Knowledge catalogue
arXiv:2605.18115v1 Announce Type: new Abstract: Building a unified visual tokenizer is essential for bridging the gap between visual understanding and generation. Yet existing approaches struggle with
arXiv:2605.16903v1 Announce Type: new Abstract: Open world image segmentation aims to achieve precise segmentation and semantic understanding of targets within images by addressing the infinitely open
arXiv:2605.18534v1 Announce Type: new Abstract: Multivariate time-series analysis involves extracting informative representations from sequences of multiple interdependent variables, supporting tasks
arXiv:2511.06516v3 Announce Type: replace Abstract: Many LLM applications require only narrow capabilities, yet standard post-training quantization (PTQ) methods allocate precision without considering
arXiv:2605.15398v1 Announce Type: cross Abstract: Recent advances in 3D generative editing, particularly pipelines based on 3D Gaussian Splatting (3DGS), have achieved high-fidelity, multi-view-consis
arXiv:2510.18814v3 Announce Type: replace-cross Abstract: Can language models improve their reasoning performance without external rewards, using only their own sampled responses for training? We show
arXiv:2507.14200v2 Announce Type: replace-cross Abstract: Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks, leading to suboptimal per
arXiv:2605.16208v1 Announce Type: cross Abstract: Flexible continuous-time survival modeling is critical for capturing complex time-varying hazard dynamics in high-dimensional data; however, training
arXiv:2503.16589v2 Announce Type: replace Abstract: A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different resul
arXiv:2605.16054v1 Announce Type: cross Abstract: Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these appr
arXiv:2505.21698v3 Announce Type: replace Abstract: Vision-language foundation models achieve promising performance in natural image classification, yet their direct application to medical imaging is
arXiv:2510.13842v2 Announce Type: replace-cross Abstract: Knowledge poisoning poses a critical threat to Retrieval-Augmented Generation (RAG) systems by injecting adversarial content into knowledge ba
arXiv:2605.15203v1 Announce Type: cross Abstract: We introduce Agent4POI, the first POI recommendation framework that generates context-conditioned multimodal representations at recommendation time, r
arXiv:2605.15206v1 Announce Type: cross Abstract: Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based ques
arXiv:2605.15768v1 Announce Type: new Abstract: Social simulation provides a compelling testbed for studying social intelligence, where agents interact through multi-turn dialogues under evolving cont
arXiv:2605.15793v1 Announce Type: new Abstract: Pre-training neural operators on diverse partial differential equation (PDE) datasets has emerged as a promising direction for building general-purpose
arXiv:2512.14671v3 Announce Type: replace Abstract: We introduce ART, Articulated Reconstruction Transformer -- a category-agnostic, feed-forward model that reconstructs complete 3D articulated object
arXiv:2605.16112v1 Announce Type: cross Abstract: Transformer-based architectures have become the dominant paradigm for Continuous-Time Dynamic Graph (CTDG) learning, yet their performance remains lim
arXiv:2509.15267v2 Announce Type: replace-cross Abstract: The costs of generative model compute rekindled promises and hopes for efficient data curation. In this work, we investigate whether recently
arXiv:2601.21357v2 Announce Type: replace Abstract: Bayesian Optimization (BO) is a principled framework for optimizing expensive black-box functions, with Expected Improvement (EI) among its most wid
Accessing enterprise data is shifting from static reports to dynamic use by autonomous systems. To keep up, organizations must route fragmented data from SaaS, IoT, and legacy sources into secure, sca
arXiv:2605.15722v1 Announce Type: cross Abstract: Accurate delineation of electrocardiogram (ECG), the segmentation of meaningful waveform features, is crucial for cardiovascular diagnostics. However,
arXiv:2605.15736v1 Announce Type: cross Abstract: Biomedical Vision--Language Models (VLMs) have shown remarkable promise in few-shot medical diagnosis but face a critical bottleneck: extit{fragility
arXiv:2605.15815v1 Announce Type: cross Abstract: Code agents increasingly help developers work with unfamiliar repositories, but every such task depends on a costly prerequisite: bootstrapping the re
arXiv:2605.15399v1 Announce Type: cross Abstract: Neural surrogate solvers of partial differential equations (PDEs) promise dramatic speedups over numerical methods, especially in scenarios requiring
arXiv:2605.15513v1 Announce Type: new Abstract: Parallel reasoning, where a generator samples many candidate solutions and an aggregator selects the best, is one of the most effective forms of test-ti
arXiv:2605.15375v1 Announce Type: cross Abstract: Remote sensing change detection (RSCD) aims to localise changes between two images of the same geographic region. In practice, change masks often foll
arXiv:2601.09512v2 Announce Type: replace-cross Abstract: To teach robots complex manipulation tasks, a common approach is to fine-tune a pre-trained vision-language-action model (VLA) on task-specifi
arXiv:2605.15625v1 Announce Type: new Abstract: We introduce ColPackAgent, an agent framework that autonomously runs Monte Carlo simulations of colloidal packing through a Model Context Protocol (MCP)
