Knowledge catalogue
arXiv:2506.11653v3 Announce Type: replace-cross Abstract: Dataset bias often leads deep learning models to exploit spurious correlations instead of task-relevant signals. We introduce the Standard Ant
arXiv:2602.10324v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly deployed in social and strategic scenarios, it becomes critical to understand where and why their b
arXiv:2605.30769v1 Announce Type: new Abstract: A key challenge in Visual Place Recognition (VPR) is matching query images against reference maps captured under diverse environmental conditions and vi
arXiv:2605.30915v1 Announce Type: new Abstract: Real-world images rarely suffer from a single degradation, and the order in which degradations are removed substantially affects the final restoration q
arXiv:2605.30876v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance whil
arXiv:2605.31455v1 Announce Type: cross Abstract: Large language models are increasingly deployed in multi-turn interactive settings where users or environments can iteratively provide lightweight fee
arXiv:2605.31065v1 Announce Type: cross Abstract: Non-terrestrial networks (NTNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling ubiquitous connectivity and massive c
arXiv:2605.30431v1 Announce Type: new Abstract: Recent progress in video diffusion models has enabled remarkable generative fidelity, yet leveraging these priors for restoration remains limited by the
arXiv:2512.13996v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts architectures are essential for scaling model capacity efficiently, yet the standard Top-k routing imposes a rigid sparsit
arXiv:2605.31377v1 Announce Type: cross Abstract: Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods
arXiv:2605.30753v1 Announce Type: new Abstract: Diffusion-based large language models (dLLMs) support parallel text generation via iterative denoising, yet inference remains latency-heavy because many
arXiv:2605.31557v1 Announce Type: new Abstract: Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchma
arXiv:2605.30992v1 Announce Type: new Abstract: Sparse Mixture of Experts (SMoE) architectures improve the training efficiency of Large Language Models (LLMs) by routing input tokens to a selected sub
arXiv:2605.30484v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing ac
arXiv:2605.30690v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. Howeve
arXiv:2503.05846v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric train
arXiv:2605.30928v1 Announce Type: new Abstract: Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward
arXiv:2605.31266v1 Announce Type: cross Abstract: The layout-to-image (L2I) task enables fine-grained control over image generation via object categories and spatial layouts. However, existing L2I met
arXiv:2605.31251v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to th
arXiv:2605.30654v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but t
arXiv:2605.30372v1 Announce Type: cross Abstract: Reservoir computing, a type of recurrent neural network, is a promising approach for temporal learning as it separates dynamic processing from the tra
arXiv:2510.04500v2 Announce Type: replace Abstract: This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves perform
arXiv:2510.16138v2 Announce Type: replace Abstract: Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert p
arXiv:2605.30712v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scr
arXiv:2506.01467v3 Announce Type: replace Abstract: Graph generative models perform well on small structured data but struggle to scale to large, complex structures. Hierarchical approaches improve sc
arXiv:2605.31446v1 Announce Type: cross Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to identify aspect terms, opinion terms, and sentiment polarities as structured triplets, providing es
arXiv:2605.31189v1 Announce Type: new Abstract: Tabular prediction in high-stakes domains requires models that are accurate, transparent, and robust to imperfect inputs. We propose FlagGAM, a rule-def
arXiv:2601.22787v2 Announce Type: replace Abstract: Post-training compression is currently divided into two contrasting regimes. On the one hand, fast, data-free, and model-agnostic methods (e.g., NF4
arXiv:2605.31145v1 Announce Type: cross Abstract: In-context localization (ICL) seeks to localize a target object specified by a small set of support examples in a query image, operating on the fly wi
arXiv:2605.30858v1 Announce Type: new Abstract: Agentic forecasting is important for decision-making in dynamic environments, but it remains challenging because agents must reason from incomplete, tim
arXiv:2605.31063v1 Announce Type: cross Abstract: Free energy estimation is a fundamental yet challenging problem, from physics to statistics. Classical approaches rely on thermodynamic transformation
