603.23964v2 Announce Type: replace Abstract: The remarkable progress of reinforcement learning (RL) is intrinsically tied to the environments used to train and evaluate artificial agents. Movin
arXiv:2604.10470v1 Announce Type: cross Abstract: Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-qu
arXiv:2604.09964v1 Announce Type: new Abstract: We present a family of Kaczmarz-based preference learning algorithms for real-time personalized matchmaking in reciprocal recommender systems. Post-step
arXiv:2604.11376v1 Announce Type: cross Abstract: Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. Howev
arXiv:2604.09602v1 Announce Type: new Abstract: Leyva-Vazquez and Smarandache (2025) demonstrated that neutrosophic T/I/F evaluation, where Truth, Indeterminacy, and Falsity are independent dimensions
arXiv:2602.03059v2 Announce Type: replace-cross Abstract: We introduce Speech-to-Spatial, a referent disambiguation framework that converts verbal remote-assistance instructions into spatially grounde
arXiv:2604.09597v1 Announce Type: cross Abstract: Creativity and strategic foresight have been extensively studied through descriptive theories -- Koestler's bisociation (1964), de Bono's lateral thin
arXiv:2604.11041v1 Announce Type: new Abstract: Semiconductor supply chains face unprecedented resilience challenges amidst global geopolitical turbulence. Conventional Large Language Model (LLM) plan
arXiv:2604.11518v1 Announce Type: cross Abstract: Cross-language migration of large software systems is a persistent engineering challenge, particularly when the source codebase evolves rapidly. We pr
arXiv:2604.09907v1 Announce Type: cross Abstract: To improve crop genetics, high-throughput, effective and comprehensive phenotyping is a critical prerequisite. While such tasks were traditionally per
arXiv:2604.09634v1 Announce Type: cross Abstract: Most AI literacy courses for non-technical undergraduates emphasize conceptual breadth over technical depth. This paper describes UNIV 182, a prerequi
arXiv:2604.11104v1 Announce Type: new Abstract: This paper presents an empirical study of a multi-model zero-shot pipeline for knowledge graph construction and exploitation, executed entirely through
arXiv:2509.20624v5 Announce Type: replace-cross Abstract: Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which l
arXiv:2603.07819v3 Announce Type: replace Abstract: Accurate estimation of pasture biomass from agricultural imagery is critical for sustainable livestock management, yet existing methods are limited
arXiv:2604.09990v1 Announce Type: new Abstract: Gait recognition is a biometric modality that identifies individuals from their characteristic walking patterns. Unlike conventional biometric traits, g
arXiv:2601.03416v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have become widely deployed, yet their safety alignment remains fragile under adversarial inputs. Previous
arXiv:2603.24329v2 Announce Type: replace-cross Abstract: Multimodal LLMs are increasingly deployed as perceptual backbones for autonomous agents in 3D environments, from robotics to virtual worlds. T
arXiv:2403.15651v5 Announce Type: replace Abstract: In this paper, we present GaNI, a Global and Near-field Illumination-aware neural inverse rendering technique that can reconstruct geometry, albedo,
arXiv:2601.06767v2 Announce Type: replace-cross Abstract: We present a Bengali mathematical reasoning model called GanitLLM (named after the Bangla word for mathematics, 'Ganit'), together with a new
arXiv:2604.11653v1 Announce Type: new Abstract: We introduce GazeVaLM, a public eye-tracking dataset for studying clinical perception during chest radiograph authenticity assessment. The dataset compr
arXiv:2604.09609v1 Announce Type: new Abstract: Human behavior models are essential as behavior references and for simulating human agents in virtual safety assessment of automated vehicles (AVs), yet
arXiv:2604.11778v1 Announce Type: cross Abstract: Contemporary large language models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in specialized domains like mathematics an
arXiv:2601.01041v3 Announce Type: replace Abstract: Deepfake detection remains highly challenging, particularly in cross-dataset scenarios and complex real-world settings. This challenge mainly arises
arXiv:2604.11101v1 Announce Type: cross Abstract: We present a new method for constructing Hadamard matrices that combines transformer neural networks with local search in the PatternBoost framework.
arXiv:2604.09645v1 Announce Type: cross Abstract: Medical conversations offer insights into clinical communication often absent from Electronic Health Records. However, developing reliable clinical Na
arXiv:2604.10748v1 Announce Type: cross Abstract: Generating multiple-choice questions (MCQs) with difficulty estimation remains challenging in automated MCQ-generation systems used in adaptive, AI-as
arXiv:2601.08209v3 Announce Type: replace Abstract: In domains such as materials science, biomedicine, and finance, high-stakes deployment of large language models (LLMs) requires injecting private, d
arXiv:2604.10941v1 Announce Type: cross Abstract: Rapid growth in artificial intelligence (AI) workloads is driving up data center power densities, increasing the need for advanced thermal management.
arXiv:2604.11519v1 Announce Type: new Abstract: Wasserstein gradient flows (WGFs) describe the evolution of probability distributions in Wasserstein space as steepest descent dynamics for a free energ
arXiv:2604.09577v1 Announce Type: cross Abstract: AI models excel at creating content, but typically render it with static, predefined interfaces. Specifically, the output of LLMs is often a markdown
arXiv:2601.04932v2 Announce Type: replace Abstract: Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as
arXiv:2604.11786v1 Announce Type: new Abstract: Modeling open-play soccer tactics is a formidable challenge due to the stochastic, multi-agent nature of the game. Existing computational approaches typ
arXiv:2509.04334v4 Announce Type: replace Abstract: Geographic reasoning is a fundamental cognitive capability that requires models to infer plausible locations by synthesizing visual evidence with sp
arXiv:2602.09932v2 Announce Type: replace Abstract: Building height (BH) and footprint (BF) are fundamental urban morphological parameters required by climate modelling, disaster-risk assessment, and
arXiv:2604.10591v1 Announce Type: cross Abstract: Effective foundation modeling in remote sensing requires spatially aligned heterogeneous modalities coupled with semantically grounded supervision, ye
arXiv:2604.11344v1 Announce Type: cross Abstract: Embedding-as-a-Service (EaaS) has become an important semantic infrastructure for natural language and multimedia applications, but it is highly vulne
arXiv:2604.11585v1 Announce Type: new Abstract: Multimodal perception systems for robotics and embodied AI often assume reliable RGB-D sensing, but in practice, depth is frequently missing, noisy, or
arXiv:2604.11600v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perce
arXiv:2604.09793v1 Announce Type: cross Abstract: Scientific breakthroughs often emerge from synthesizing prior ideas into novel contributions. While language models (LMs) show promise in scientific d
arXiv:2604.09999v1 Announce Type: new Abstract: IR drop analysis is essential in physical chip design to ensure the power integrity of on-chip power delivery networks. Traditional Electronic Design Au