Knowledge catalogue
arXiv:2605.09514v1 Announce Type: new Abstract: Unobserved confounding prevents standard covariate adjustment from identifying causal response functions in observational studies. Proxy causal learning
arXiv:2605.10272v1 Announce Type: cross Abstract: Federated learning (FL) enables the collaborative training of large-scale language models (LLMs) across edge devices while keeping user data on-device
arXiv:2605.10436v1 Announce Type: cross Abstract: Domain Generation Algorithms (DGAs) evolve continuously to evade botnet detection, posing a persistent challenge for dependable network defense. While
arXiv:2605.09537v1 Announce Type: new Abstract: Despite rapid progress in Vision-Language-Action (VLA) models for robotic control, instruction drift remains a persistent failure mode in long-horizon t
arXiv:2605.09774v1 Announce Type: new Abstract: DRIVE-C is a controlled corruption dataset designed to evaluate visual perception robustness in autonomous driving systems. It is built from real-world
arXiv:2605.09701v1 Announce Type: new Abstract: Existing latent world models for autonomous driving have opened a promising path toward future-aware driving intelligence. However, they typically treat
arXiv:2605.08627v1 Announce Type: new Abstract: All-in-one image restoration aims to handle diverse degradations within a single model. However, existing methods often suffer from three key limitation
arXiv:2605.10281v1 Announce Type: cross Abstract: Generating realistic drum audio directly from symbolic representations is a challenging task at the intersection of music perception and machine learn
arXiv:2605.09347v1 Announce Type: new Abstract: Discrete variables are common in many applications, such as probabilistic reasoning, planning and explainable AI. When symbolic reasoning techniques are
arXiv:2503.06047v2 Announce Type: replace Abstract: Large language model (LLM)-based agents are increasingly applied to complex strategic environments that demand long-horizon reasoning, multi-agent i
arXiv:2605.09566v1 Announce Type: new Abstract: Recent Deep Unfolding Networks (DUNs) have significantly advanced Compressive Sensing (CS) by integrating iterative optimization with deep networks. How
arXiv:2603.04415v2 Announce Type: replace Abstract: Reasoning post-training improves Large Language Models (LLMs) on complex tasks such as mathematics and coding, but its benefits across diverse multi
arXiv:2512.23964v2 Announce Type: replace-cross Abstract: Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical fl
arXiv:2605.08441v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) generates hundreds of thousands of tokens per training step, with rollout generation dominating
arXiv:2605.10521v1 Announce Type: cross Abstract: Medical image segmentation models can perform unevenly across subgroups. Most existing fairness methods focus on improving average subgroup performanc
arXiv:2605.10765v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual cap
arXiv:2604.05064v2 Announce Type: replace-cross Abstract: Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typ
arXiv:2605.09098v1 Announce Type: new Abstract: We propose Dynamic Meta-Metrics (DMM), a framework for machine translation evaluation that learns source-sentence conditioned combinations of existing m
arXiv:2605.10923v1 Announce Type: cross Abstract: Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilit
arXiv:2602.06550v2 Announce Type: replace-cross Abstract: Zero-shot generalization in contextual reinforcement learning remains a core challenge, particularly when the context is latent and must be in
arXiv:2605.10770v1 Announce Type: new Abstract: Multi-domain fine-tuning of large language models requires improving performance on target domains while preserving performance on constrained domains,
arXiv:2605.10185v1 Announce Type: cross Abstract: Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensi
arXiv:2605.09820v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive models, primarily due to their ability to enable par
arXiv:2605.10360v1 Announce Type: new Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains a cha
arXiv:2605.10261v1 Announce Type: new Abstract: TCAV (Testing with Concept Activation Vectors) is an interpretability method that assesses the alignment between the internal representations of a train
arXiv:2505.05209v4 Announce Type: replace Abstract: Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. Whi
arXiv:2605.08723v1 Announce Type: new Abstract: Weakly supervised Audio-Visual Video Parsing (AVVP) aims to recognize and temporally localize audio, visual, and audio-visual events in videos using onl
arXiv:2605.08177v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a practical route for adapting large language models to downstream tasks, with LoRA-style methods be
arXiv:2405.12969v3 Announce Type: replace Abstract: Noisy labels severely hinder the accuracy and generalization of machine learning models, especially when ambiguous instance features make reliable a
arXiv:2510.19414v2 Announce Type: replace-cross Abstract: The growing prevalence of speech deepfakes has raised serious concerns, particularly in real-world scenarios such as telephone fraud and ident
arXiv:2605.10050v1 Announce Type: new Abstract: Long-form video understanding remains challenging for Video Large Language Models (VideoLLMs), as the dense frame sampling introduces massive visual tok
arXiv:2602.09514v3 Announce Type: replace-cross Abstract: Long-horizon planning is widely recognized as a core capability of autonomous LLM-based agents; however, current evaluation frameworks suffer
arXiv:2506.08136v3 Announce Type: replace Abstract: We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The ben
arXiv:2605.06644v2 Announce Type: replace Abstract: Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identit
arXiv:2605.08636v1 Announce Type: new Abstract: Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continu
arXiv:2605.09603v1 Announce Type: new Abstract: Masked diffusion language models enable parallel token generation and offer improved decoding efficiency over autoregressive models. However, their perf
arXiv:2605.08695v1 Announce Type: new Abstract: Forensic analysis of AI-edited images requires more than binary real-versus-fake prediction: a useful system should localize the edit, identify its sema
arXiv:2605.09378v1 Announce Type: cross Abstract: Long-horizon video generation has advanced in visual quality, yet existing methods still struggle to maintain knowledge consistency and coherent pedag
arXiv:2605.08406v1 Announce Type: cross Abstract: Explaining how to get from A to B can be challenging. It requires mentally simulating what the listener will do based on what they are told. To captur
arXiv:2602.05391v2 Announce Type: replace Abstract: Dataset distillation seeks to synthesize a highly compact dataset that achieves performance comparable to the original dataset on downstream tasks.
