Knowledge catalogue
arXiv:2211.16780v3 Announce Type: replace-cross Abstract: In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because pre
arXiv:2604.13251v1 Announce Type: new Abstract: Analog optical computers promise large efficiency gains for machine learning inference, yet no demonstration has moved beyond small-scale image benchmar
arXiv:2604.13856v1 Announce Type: new Abstract: Reconstructing a complete 3D head from a single portrait remains challenging because existing methods still face a sharp quality-speed trade-off: high-f
arXiv:2604.13795v1 Announce Type: new Abstract: Vision transformers (ViT) have been shown to allow for more flexible feature detection and can outperform convolutional neural network (CNN) when pre-tr
arXiv:2604.13924v1 Announce Type: cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging d
arXiv:2604.13938v1 Announce Type: new Abstract: Subject-driven image generation has shown great success in creating personalized content, but its capabilities are largely confined to single subjects i
arXiv:2604.13459v1 Announce Type: new Abstract: Turbofan engine degradation under sustained operational stress necessitates robust prognostic systems capable of accurately estimating the Remaining Use
arXiv:2503.10522v4 Announce Type: replace-cross Abstract: Audio and music generation based on flexible multimodal control signals is a widely applicable topic, with the following key challenges: 1) a
arXiv:2310.02540v2 Announce Type: replace Abstract: Classical machine learning models, such as linear models and tree-based models, are widely used in industry. These models are sensitive to data dist
arXiv:2604.13133v1 Announce Type: new Abstract: Thermodynamic cycles are pivotal in determining the efficacy of energy conversion systems. Traditional design methodologies, which rely on expert knowle
arXiv:2604.13662v1 Announce Type: cross Abstract: Tuning of gate-defined semiconductor quantum dots (QDs) is a major bottleneck for scaling spin qubit technologies. We present a deep learning (DL) dri
arXiv:2604.13666v1 Announce Type: cross Abstract: Assessing teachers' geometric content knowledge is essential for geometry instructional quality and student learning, but difficult to scale. The Van
arXiv:2507.16005v2 Announce Type: replace-cross Abstract: Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations an
arXiv:2604.13068v1 Announce Type: new Abstract: When do large language models decide to hallucinate? Despite serious consequences in healthcare, law, and finance, few formal answers exist. Recent work
arXiv:2604.13583v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for legal reasoning requires workflows that span task design, expert annotation, model execution, and metric-bas
arXiv:2604.13275v1 Announce Type: new Abstract: Larger language models become simultaneously better and worse at handling contextual information -- better at ignoring false claims, worse at ignoring i
arXiv:2604.13705v1 Announce Type: new Abstract: Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agen
arXiv:2604.13891v1 Announce Type: new Abstract: Automated driving at unsignalized intersections is challenging due to complex multi-vehicle interactions and the need to balance safety and efficiency.
arXiv:2604.13824v1 Announce Type: new Abstract: World models have been emerging as critical components for assessing the consequences of actions generated by interactive agents in online planning and
arXiv:2604.13846v1 Announce Type: new Abstract: Personalized Large Language Models (LLMs) facilitate more natural, human-like interactions in human-centric applications. However, existing personalizat
arXiv:2604.13316v1 Announce Type: new Abstract: Neural operators have emerged as fast surrogate models for physics simulations, yet they remain acutely vulnerable to adversarial perturbations, a criti
arXiv:2604.13688v1 Announce Type: new Abstract: 3D editing refers to the ability to apply local or global modifications to 3D assets. Effective 3D editing requires maintaining semantic consistency by
arXiv:2604.13061v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in high-stakes autonomous and interactive workflows, where reliability demands continuous, multi-
arXiv:2604.13305v1 Announce Type: new Abstract: Reward models (RMs) are inherently non-neutral value functions designed and trained to encode specific objectives, such as human preferences or text-ima
arXiv:2604.13253v1 Announce Type: new Abstract: Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with asymptotic coverage guarantees for time series under distributio
arXiv:2503.06078v2 Announce Type: replace Abstract: Federated learning (FL) has emerged as a promising framework for distributed learning, enabling collaborative model training without sharing private
arXiv:2604.13263v1 Announce Type: new Abstract: Meta-learning offers a principled framework leveraging task-invariant priors from related tasks, with which task-specific models can be fine-tuned on do
arXiv:2604.13359v1 Announce Type: new Abstract: Biosignals exhibit substantial cross-subject and cross-session variability, inducing severe domain shifts that degrade post-deployment performance for s
arXiv:2604.13906v1 Announce Type: new Abstract: Bitstream-corrupted video recovery aims to restore realistic content degraded during video storage or transmission. Existing methods typically assume th
arXiv:2604.13980v1 Announce Type: new Abstract: Antibody lead optimization is inherently a multi-objective challenge in drug discovery. Achieving a balance between different drug-like properties is cr
