Knowledge catalogue
arXiv:2605.30368v1 Announce Type: cross Abstract: Surrogate Safety Measures (SSMs) are extensively utilised in the evaluation of traffic risk in automated driving contexts. However, the majority of SS
arXiv:2605.31512v1 Announce Type: new Abstract: Multilingual orthopedic decision support remains challenging in low-resource healthcare settings, where clinical narratives contain specialized terminol
arXiv:2603.21558v2 Announce Type: replace Abstract: Self-improvement training, where models learn from self-generated solutions, promises sustained capability gains but suffers from a pervasive failur
arXiv:2605.31108v1 Announce Type: new Abstract: In this work we introduce a novel approach to domain incremental learning, adapting models over time to evolving, non-stationary data. In contrast to ot
arXiv:2605.30524v1 Announce Type: new Abstract: Large language models are now adapted through chains of post-training stages rather than through a single instruction-tuning pass. This paper studies wh
arXiv:2605.31604v1 Announce Type: new Abstract: Unified multimodal models (UMMs) aim to handle perception and generation in a single model. Yet existing UMMs still rely on a frozen, separately pretrai
arXiv:2508.09925v3 Announce Type: replace-cross Abstract: We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservo
arXiv:2510.02060v2 Announce Type: replace Abstract: In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specifi
arXiv:2605.31048v1 Announce Type: new Abstract: Recent crack segmentation methods often follow generic semantic segmentation designs, using stronger backbones, hybrid CNN-Transformer-Mamba encoders, a
arXiv:2601.01456v2 Announce Type: replace-cross Abstract: In this paper, we revisit multimodal few-shot 3D point cloud semantic segmentation (FS-PCS), identifying a conflict in 'Fuse-then-Refine' para
arXiv:2503.22996v3 Announce Type: replace Abstract: Sparse Mixture of Experts (SMoE) models scale the capacity of models while maintaining constant computational overhead. SMoE methods fall into two c
arXiv:2605.31176v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems typically rely on a single retriever and a single set of hyperparameters, despite facing highly heterogeneo
arXiv:2605.30523v1 Announce Type: cross Abstract: Recent work describes what transformers can and cannot compute through connections to boolean circuits, but existing results lack exact characterizati
arXiv:2602.21620v2 Announce Type: replace-cross Abstract: We study the discrete Bertrand pricing game with a non-increasing demand function. The game has n ge 2 players who simultaneously choose price
arXiv:2605.30960v1 Announce Type: new Abstract: Accurate Zeroth-Order (ZO) Hessian estimation is a cornerstone of derivative-free methods, essential for tasks such as bilevel optimization, Bayesian in
arXiv:2605.30619v1 Announce Type: cross Abstract: Best-of-N sampling is widely used to construct pairwise preference data: N candidates are drawn from a base distribution, and the best is paired with
arXiv:2605.31106v1 Announce Type: new Abstract: Riemannian diffusion models generalize score-based generative modeling to manifold-supported data via stochastic diffusion equations on the manifold. Ho
arXiv:2605.30855v1 Announce Type: new Abstract: Frame-wise action-controlled image-to-video generation is a promising paradigm for interactive world simulation, where each control signal should elicit
arXiv:2605.31043v1 Announce Type: cross Abstract: Cross-domain EEG decoding remains challenging despite advances in Riemannian deep learning: covariance matrices from different subjects occupy systema
arXiv:2605.31311v1 Announce Type: cross Abstract: Networked AI systems increasingly rely on multiple agents that collaboratively learn and adapt models over communication networks. In such systems, bi
arXiv:2510.05115v3 Announce Type: replace Abstract: Large language models (LLMs) have opened new paradigms in optimization modeling by enabling the generation of executable solver code from natural la
arXiv:2509.21379v3 Announce Type: replace-cross Abstract: Concept unlearning in diffusion models is hampered by feature splitting, where concepts are distributed across many latent features, making th
arXiv:2605.30854v1 Announce Type: cross Abstract: Language models fine-tuned with reinforcement learning typically optimize for task reward, ignoring multi-agent strategic structure. Because these age
arXiv:2412.03876v2 Announce Type: replace Abstract: Text-to-Image (T2I) diffusion models are widely recognized for their ability to generate high-quality and diverse images based on text prompts. Howe
arXiv:2605.30711v1 Announce Type: cross Abstract: Agentic LLMs must continuously decide whether newly extracted facts should be added, merged with existing memories, or ignored, yet prior work has foc
arXiv:2602.00942v3 Announce Type: replace Abstract: Modern large language models are increasingly deployed under compute and memory constraints, making flexible control of model capacity a central cha
arXiv:2605.31284v1 Announce Type: cross Abstract: The morphological analysis of mitochondria in fluorescence microscopy (FM) is crucial for understanding cellular health, energy production, and metabo
arXiv:2605.30646v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic var
arXiv:2605.30409v1 Announce Type: cross Abstract: Real-time streaming video-to-video editing (V2V) is critical for interactive applications such as live broadcasting and gaming, yet it remains a formi
arXiv:2602.16682v2 Announce Type: replace Abstract: A core aspect of human perception is situated awareness, the ability to relate ourselves to the surrounding physical environment and reason over pos
arXiv:2605.31127v1 Announce Type: new Abstract: Nonlinear conservation laws are at the heart of many of the most important dynamical systems in science and engineering. In practical applications, such
arXiv:2605.30461v1 Announce Type: cross Abstract: We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL) that combines state-augmented policy learning with distrib
arXiv:2605.31498v1 Announce Type: new Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generat
arXiv:2601.22943v2 Announce Type: replace Abstract: Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characte
arXiv:2605.30597v1 Announce Type: new Abstract: Nonlinear dimensionality-reduction methods such as UMAP and PaCMAP adaptively normalize local distances during graph construction, erasing neighborhood
arXiv:2605.31469v1 Announce Type: cross Abstract: Conversational automatic speech recognition in Hungarian is constrained by the limited amount of publicly available dialogue-style training data. The
arXiv:2605.31373v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressi
arXiv:2511.03100v2 Announce Type: replace-cross Abstract: The agent-environment co-design paradigm jointly optimises agent policies and environment configurations in search of improved system performa
arXiv:2605.31238v1 Announce Type: new Abstract: Endowing large language models with compositional reasoning over specialized documents requires multi-hop training data at scale, where such data rarely
arXiv:2605.30593v1 Announce Type: cross Abstract: Engine Health Management (EHM) depends on reliable forecasting of Remaining Useful Life (RUL) and on tracking thermal indicators such as turbine gas t
arXiv:2602.13110v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as scalable judges in pairwise evaluation, but they remain prone to miscalibration and bias
arXiv:2605.31433v1 Announce Type: new Abstract: Self-play can train language models without external supervision. However, existing methods require rule-checkable answers, leaving open-ended tasks dep
arXiv:2605.30638v1 Announce Type: cross Abstract: We introduce Score Broadcast and Decorrelation (SBD), a principled framework for broadcast-based credit assignment for general families of differentia
arXiv:2402.17672v2 Announce Type: replace Abstract: Polarimetric synthetic aperture radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and gen
arXiv:2605.31014v1 Announce Type: new Abstract: Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype cla
arXiv:2605.30698v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance on visual question answering (VQA). To mitigate individual hallucinations and blind spo
arXiv:2605.31067v1 Announce Type: new Abstract: Open-set task execution can significantly benefit from seamlessly switching between coarse and fine scene representations depending on the context and t
arXiv:2605.30557v1 Announce Type: cross Abstract: Spatial reasoning is a fundamental capability for vision-language models (VLMs) deployed in real-world environments. However, visual observations are
arXiv:2605.30722v1 Announce Type: new Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalisin
arXiv:2510.02919v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly solve complex reasoning tasks via long chain-of-thought, but their forward-only autoregressive generation
arXiv:2605.31426v1 Announce Type: cross Abstract: Image Scanning Microscopy (ISM) is a fluorescence imaging technique that combines detector-array acquisition and computational reconstruction to achie
arXiv:2605.30608v1 Announce Type: new Abstract: Learning a shared representation between spoken text and gesture is central to co-speech gesture retrieval, synthesis, and understanding, but remains ch
arXiv:2605.31550v1 Announce Type: new Abstract: Table question answering requires models to recover semantic relations encoded implicitly by two-dimensional layout, merged cells, and hierarchical head
arXiv:2605.30729v1 Announce Type: new Abstract: Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task
arXiv:2605.30837v1 Announce Type: cross Abstract: Prompt-injection detectors are heterogeneous: each is strong on a different slice of attacks, and none is always reliable. Yet existing systems still
arXiv:2605.31520v1 Announce Type: cross Abstract: Credential leakage in public source code repositories poses a critical security threat, with over 23.8 million secrets exposed in 2024 alone. Existing
arXiv:2602.03655v2 Announce Type: replace Abstract: How do neural networks trained over sequences acquire the ability to perform structured operations, such as arithmetic, geometric, and algorithmic c
arXiv:2509.06856v2 Announce Type: replace-cross Abstract: We propose a novel randomized framework for the estimation problem of large-scale linear statistical models, namely Sequential Least-Squares E
arXiv:2601.05134v2 Announce Type: replace Abstract: Certified unlearning based on differential privacy offers strong guarantees but remains largely impractical: the noisy fine-tuning approaches propos
arXiv:2601.20789v3 Announce Type: replace Abstract: Open-weight coding agents should hold a fundamental advantage over closed-source systems because they can specialize to private codebases, encoding