arXiv:2605.09588v1 Announce Type: cross Abstract: Organizations increasingly deploy multiple AI systems across task domains, but selecting a small, high-performing ensemble can require costly model ca
arXiv:2602.03783v2 Announce Type: replace-cross Abstract: Modern AI agents such as large language models are trained on diverse tasks -- translation, code generation, mathematical reasoning, and text
arXiv:2601.20251v3 Announce Type: replace-cross Abstract: Exhaustively evaluating many large language models (LLMs) on a large suite of benchmarks is expensive. We cast benchmarking as finite-populati
arXiv:2605.10251v1 Announce Type: new Abstract: We present GraphDepth, a monocular depth estimation architecture that synergistically integrates Graph Neural Networks (GNNs) within a convolutional enc
arXiv:2506.11578v4 Announce Type: replace Abstract: Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achie
arXiv:2605.09801v1 Announce Type: new Abstract: Solving multi-robot motion planning (MRMP) requires generating collision-free kinodynamically feasible trajectories for multiple interacting robots. We
arXiv:2605.09848v1 Announce Type: new Abstract: Electrocardiogram (ECG) interpretation is essential for diagnosing a wide range of cardiac abnormalities. While deep learning has shown strong potential
arXiv:2605.08273v1 Announce Type: cross Abstract: Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administrat
arXiv:2410.01656v2 Announce Type: replace-cross Abstract: We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set S subseteq R^d. Kontonis, Tz
arXiv:2605.10063v1 Announce Type: new Abstract: Learning dynamic whole-body motions for legged robots through reinforcement learning (RL) remains challenging due to the high risk of failure, which mak
arXiv:2605.10366v1 Announce Type: new Abstract: Graph reasoning agents operating from natural-language inputs must solve a coupled problem: they must reconstruct a structured graph instance from text,
arXiv:2605.08606v1 Announce Type: new Abstract: Egocentric human mesh recovery (HMR) from monocular head-mounted cameras is increasingly important for AR/VR applications, but remains challenging due t
arXiv:2605.09874v1 Announce Type: cross Abstract: Next-generation visual assistants, such as smart glasses, embodied agents, and always-on life-logging systems, must reason over an entire day or more
arXiv:2509.21892v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models typically fix the number of activated experts k at both training and inference. However, real-world deployment
arXiv:2605.08799v1 Announce Type: new Abstract: Diffusion policies have demonstrated exceptional performance in embodied AI. However, their iterative denoising process results in high latency, and exi
arXiv:2605.10938v1 Announce Type: cross Abstract: Diffusion and flow-based models have become the de facto approaches for generating continuous data, e.g., in domains such as images and videos. Their
arXiv:2507.06658v2 Announce Type: replace-cross Abstract: Theories of democratic stability, populism, and party-system crisis often point to a form of polarization that comparative research rarely mea
arXiv:2605.10790v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success, yet their training remains inefficient due to a severe optimization bottleneck, which we term Represe
arXiv:2605.08377v1 Announce Type: new Abstract: In many practical applications it is important to build symmetries into neural network architectures. Consider the important case of permutation symmetr
arXiv:2605.08360v1 Announce Type: new Abstract: Modern AI is opening the door to collective decision-making in which participants express their views as free-form text rather than voting on a fixed se