arXiv:2604.13325v1 Announce Type: new Abstract: Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional
arXiv:2604.13692v1 Announce Type: new Abstract: As large language models (LLMs) generate text that increasingly resembles human writing, the subtle cues that distinguish AI-generated content from huma
arXiv:2506.20083v4 Announce Type: replace Abstract: Integrating compositional and symbolic properties into current distributional semantic spaces can enhance the interpretability, controllability, com
arXiv:2604.13472v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) is widely used to address large joint observation and action spaces by decomposing a centralized c
arXiv:2604.13521v1 Announce Type: new Abstract: Neural network models with latent recurrent processing, where identical layers are recursively applied to the latent state, have gained attention as pro
arXiv:2604.13618v1 Announce Type: new Abstract: Rubric-augmented verification guides reward models with explicit evaluation criteria, yielding more reliable judgments than single-model verification. H
arXiv:2604.13098v1 Announce Type: cross Abstract: State-of-the-art (SOTA) urban traffic control increasingly employs Multi-Agent Reinforcement Learning (MARL) to coordinate Traffic Light Controllers (
arXiv:2604.13634v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by letting draft tokens bypass full verification, but conventional frameworks suffer from fre
arXiv:2604.13107v1 Announce Type: cross Abstract: As coding agents have seen rapid capability and adoption gains, users are applying them to general tasks beyond software engineering. In this post, we
arXiv:2604.13304v1 Announce Type: new Abstract: Understanding the internal activations of Vision Transformers (ViTs) is critical for building interpretable and trustworthy models. While Sparse Autoenc
arXiv:2604.13077v1 Announce Type: new Abstract: Coronary angiography (CAG) reports contain clinically relevant physiological measurements, yet this information is typically in the form of unstructured
arXiv:2604.13452v1 Announce Type: new Abstract: Long-form visual storytelling requires maintaining continuity across shots, including consistent characters, stable environments, and smooth scene trans
arXiv:2604.13245v1 Announce Type: new Abstract: Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing dece
arXiv:2604.13054v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved rapid progress, yet their scaling behavior remains less clearly characterized and often less pred
arXiv:2604.13950v1 Announce Type: new Abstract: We show how causal interventions in Transformer models provide insights into English syntax by focusing on a long-standing challenge for syntactic theor
arXiv:2604.13409v1 Announce Type: new Abstract: In clinical practice, the robustness of deep learning models for multimodal brain tumor segmentation is severely compromised by incomplete MRI data. Thi
arXiv:2604.13504v1 Announce Type: cross Abstract: Designing effective reward functions is a cornerstone of reinforcement learning (RL), yet it remains a challenging and labor-intensive process due to
arXiv:2604.13804v1 Announce Type: new Abstract: The rapid evolution of multimodal large models has revolutionized the simulation of diverse characters in speech dialogue systems, enabling a novel inte
arXiv:2603.27064v2 Announce Type: replace-cross Abstract: Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a ca
arXiv:2604.13561v1 Announce Type: new Abstract: Vision-language models trained with contrastive learning on paired medical images and reports show strong zero-shot diagnostic capabilities, yet the eff
arXiv:2604.13746v1 Announce Type: new Abstract: Dynamic 3D scene reconstruction is essential for immersive media such as VR, MR, and XR, yet remains challenging for long multi-view sequences with larg
arXiv:2604.13706v1 Announce Type: new Abstract: Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reasoning
arXiv:2504.21751v4 Announce Type: replace-cross Abstract: Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components
arXiv:2512.20481v4 Announce Type: replace-cross Abstract: To maintain coherence in language, the brain must satisfy key competing temporal demands: the gradual accumulation of meaning across extended
arXiv:2604.13946v1 Announce Type: cross Abstract: Automated code generation remains a persistent challenge in software engineering, as conventional multi-agent frameworks are often constrained by stat
arXiv:2601.15550v3 Announce Type: replace Abstract: Existing cultural commonsense benchmarks treat nations as monolithic, assuming uniform practices within national boundaries. But does cultural commo
arXiv:2604.14118v1 Announce Type: new Abstract: Given two symmetric positive-definite matrices A, B in R^{n imes n}, we study the spectral properties of the interpolation A^{1-x} B^x for 0 leq x leq 1
arXiv:2604.13816v1 Announce Type: new Abstract: Determining the number of clusters is a central challenge in unsupervised learning, where ground-truth labels are unavailable. The Silhouette coefficien
arXiv:2604.13471v1 Announce Type: new Abstract: In silico tools are important for generating novel hypotheses and exploring alternatives in de novo metabolic pathway design. However, while many comput
arXiv:2604.13313v1 Announce Type: new Abstract: Vision-Language Models demonstrate remarkable capabilities but often struggle with compositional reasoning, exhibiting vulnerabilities regarding word or