arXiv:2605.16207v1 Announce Type: new Abstract: Effective tutoring requires distinguishing optimal, valid but suboptimal, and incorrect student solutions, a distinction central to intelligent tutoring
arXiv:2605.15904v1 Announce Type: new Abstract: Cybersecurity Knowledge Graphs (CKGs) unify diverse Cyber Threat Intelligence (CTI) sources into structured, queryable formats, offering scalable soluti
arXiv:2605.15315v1 Announce Type: new Abstract: LLM-powered coding agents spend the majority of their token budget reading repository files, yet much of the retrieved code is irrelevant to the task at
arXiv:2605.15775v1 Announce Type: new Abstract: Continual learning (CL) aims to train models sequentially over multiple domains without forgetting previously learned knowledge. However, existing CL me
arXiv:2605.15354v1 Announce Type: new Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tas
arXiv:2605.15325v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong performance in video anomaly detection (VAD) while providing interpretable predictions. However, existin
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:2605.15254v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) combine deep learning with physical constraints for solving partial differential equations (PDEs), and are wide
arXiv:2505.06982v3 Announce Type: replace Abstract: Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations
arXiv:2605.15728v1 Announce Type: cross Abstract: Category-level 6D object pose estimation is typically formulated as a multi-category joint learning problem with fully shared model parameters. Howeve
arXiv:2605.15942v1 Announce Type: cross Abstract: Open-vocabulary segmentation models often struggle to generalize to unseen combinations of object categories and attributes, because fine-grained desc
arXiv:2602.00747v2 Announce Type: replace-cross Abstract: Determining an effective data mixture is a key factor in Large Language Model (LLM) pre-training, where models must balance general competence
arXiv:2410.01990v3 Announce Type: replace Abstract: This paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original K
arXiv:2605.15202v1 Announce Type: new Abstract: Presentations are a primary medium for scholarly communication, yet most AI slide generators optimize the artifact (a visually plausible deck) while und
arXiv:2605.15532v1 Announce Type: cross Abstract: Distillation enables compact Vision-Language Models (VLMs) to obtain strong reasoning capabilities, yet the prompts driving this process are typically
arXiv:2407.08094v3 Announce Type: replace-cross Abstract: We introduce the Binless Multidimensional Thermodynamic Integration (BMTI) method for nonparametric, robust, and data-efficient density estima
arXiv:2605.15967v1 Announce Type: new Abstract: We study event-graph substrates: a class of world models that represent agent state as an append-only log of typed RDF triples and answer counterfactual
arXiv:2605.15706v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existi
arXiv:2605.15759v1 Announce Type: new Abstract: Large language model (LLM) agents require long-term memory to leverage information from past interactions. However, existing memory systems often face a
arXiv:2605.15225v1 Announce Type: cross Abstract: Biologically-inspired AI agent frameworks claim reliability benefits through structural guarantees adapted from gene regulatory networks, immune syste
arXiv:2511.19399v3 Announce Type: replace-cross Abstract: Deep research agents perform multi-step research to produce long-form, well-attributed answers. However, most open deep research agents are tr
arXiv:2605.15676v1 Announce Type: new Abstract: Block discrete diffusion language models factorize a sequence autoregressively over fixed-size positional blocks, decoupling within-block parallel denoi
arXiv:2605.12581v1 Announce Type: cross Abstract: Synthesising autonomous agents that can navigate uncertain environments while adhering to complex temporal constraints remains a fundamental challenge
arXiv:2605.16126v1 Announce Type: cross Abstract: For a fixed flow-based generative model under a small inference budget, sample quality can depend strongly on where the sampler spends its few functio
arXiv:2605.16223v1 Announce Type: cross Abstract: Generative video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain. Unlike natur
arXiv:2605.15355v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are biologically inspired energy-efficient models that use sparse binary spike-based communication between neurons, makin
arXiv:2602.12262v3 Announce Type: replace Abstract: Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decodin
arXiv:2605.15320v1 Announce Type: cross Abstract: Avatar reconstruction has traditionally relied on per-subject optimization that requires hours of computation or on expensive preprocessing that limit
arXiv:2605.15794v1 Announce Type: new Abstract: We present ForMaT (Format-Preserving Multilingual Translation), a parallel corpus of 3,956 PDFs across 15 language pairs that preserves original layout
arXiv:2605.15951v1 Announce Type: new Abstract: Finetuning Large Vision-Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grou
arXiv:2605.15412v1 Announce Type: cross Abstract: Modern quantitative trading increasingly relies on systematic models to extract predictive signals from large-scale financial data, where alpha factor