Knowledge catalogue
arXiv:2607.20799v1 Announce Type: new Abstract: Scalar metrics are often used to evaluate clusterings against known classes, but they can obscure a fundamental trade-off: clusterings should be informa
arXiv:2607.20922v1 Announce Type: new Abstract: We propose FA-LAM, a Focus-Aware Large Avatar Model for one-shot animatable Gaussian head creation, while simultaneously enabling static 3D and dynamic
arXiv:2607.21480v1 Announce Type: new Abstract: An initial high-recall stage in an empirical pipeline decides which items pass to later review, labelling, or modelling, and relevant items it misses ar
arXiv:2507.14268v2 Announce Type: replace Abstract: This paper reviews algorithmic and modeling approaches for fitting generalized power diagrams to three-dimensional image data, a key step in virtual
arXiv:2607.20500v1 Announce Type: new Abstract: Large Language Models (LLMs) perform strongly on well-specified reasoning tasks with a feasible answer. However, problems encountered in the open world
arXiv:2607.20583v1 Announce Type: cross Abstract: The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess
arXiv:2607.19195v2 Announce Type: replace-cross Abstract: Using large deviations theory, we solve and obtain a general expression for the free energy functional for a broad class of associative memori
arXiv:2607.20540v1 Announce Type: cross Abstract: How should a diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise schedules are b
arXiv:2510.15236v2 Announce Type: replace Abstract: Contemporary AGI evaluations report multidomain capability profiles, yet they typically assign symmetric weights and rely on snapshot scores. This c
arXiv:2607.20497v1 Announce Type: new Abstract: Prompt optimization for text classification spans diverse approaches, from demonstration selection to exploration-based search to error-driven diagnosis
arXiv:2605.11936v2 Announce Type: replace Abstract: Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from the learned
arXiv:2607.20970v1 Announce Type: new Abstract: Implicit neural representations (INRs) for time-varying volumetric data are typically trained using dense sampling over spatiotemporal coordinates, wher
arXiv:2607.20955v1 Announce Type: new Abstract: Accurate segmentation of brain stroke lesions in non-contrast computed tomography (NCCT) scans is critical for rapid clinical decision-making, yet remai
arXiv:2607.21085v1 Announce Type: new Abstract: Despite remarkable progress in visual understanding, Multimodal Large Language Models (MLLMs) remain prone to hallucinations when reasoning about spatia
arXiv:2607.21036v1 Announce Type: new Abstract: Adversarial attacks against large vision-language models (LVLMs) serve as an effective means of assessing their robustness in cross-modal semantic under
arXiv:2505.23869v5 Announce Type: replace-cross Abstract: A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a loss
arXiv:2607.21117v1 Announce Type: cross Abstract: Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, partic
arXiv:2607.21561v1 Announce Type: new Abstract: Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembl
arXiv:2607.21580v1 Announce Type: cross Abstract: Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts or motion-c
arXiv:2607.20533v1 Announce Type: cross Abstract: Portfolio construction under the Black-Litterman model requires investors to specify views on asset returns alongside explicit uncertainty estimates -
arXiv:2606.17028v2 Announce Type: replace-cross Abstract: Simple linear and frequency-domain models remain surprisingly competitive in long-horizon time-series forecasting, and recent mechanistic evid
arXiv:2607.20896v1 Announce Type: new Abstract: Spatial transcriptomics assays remain costly and technically demanding, restricting transcriptome-wide profiling to specialist settings and preventing r
arXiv:2607.21366v1 Announce Type: cross Abstract: Deep neural networks encode complex representations, but deconstructing this internal knowledge remains a challenge. Given the link between learning a
arXiv:2607.21208v1 Announce Type: new Abstract: Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge supports only conc
arXiv:2607.21188v1 Announce Type: new Abstract: The Single Constant Multiplication problem is a fundamental NP-hard optimization task in hardware design, which seeks to decompose a fixed constant usin
arXiv:2607.21517v1 Announce Type: cross Abstract: The Shannon capacity Theta(G) of a graph G quantifies the maximum rate at which information can be transmitted with zero error over a noisy channel. I
arXiv:2607.21368v1 Announce Type: new Abstract: Multi-object tracking in dense crowds requires solving a bipartite assignment problem between detections and trajectories at every video frame. The clas
arXiv:2607.20502v1 Announce Type: new Abstract: To allow for principled comparison between two probabilistic graphical models defined over non-identical variable sets, they have to be lifted to a comm
arXiv:2512.10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases
arXiv:2607.20426v1 Announce Type: cross Abstract: Existing LLM hallucination mitigation methods, including prompt engineering and model optimization, either hardly alter models'internal knowledge or h
arXiv:2607.20989v1 Announce Type: new Abstract: Acoustic impedance imaging is a fundamental yet severely ill-posed problem in subsurface analysis: the seismic wavelet is unknown, observations are band