arXiv:2605.31146v1 Announce Type: cross Abstract: This paper investigates how User Experience Research (UXR) methods can be combined with AI-supported analysis to develop clearer design direction for
arXiv:2601.19448v2 Announce Type: replace Abstract: Deep Neural Networks remain inherently vulnerable to backdoor attacks. Traditional test-time defenses largely operate under the paradigm of internal
arXiv:2605.31187v1 Announce Type: new Abstract: Detecting covariate shift is critical for building reliable vision systems. While most prior work focuses on improving robustness to shift, explicitly d
arXiv:2605.30375v1 Announce Type: cross Abstract: High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraf
arXiv:2605.30819v1 Announce Type: new Abstract: Most text-driven 3D indoor scene synthesis methods generate rooms from object-centric prompts, asking what furniture should be placed rather than how th
arXiv:2603.10468v2 Announce Type: replace-cross Abstract: We study timestamped speaker-attributed automatic speech recognition (SA-ASR) for long-form, multi-party speech with overlap. In this setting,
arXiv:2605.30374v1 Announce Type: new Abstract: Estimating hip muscle forces and joint moments during gait typically relies on musculoskeletal simulation, which is informative but time-consuming and d
arXiv:2605.30818v1 Announce Type: cross Abstract: Non-contact material identification enables adaptive interaction for embodied intelligence yet faces challenges from geometry-induced variations (e.g.
arXiv:2509.14221v3 Announce Type: replace-cross Abstract: Generative Engine Marketing (GEM) is an emerging ecosystem for monetizing generative engines, such as LLM-based chatbots, by seamlessly integr
arXiv:2605.31318v1 Announce Type: new Abstract: Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learn
arXiv:2605.30747v1 Announce Type: new Abstract: Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns. Ho
arXiv:2605.30984v1 Announce Type: cross Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and output d
arXiv:2605.30453v1 Announce Type: cross Abstract: Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and den
arXiv:2605.31277v1 Announce Type: cross Abstract: Traditional traffic analysis is being fundamentally challenged by the rapid adoption of encryption, tunnelling, and privacy-preserving protocols, whic
arXiv:2605.30486v1 Announce Type: cross Abstract: Spatio-temporal forecasting on sensor graphs is commonly tackled with a single backbone architecture applied uniformly across all nodes, although grap
arXiv:2603.23398v2 Announce Type: replace-cross Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and
arXiv:2605.31031v1 Announce Type: new Abstract: Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce Gr
arXiv:2605.30884v1 Announce Type: new Abstract: Existing agentic reinforcement learning methods for GUI grounding have limitations at two levels. At the data level, current approaches typically treat
arXiv:2605.31162v1 Announce Type: new Abstract: Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored. We
arXiv:2605.31234v1 Announce Type: new Abstract: Learning generalizable vision-language-action (VLA) models from large-scale human videos is promising but challenging due to cross-embodiment discrepanc
Nous Research announced native support for Hermes Agent on Windows, expanding the availability of their Hermes model to Windows-based systems. This development enables Windows users to run Hermes Agen
arXiv:2511.18760v2 Announce Type: replace Abstract: Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and efficient construction of arguments.
arXiv:2605.30834v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnera
arXiv:2602.16165v2 Announce Type: replace-cross Abstract: Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and del
Processing millions of user reviews in real-time, under strict latency and cost constraints, is no easy task. Trustpilot has been doing exactly that with custom machine learning since long before larg
arXiv:2605.31186v1 Announce Type: new Abstract: Data streams are nowadays among the most frequently analyzed data structures, with the concept drift posing a major challenge encountered by processing
arXiv:2605.31068v1 Announce Type: new Abstract: We introduce HQ-JEPA, a hybrid quantum-classical joint-embedding predictive architecture for cross-modal remote sensing representation learning. The pro
arXiv:2509.19452v4 Announce Type: replace-cross Abstract: Search and rescue operations require unmanned aerial vehicles to both traverse unknown unstructured environments at high speed and track targe
arXiv:2605.30939v1 Announce Type: new Abstract: Semantic road segmentation is important for autonomous driving, but existing methods suffer severe performance degradation under low-light conditions. M