Knowledge catalogue
arXiv:2605.18745v1 Announce Type: cross Abstract: Diffusion-based generative models increasingly rely on inference-time guidance, adding a drift term or reweighting mixture of experts, to improve samp
arXiv:2605.18383v1 Announce Type: new Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning. Tab
arXiv:2605.18109v1 Announce Type: new Abstract: In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather than from a clean
arXiv:2605.18430v1 Announce Type: new Abstract: Text-to-CAD generation aims to create parametric CAD models from natural language, enabling rapid prototyping and intuitive design workflows. However, e
arXiv:2605.18063v1 Announce Type: new Abstract: Object counting is a foundational vision task with over a decade of dedicated research, yet state-of-the-art models still fail systematically in the mix
arXiv:2605.18434v1 Announce Type: cross Abstract: E-commerce image search often takes a cropped image as the query, while each candidate is represented by full item images and structured text. This im
arXiv:2605.17247v1 Announce Type: new Abstract: Argumentative essays serve as a vital medium for assessing critical thinking and reasoning skills, yet there is limited works on accurately understandin
arXiv:2605.18163v1 Announce Type: new Abstract: Hallucination correction is not a one-direction problem. We show that intermediate layers are neither uniformly more truthful than final layers nor unif
arXiv:2409.10102v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). Although ex
arXiv:2509.23986v2 Announce Type: replace Abstract: Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such too
arXiv:2605.17942v1 Announce Type: new Abstract: Feed-forward 3D reconstruction has recently demonstrated strong generalization across diverse scenes, yet its performance in UAV imagery remains underex
arXiv:2601.12696v2 Announce Type: replace Abstract: Current guardian models are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerab
arXiv:2605.16750v1 Announce Type: cross Abstract: Personalized exercise recommendation dynamically aligns pedagogical resources with individual knowledge mastery, which is crucial for satisfying stude
arXiv:2505.16831v3 Announce Type: replace-cross Abstract: Unlearning in large language models (LLMs) aims to remove specified data, but its efficacy is typically assessed with task-level metrics like
arXiv:2602.22801v2 Announce Type: replace-cross Abstract: Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving
arXiv:2605.17691v1 Announce Type: cross Abstract: Automating the classification of negative treatment in legal precedent is a critical yet nuanced NLP task where misclassification carries significant
arXiv:2605.17626v1 Announce Type: new Abstract: Test-time scaling is an important mechanism for improving large language models, especially on tasks with deterministic verifiers. Code translation is a
arXiv:2605.18029v1 Announce Type: new Abstract: Multimodal product retrieval (MPR) underpins checkout-free retail and automated inventory systems, yet it demands fine-grained SKU discrimination that s
arXiv:2601.16398v2 Announce Type: replace-cross Abstract: Algorithmic audits are essential tools for examining systems for properties required by regulators or desired by operators. Current audits of
arXiv:2605.16402v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have revolutionized GUI automation, yet their efficacy is largely established on idealized, single-layer interf
arXiv:2605.16547v1 Announce Type: new Abstract: Semantic communication has emerged as a promising paradigm for enabling goal-oriented networking. However, most existing semantic communication solution
arXiv:2605.16877v1 Announce Type: new Abstract: Zero-shot textual explanations aim to make image classifiers more transparent by probing their internal representations, without relying on task-specifi
arXiv:2605.15708v1 Announce Type: new Abstract: Recent advances in 3D datasets and multimodal models have greatly improved natural language 3D scene understanding. However, most 3D referring segmentat
arXiv:2605.15510v1 Announce Type: new Abstract: This paper presents a quadratic unconstrained binary optimization-based formulation framework for robot design optimization using kinematic structure-le
arXiv:2605.15376v1 Announce Type: new Abstract: Sub-Saharan Africa faces persistently high student-teacher ratios and shortages of qualified teachers, limiting students' access to personalized learnin
arXiv:2601.03707v2 Announce Type: replace Abstract: Existing UAV vision-and-language navigation (VLN) benchmarks rarely provide realistic aerial scenes, natural process-level instructions, and suffici
arXiv:2605.15687v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) may memorize sensitive cross-modal information during pretraining, making machine unlearning (MU) crucial. Ex
arXiv:2507.16806v2 Announce Type: replace-cross Abstract: When language models (LMs) are trained via reinforcement learning (RL) to generate natural language 'reasoning chains', their performance impr
arXiv:2605.15400v1 Announce Type: new Abstract: While AI agents are rapidly advancing from isolated tools to interactive collaborators, data-driven human-machine teaming (HMT) methods remain costly in
