Knowledge catalogue
arXiv:2603.15857v2 Announce Type: replace-cross Abstract: Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are onl
arXiv:2608.25221v1 Announce Type: new Abstract: We study exact representations of MAX_N(x)=max{x_1,ldots,x_N} using two-hidden-layer ReLU neural networks. This problem has been studied in recent years
arXiv:2608.25467v1 Announce Type: new Abstract: Multimodal regression suffers from the mean-collapse pathology: under squared loss, an unconstrained regressor converges to the conditional mean, which
arXiv:2608.25133v1 Announce Type: new Abstract: The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of machin
arXiv:2608.25017v1 Announce Type: new Abstract: A latent world model trains its decoder on latents anchored to observations, then deploys it on the model's own free-running rollout, hundreds of steps
arXiv:2608.25160v1 Announce Type: cross Abstract: Waveform inversion seeks to estimate the wave speed of a heterogeneous, inaccessible medium, from time-resolved measurements of the waves at user cont
arXiv:2608.25102v1 Announce Type: new Abstract: The Robot Operating System 2 (ROS2) has become a widely adopted framework for the development of distributed robotic systems. However, its communication
arXiv:2608.24893v1 Announce Type: cross Abstract: In competitive first-person shooter (FPS) games such as Counter-Strike 2 (CS2), account-integrity review often asks whether an account's recent behavi
arXiv:2608.25910v1 Announce Type: new Abstract: Machine Learning (ML) is increasingly used in virtual prototypes of embedded systems to model behaviors that are difficult to capture analytically. Howe
arXiv:2608.25095v1 Announce Type: cross Abstract: The proliferation of distributed energy resources (DERs) in distribution grids enables the active coordination of these assets to reduce costs and ena
arXiv:2505.23022v2 Announce Type: replace Abstract: Large Language Model (LLM) serving increasingly underpins online Web services such as conversational agents, Web search, and programming assistants,
arXiv:2603.16558v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on many multimodal tasks, but object hallucinations severely undermine their reliabi
arXiv:2608.25123v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves large language models by incorporating external knowledge without retraining, but existing methods often u
arXiv:2608.25819v1 Announce Type: new Abstract: Accurate 3D segmentation is central to quantitative lesion assessment and anatomy mapping for clinical planning and follow-up. Thin, elongated, and fine
arXiv:2608.25245v1 Announce Type: cross Abstract: LLM-generated search queries are widely used to augment IR evaluation, yet they may contain concepts that presuppose answer-side document knowledge, v
arXiv:2608.25084v1 Announce Type: new Abstract: We present a natively multiscale operator learning method for the surrogate modeling of (numerical solvers for) multiscale partial differential equation
arXiv:2603.02203v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for Recursive Self-Improving AI (RSI) by adapting Large Reasoning
arXiv:2608.25118v1 Announce Type: new Abstract: Machine learning is usually formalized through samples, while the persistent individual to which multiple observed or possible events refer often remain
arXiv:2608.25238v1 Announce Type: cross Abstract: In dynamic collaborative systems, the selection of reliable collaborators is critical to ensuring effective task execution. Existing trust evaluation
arXiv:2608.25326v1 Announce Type: new Abstract: In transductive classification, an adversary fixes a labeled population, one label is hidden uniformly, and the learner sees all remaining labels. For b
arXiv:2608.26033v1 Announce Type: new Abstract: In medical imaging, it is common to use learned perceptual image patch similarity (LPIPS) to compare images semantically in feature space. Although back
arXiv:2608.24982v1 Announce Type: new Abstract: Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-T
arXiv:2608.25944v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) makes it increasingly difficult to distinguish human writing from machine-generated text. Training
arXiv:2607.18098v2 Announce Type: replace Abstract: Large language models are increasingly used in practical systems, making efficient model selection important for reducing deployment cost. LLM routi
arXiv:2608.25478v1 Announce Type: new Abstract: In recent years, distinguishing between AI-generated text and human-written text has remained a challenge. In this paper, we introduce VietAIDetector, a
arXiv:2608.25841v1 Announce Type: new Abstract: Drug synergy prediction estimates whether two drugs produce a stronger joint effect than expected from their individual activities. For drug combination
arXiv:2608.25555v1 Announce Type: new Abstract: Explainability for transformer-based language models is becoming crucial as these systems are deployed in high-stakes applications. As a result, the eco
arXiv:2608.25924v1 Announce Type: new Abstract: This paper reconsiders intelligence from a vision-centered perspective and examines whether intelligence emerging from visual experience and learning ma
arXiv:2608.25157v1 Announce Type: new Abstract: Automatic AV-sync metrics are widely used to rank and train audio-visual generators, but they are rarely audited as measurement instruments. We jointly