Long-horizon execution in Large Language Models (LLMs) remains unstable even when high-level strategies are provided. Evaluating on controlled algorithmic puzzles, we demonstrate that while decomposit
arXiv:2607.21213v1 Announce Type: new Abstract: Digital sewing patterns typically consist of disjoint 2D panels without explicit stitch annotations, making downstream 3D modeling reliant on labor-inte
arXiv:2604.00004v2 Announce Type: replace-cross Abstract: The extension of context windows in Large Language Models is typically facilitated by scaling positional encodings followed by lightweight Con
arXiv:2607.21233v1 Announce Type: new Abstract: Motivated by challenging modelling issues in the life sciences, we investigate the relationship between logic programming semantics and the eventual sta
arXiv:2607.21414v1 Announce Type: new Abstract: In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a particular formul
arXiv:2507.13543v5 Announce Type: replace-cross Abstract: We develop a framework for dualizing the Kolmogorov structure function h_x(alpha), which then allows using computable complexity proxies. We e
arXiv:2607.21089v1 Announce Type: new Abstract: Semantic segmentation of 3D point clouds faces severe class imbalance, yet the effectiveness of specialized imbalance-aware methods from 2D computer vis
arXiv:2607.20897v1 Announce Type: new Abstract: Age estimation from finger vein images has been widely considered impractical due to severe demographic biases in public datasets and physiological conf
arXiv:2607.20462v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output
arXiv:2607.20642v1 Announce Type: new Abstract: Computer aided design (CAD) is ubiquitous: virtually any modern object was designed using editable CAD tools. However, with the shortage of available CA
arXiv:2607.20792v1 Announce Type: new Abstract: Memoir combines per-sample fast memory, shared slow parameters, variable-depth latent recurrence, and a future-latent energy objective. We test its risk
arXiv:2607.21552v1 Announce Type: new Abstract: Unlike large language models (LLMs) that exhibit strong reasoning capabilities, vision-language models (VLMs) struggle with visual reasoning, even on ge
arXiv:2607.20557v1 Announce Type: cross Abstract: Scientific discovery is increasingly shifting from isolated disciplines to multi-domain reasoning, and AI for science faces a similar transition. Exis
arXiv:2607.20429v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate diverse human opinions in open-ended tasks such as synthetic surveys, focus group modeling, an
arXiv:2607.21290v1 Announce Type: cross Abstract: Multi-task learning (MTL) is a promising approach for prediction tasks derived from video game state data, as modern game telemetry provides multiple
arXiv:2607.20857v1 Announce Type: cross Abstract: Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse p
arXiv:2602.08792v2 Announce Type: replace-cross Abstract: The pantograph-catenary interface is essential for ensuring uninterrupted and reliable power delivery in electrified rail systems. However, el
arXiv:2510.12430v2 Announce Type: replace-cross Abstract: Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, come
arXiv:2607.21548v1 Announce Type: cross Abstract: The coupled ghost and gluon Dyson--Schwinger equations (DSEs) of four-dimensional Landau-gauge Yang--Mills (YM) theory are solved with a neural repres
arXiv:2607.20811v1 Announce Type: new Abstract: In spite of the fundamental role of neural networks in contemporary machine learning research, our understanding of the computational complexity of opti
arXiv:2607.21284v1 Announce Type: new Abstract: Extracting structured content from news pages remains challenging due to heterogeneous HTML layouts, inconsistent markup, and substantial boilerplate su
arXiv:2607.21080v1 Announce Type: new Abstract: Long-horizon weather forecasting is a fundamental challenge in atmospheric science, for which autoregressive Deep Learning Weather Prediction (DLWP) has
arXiv:2603.04691v2 Announce Type: replace Abstract: Stopwords and function words are relatively less informative for the content of a language and more often play a structural role in a sentence. Stop
arXiv:2607.20790v1 Announce Type: new Abstract: Virtual reality (VR) headsets (e.g., Meta Quest, Apple Vision Pro) provide a seamless user experience due to their fast, frictionless interaction with t
arXiv:2607.20834v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets. However, scaling these
arXiv:2510.16703v3 Announce Type: replace-cross Abstract: The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables. In this paper, we consider the i
arXiv:2607.20506v1 Announce Type: new Abstract: GraphRAG enables deeper reasoning by structuring knowledge as graphs but struggles with n-ary facts. HyperGraphRAG uses hypergraphs for richer semantics
arXiv:2607.21179v1 Announce Type: new Abstract: The goal of this paper is to reduce the input token cost of Omni-modal large language models (Omni-LLMs) at inference time. Omni-LLMs reason jointly ove
arXiv:2607.21318v1 Announce Type: cross Abstract: Replacing an object with one that differs in category or shape requires complete source removal, natural target formation unconstrained by the source