arXiv:2605.15312v1 Announce Type: cross Abstract: Large-scale facial datasets like CelebA are widely used in computer vision, yet the cultural biases embedded in their labels remain underexplored. Fai
arXiv:2605.16011v1 Announce Type: cross Abstract: Adaptive learning refers to educational technologies that track learners' learning progress and adapt the instructional process based on individual le
arXiv:2605.15218v1 Announce Type: new Abstract: Large language models deployed for MAPDL finite-element simulation face practical reliability challenges: without structured execution control, tool enc
arXiv:2605.15666v1 Announce Type: new Abstract: Hyperspectral imaging (HSI) provides dense spectral information for the Earth's surface, enabling material-level understanding of land cover and ecosyst
arXiv:2605.15549v1 Announce Type: cross Abstract: The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, desi
arXiv:2605.15680v1 Announce Type: new Abstract: Online patient inquiries are often informal, incomplete, and written before professional assessment, yet they must still be routed to an appropriate lev
arXiv:2605.15282v1 Announce Type: new Abstract: Literary translation requires balancing target-language fluency with faithfulness to the source. Recent large language models (LLMs) often produce fluen
arXiv:2605.16233v1 Announce Type: new Abstract: Can LLM agents improve decision-making through self-generated memory without gradient updates? We propose FORGE (Failure-Optimized Reflective Graduation
arXiv:2605.16026v1 Announce Type: cross Abstract: Compositional speech-to-speech translation (S2ST) systems built upon speech large language models (SpeechLLMs) have recently shown promising performan
arXiv:2605.15901v1 Announce Type: new Abstract: Diffusion geometry is a manifold learning framework that uses random walks defined by Markov transition matrices to characterize the geometry of a datas
arXiv:2511.09378v2 Announce Type: replace Abstract: A series of influential studies established that large language models cannot reliably solve even simple planning tasks. We show that the latest gen
arXiv:2605.16215v1 Announce Type: new Abstract: Clinical decision support systems (CDSS) require scrutable, auditable pipelines that enable rigorous, reproducible validation. Yet current LLM-based CDS
arXiv:2605.15223v1 Announce Type: cross Abstract: This paper presents an LLM-empowered workflow for RISC-V supply chain analysis, integrating Vision-Language Models (VLMs) and Model-Driven Engineering
arXiv:2605.15338v1 Announce Type: cross Abstract: Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for pe
arXiv:2605.16023v1 Announce Type: new Abstract: LLM-as-a-judge has become the dominant paradigm for grading model outputs at scale, yet the same model assigns systematically different scores when its
arXiv:2605.15589v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledg
arXiv:2605.15574v1 Announce Type: new Abstract: Longitudinal chest X-ray (CXR) interpretation requires reasoning over disease evolution across multiple patient visits, yet most existing medical VQA be
arXiv:2511.15887v2 Announce Type: replace Abstract: Our ability to interpret others' mental states through nonverbal cues (NVCs) is fundamental to our survival and social cohesion. While existing Theo
arXiv:2605.16118v1 Announce Type: new Abstract: The source distribution in conditional flow matching is a design parameter that can be calibrated to data, not a default isotropic prior. We exploit thi
arXiv:2605.15326v1 Announce Type: new Abstract: Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlu
arXiv:2605.15520v1 Announce Type: cross Abstract: Data attribution has become an important component of pricing, auditing, and governance in machine learning pipelines, yet most attribution methods im
arXiv:2605.15229v1 Announce Type: cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a des
arXiv:2605.16238v1 Announce Type: new Abstract: Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling tea
arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deploy
arXiv:2605.15425v1 Announce Type: cross Abstract: Agentic coding systems increasingly use large language models (LLMs) for software engineering tasks such as debugging, root cause analysis, and code r
arXiv:2605.15777v1 Announce Type: new Abstract: Computer-Using Agents (CUAs) are rapidly extending large language models (LLMs) beyond text-based reasoning toward action execution in more complex envi
arXiv:2605.15204v1 Announce Type: new Abstract: Multi-agent orchestration frameworks such as LangChain, LangGraph, and CrewAI route tasks through graph-based pipelines but do not enforce the stage con
arXiv:2605.15585v1 Announce Type: new Abstract: Large language models can generate executable code for educational animations, but the resulting renders often exhibit visual defects, including element
arXiv:2512.15693v2 Announce Type: replace Abstract: The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated vid
arXiv:2602.21141v2 Announce Type: replace Abstract: Object perception is fundamental for tasks such as robotic material handling and quality inspection. However, modern supervised deep-learning models
arXiv:2605.15465v1 Announce Type: new Abstract: Physiological time series signals reflect complex, multi-scale dynamical processes of the human body. Existing modeling studies focus on static tasks su