arXiv:2608.25933v1 Announce Type: new Abstract: *Chulin Zhao and Ruoqi Hu contributed equally to this work. State-of-the-art text-to-image (T2I) models exhibit pronounced and systematic defects when p
arXiv:2608.25977v1 Announce Type: new Abstract: Personality is increasingly important in large language models (LLMs), as it shapes users' trust, engagement, and emotional experiences. While the Myers
arXiv:2608.25941v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are widely used to interpret the internal representations of large language models (LLMs), yet their reliability under post-h
arXiv:2608.25846v1 Announce Type: cross Abstract: Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics
arXiv:2608.24210v1 Announce Type: new Abstract: Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This abil
arXiv:1808.09334v4 Announce Type: replace-cross Abstract: We introduce a novel discriminative latent variable model for bilingual lexicon induction. Our model combines the bipartite matching dictionar
arXiv:2608.24242v1 Announce Type: new Abstract: Currently available commercial vision-based tactile sensors provide rich contact information but remain vulnerable to abrasion and repeated concentrated
arXiv:2608.23817v1 Announce Type: new Abstract: SHAP and LIME are now standard tools for interpreting black-box predictions, yet their outputs can vary substantially when the input is perturbed by sma
arXiv:2608.24419v1 Announce Type: new Abstract: LLM-as-a-judge evaluation is usually assessed by agreement and robustness to surface perturbations, but reliability does not establish construct validit
arXiv:2608.24644v1 Announce Type: cross Abstract: This paper introduces an environment for constructing literate programs in concert with language-aware machine agents. This environment includes a gra
arXiv:2608.23892v1 Announce Type: cross Abstract: This article presents the abridged core of A Mathematical Theory of Interpretation (MTI), which treats interpretation as observer-relative spectral me
arXiv:2511.09790v2 Announce Type: replace-cross Abstract: Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation ('task') space o
arXiv:2608.24328v1 Announce Type: new Abstract: In this work, we propose a structural variant of the Factorial Hidden Markov Model (FHMM) for the analysis of disease trajectories in patients with Type
arXiv:2608.23626v1 Announce Type: new Abstract: Foundation models for astronomy are trained on survey pixels together with the catalogue products derived from those pixels. Those catalogues are incomp
arXiv:2608.23798v1 Announce Type: new Abstract: Speciation in generative diffusion models denotes the emergence of distinct stable branches during denoising, through which initially undifferentiated t
arXiv:2601.19435v2 Announce Type: replace-cross Abstract: Sustainable monetization of large language models (LLMs) remains a critical open challenge. Traditional search advertising, which relies on st
arXiv:2402.11215v4 Announce Type: replace Abstract: The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-sca
arXiv:2502.21099v3 Announce Type: replace-cross Abstract: This paper proposes {sf AEPG-SPIDER}, an Adaptive Extrapolated Proximal Gradient (AEPG) method with variance reduction for minimizing composit
Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have adva
arXiv:2601.19509v2 Announce Type: replace Abstract: In loosely coupled strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integration, the body-frame DVL velocity is projected into t
arXiv:2608.23816v1 Announce Type: new Abstract: Quantized fine-tuning (QLoRA) saves memory but not time. It dequantizes every 4-bit weight on the fly, so it trains more slowly than fp16 LoRA. We prese
arXiv:2506.02825v3 Announce Type: replace-cross Abstract: We present the OmniMatch algorithm for seeded multiple graph matching. In the setting of d-dimensional Random Dot Product Graphs (RDPG), we pr
arXiv:2608.24219v1 Announce Type: new Abstract: Facial attractiveness has been linked to statistical regularities such as symmetry and averageness, suggesting that beauty may depend on the ease with w
arXiv:2608.24762v1 Announce Type: cross Abstract: Many disentanglement methods represent generative factors using Euclidean product coordinates, although the underlying factor spaces may wrap, collaps
arXiv:2608.23974v1 Announce Type: new Abstract: Breast ultrasound (BUS) is widely used for breast cancer diagnosis yet remains operator-dependent. While deep learning shows promise, ensuring diagnosti
arXiv:2608.23994v1 Announce Type: new Abstract: Human-robot teaching focuses on enabling nontechnical experts to customize robots according to their needs after deployment. With recent advances in mac
arXiv:2608.24319v1 Announce Type: new Abstract: An intelligent system does not merely reason: it governs its own reasoning - how much to compute, when to stop, which module to activate. Can that role
arXiv:2601.00282v2 Announce Type: replace-cross Abstract: Quantization is widely used to accelerate inference and streamline the deployment of large language models (LLMs), yet its effects on self-exp
arXiv:2608.24632v1 Announce Type: new Abstract: Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluat
arXiv:2608.24559v1 Announce Type: cross Abstract: Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from modern
arXiv:2608.24594v1 Announce Type: new Abstract: Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation r