Knowledge catalogue
arXiv:2603.04419v3 Announce Type: replace-cross Abstract: Vision-language models produce different object and use descriptions under different persona prompts, but low overlap alone does not identify
arXiv:2609.15716v1 Announce Type: new Abstract: CMDIR extends Manifold-Decomposed Impedance Retargeting (MDIR) to transform fixed-impedance demonstrations into continuous variable-impedance controller
arXiv:2609.14383v1 Announce Type: new Abstract: Real-time pedestrian detection in driving scenes is constrained by three coupled failure modes: tiny targets lose discriminative evidence, occlusion wea
arXiv:2602.08696v3 Announce Type: replace-cross Abstract: Dysarthric speech recognition is limited by high speaker variability and scarce labeled data. Existing synthesis methods often couple speaker
arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MO
arXiv:2311.03154v3 Announce Type: replace Abstract: There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients trai
arXiv:2510.13134v2 Announce Type: replace Abstract: We study continuous-time dropout in controlled differential equations. We introduce a random-batch approximation of additive vector fields. On each
arXiv:2609.15193v1 Announce Type: new Abstract: Drifting models offer a promising route to faster generative AI: they perform gradual transport during training, while generating new samples in a singl
arXiv:2609.14011v1 Announce Type: new Abstract: Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them
arXiv:2609.14985v1 Announce Type: new Abstract: We show how users can create and manipulate causal virtual worlds with large-language-model (LLM) and large-video-model agents. The approach uses feedba
arXiv:2609.14226v1 Announce Type: new Abstract: Automated radiology report generation has advanced rapidly in diagnostic accuracy, yet generated reports frequently diverge from the stylistic conventio
arXiv:2609.15989v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a safety strategy where the reasoning of a large language model 'actor' is inspected by a 'monitor' (often another
arXiv:2609.13664v1 Announce Type: new Abstract: Knowledge graphs are increasingly distributed across autonomous organizations that share an entity space but own disjoint subsets of relations, forming
arXiv:2606.15317v2 Announce Type: replace Abstract: Offline models for autonomous robots often fail under time-varying dynamics outside their training distribution. While Koopman operator theory offer
arXiv:2609.13842v1 Announce Type: cross Abstract: Recent advances in speech synthesis and voice conversion have made deepfake speech increasingly realistic, making generalization to unseen spoofing at
arXiv:2512.24688v2 Announce Type: replace Abstract: Relative localization is essential for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental featur
arXiv:2609.13201v1 Announce Type: new Abstract: Training datasets for upcoming LLMs would include a significant amount of AI text/image data generated from current LLMs. In such a scenario, it is impo
arXiv:2609.13942v1 Announce Type: cross Abstract: The CRITICS project addresses science accessibility and literacy by converging advanced Machine Translation (MT) based on Large Language Models (LLMs)
arXiv:2609.14934v1 Announce Type: cross Abstract: Statistical data fusion combines two panels that share a block of covariates but observe disjoint outcome blocks, and in its traditional form no row o
arXiv:2609.14725v1 Announce Type: new Abstract: Few-step distillation accelerates diffusion models but must balance diversity and fidelity: trajectory-based distillation preserves mode coverage, while
arXiv:2609.14811v1 Announce Type: new Abstract: We introduce Crypto Accounting Bench (CAB), a benchmark for assessing whether frontier and open-weight language models can reconstruct the complete jour
arXiv:2609.13646v1 Announce Type: new Abstract: This work addresses decentralized online Riemannian optimization on Hadamard manifolds. Prior work under geodesic convexity (g-convexity) may require cu
arXiv:2609.13413v1 Announce Type: new Abstract: We introduce CVSS-X, a large-scale synthetic speech-to-speech translation corpus that extends CVSS by reversing the translation direction. While CVSS tr
arXiv:2609.15234v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have gained significant attention due to their strong language understanding and generation capabilities, demonst
arXiv:2609.13892v1 Announce Type: cross Abstract: We study the recovery of forward and reverse quadratic optimal-transport maps from unpaired samples in high dimensions. We introduce a bidirectional n
arXiv:2609.14171v1 Announce Type: new Abstract: Complex-valued signals, such as Magnetic Resonance Imaging (MRI) and audio spectrograms, are almost always modelled as flat two-channel Euclidean data.
arXiv:2609.15070v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) generate text by iteratively denoising a masked sequence, independently predicting multiple tokens at each step. This c
arXiv:2609.13808v1 Announce Type: new Abstract: Structured knowledge fact checking aims to determine the truthfulness of natural language claims by reasoning over structured evidence. Recent program-g
arXiv:2609.15044v1 Announce Type: cross Abstract: Data attribution seeks to quantify how individual training examples shape a model's predictions and underpins problems including data valuation, machi
arXiv:2609.15485v1 Announce Type: new Abstract: Avalanche forecasting requires knowledge of snowpack conditions and recent avalanche activity, but field observations are sparse across large mountain r
arXiv:2609.14045v1 Announce Type: new Abstract: Agentic learning systems are often required to adapt after deployment by observing new data and reusing prior knowledge under limited supervision or fee
arXiv:2510.19266v3 Announce Type: replace Abstract: State-space models (SSMs) have emerged as promising alternatives to Transformers for sequence modeling. However, training competitive SSMs from scra
arXiv:2609.14193v1 Announce Type: cross Abstract: On-policy distillation (OPD) has become a standard component of frontier post-training pipelines, yet how much its training data actually contributes
arXiv:2508.02312v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs), now a foundation in advancing natural language processing, power applications such as text generation, machine t
arXiv:2609.15722v1 Announce Type: new Abstract: AI-driven automated decision-making requires both predictive performance and interpretability. Recent advances in interpretable machine learning (IML) p
arXiv:2609.13711v1 Announce Type: new Abstract: Selecting a decentralized Multi-Robot Task Allocation (MRTA) method for embedded deployment on autonomous platforms requires considering more than route
arXiv:2609.14821v1 Announce Type: new Abstract: Earth system modeling is shifting from task-specific predictors toward foundation models with general spatiotemporal representation capabilities. Althou
arXiv:2607.17425v3 Announce Type: replace Abstract: Sparse autoencoders (SAEs) compress model activations into sparse codes, but equal reconstruction error and sparsity can preserve different linearly
arXiv:2602.00377v3 Announce Type: replace Abstract: Existing knowledge probing methods rely on pre-defined queries, limiting extraction to known concepts. We introduce DecompressionLM, a stateless fra
arXiv:2609.13324v1 Announce Type: cross Abstract: Graph-RAG systems often assume pristine data quality, overlooking the severe impact of perturbations in cloud-native databases. This paper proposes a
arXiv:2609.15225v1 Announce Type: new Abstract: Objective: To develop an intelligent framework, termed CUA-Net, for the automated classification of congenital uterine anomalies (CUA) without requiring
arXiv:2606.22906v2 Announce Type: replace-cross Abstract: Large language models have shown strong performance on software engineering (SE) tasks, yet understanding large industrial repositories remain
arXiv:2512.08379v3 Announce Type: replace Abstract: Biosignals collected from wearable devices are widely utilized in healthcare applications. Machine learning models used in these applications often
arXiv:2609.14129v1 Announce Type: cross Abstract: Ultra-High-Definition (UHD) video presents significant challenges for efficient storage and real-time decoding. Learning-based methods, such as Neural
arXiv:2609.13712v1 Announce Type: new Abstract: Many real-world scenarios can be represented using graph-structured data. However, traditional GNNs that transmit messages based on first-order neighbor
arXiv:2609.15803v1 Announce Type: cross Abstract: Long-running AI agents create a control problem: each action they take changes the state, which in turn affects the trajectory of future actions. If t
arXiv:2609.14257v1 Announce Type: new Abstract: Semantic text watermarks encode signals in meaning rather than surface token choices, offering robustness to paraphrasing and other semantic-preserving
arXiv:2609.14715v1 Announce Type: new Abstract: We scale our conventional sub-150M pretraining recipe from 53.5M to 109.7M parameters, holding the method fixed (Qwen3-style decoder with grouped-query
arXiv:2609.15122v1 Announce Type: cross Abstract: Generative CAD models are expected to remain behaviorally correct after parameter edits, so increasing the number of edit checks is often treated as a
arXiv:2607.16196v2 Announce Type: replace Abstract: Soft plush companions provide a safe and intuitive platform for affective human-robot interaction, but their deformable structure and distributed ta
arXiv:2609.15744v1 Announce Type: new Abstract: Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early
arXiv:2603.02790v2 Announce Type: replace Abstract: Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data mo
arXiv:2609.13756v1 Announce Type: new Abstract: One in every five vehicle accidents on the road today is caused simply due to driver fatigue. Fatigue or otherwise drowsiness, significantly reduces the
arXiv:2609.13194v1 Announce Type: cross Abstract: A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. We tes
arXiv:2609.14595v1 Announce Type: cross Abstract: Multichannel time-series classification commonly assumes synchronized sensor streams, although latency, clock drift, and preprocessing can introduce r
arXiv:2609.15570v1 Announce Type: new Abstract: World Action Models (WAMs) use video generation models to predict future visual dynamics for robotic manipulation, but iterative denoising introduces ad
arXiv:2606.17995v2 Announce Type: replace-cross Abstract: We study the privacy of releasing functional posterior sample paths from a Gaussian process (GP) when the entire training set including covari
arXiv:2609.13588v1 Announce Type: new Abstract: Control allocation for multirotors with bidirectional propellers is commonly formulated in signed-thrust variables, where the wrench map is linear. The
arXiv:2609.14642v1 Announce Type: new Abstract: Generation of musculoskeletal gait trajectories conditioned on patient-specific parameters remains a key challenge for wearable robotics and rehabilitat
arXiv:2609.13990v1 Announce Type: cross Abstract: Diffusion-based generative models (DMs) have found applications in control problems, and in particular robotics, where the DMs enable exploration of p