Knowledge catalogue
arXiv:2606.11779v1 Announce Type: new Abstract: The need for detecting and sorting batteries is drastically increasing for many applications. This study proves the potential of transfer learning in pr
arXiv:2606.11660v1 Announce Type: new Abstract: Data attribution is a promising field in interpretability that aims to explain model behavior through the influence of its training data, with applicati
arXiv:2606.11255v1 Announce Type: new Abstract: Bernstein--Schur kernels are products of a finite-feature kernel (one with an explicit finite-dimensional feature map) and a completely monotone shift-i
arXiv:2606.12171v1 Announce Type: new Abstract: Knowledge Distillation (KD) and mixup have proven effective at inducing smoothness in class boundaries; KD captures inherent class relationships in prob
arXiv:2606.12273v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training me
arXiv:2510.07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a p
arXiv:2606.12210v1 Announce Type: new Abstract: Can financial news reliably predict short-term stock movements? Despite advances in large language models, this question remains unresolved. We revisit
arXiv:2606.11711v1 Announce Type: new Abstract: Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives. In practice, trackin
arXiv:2602.19718v2 Announce Type: replace-cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the
arXiv:2601.00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recogniti
arXiv:2606.12286v1 Announce Type: new Abstract: Counting living cells is an important step in many biological research workflows. Our collaborators at the Wellcome Sanger Institute study vital genes i
arXiv:2507.11688v4 Announce Type: replace Abstract: Contemporary large models often exhibit behaviors suggesting the presence of low-level primitives that compose into modules with richer functionalit
arXiv:2602.06868v2 Announce Type: replace Abstract: Zero-order optimization has recently received significant attention for designing optimal trajectories and policies for robotic systems. However, mo
arXiv:2606.12411v1 Announce Type: new Abstract: Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with
arXiv:2507.23534v3 Announce Type: replace-cross Abstract: Continual learning (CL) seeks to mitigate catastrophic forgetting when models are trained with sequential tasks. A common approach, experience
arXiv:2512.16415v3 Announce Type: replace Abstract: Object counting in complex scenes is particularly challenging in the zero-shot (ZS) setting, where instances of unseen categories are counted using
arXiv:2601.11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable
arXiv:2510.17816v2 Announce Type: replace-cross Abstract: Wi-Fi-based human activity recognition (HAR) provides substantial convenience and has emerged as a thriving research field, yet the coarse spa
arXiv:2506.20040v3 Announce Type: replace-cross Abstract: Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across
arXiv:2509.19463v2 Announce Type: replace Abstract: A central challenge for multi-robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Coll
arXiv:2606.11326v1 Announce Type: new Abstract: Recent feed-forward 3D reconstruction methods have demonstrated strong performance and flexibility in efficient end-to-end scene geometry estimation fro
arXiv:2606.11953v1 Announce Type: new Abstract: Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus
arXiv:2606.11469v1 Announce Type: cross Abstract: We study the task of density estimation, where we hope to accurately estimate a probability density from n samples. A textbook method for density esti
arXiv:2606.12114v1 Announce Type: new Abstract: Sensitive personal information can appear in large-scale pre-training corpora for large language models (LLMs). Detecting and filtering such information
arXiv:2606.12400v1 Announce Type: new Abstract: Long input sequences are central to document understanding and multi-step reasoning in Large Language Models, yet the quadratic cost of attention makes
arXiv:2603.09715v2 Announce Type: replace Abstract: Visual instruction tuning is crucial for improving vision-language large models (VLLMs). However, many samples can be solved via linguistic patterns
arXiv:2606.12189v1 Announce Type: new Abstract: We address 4D reconstruction from partial point cloud sequences, where depth-sensor observations are incomplete, unordered, and lack explicit temporal c
arXiv:2606.11710v1 Announce Type: new Abstract: This paper presents ERN-Net, an Evolving Reason Node-Net for efficient document image binarization. ERN-Net enhances degradation-sensitive regions, such
arXiv:2606.11471v1 Announce Type: cross Abstract: The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent
arXiv:2606.11285v1 Announce Type: new Abstract: Unauthorized unmanned aerial vehicle (UAV) activity around airports, public venues, and other sensitive sites has made protected-airspace monitoring inc
arXiv:2511.16672v4 Announce Type: replace Abstract: Recent advances in large multimodal models (LMMs) have enabled impressive reasoning and perception abilities, yet most existing training pipelines s
arXiv:2606.11450v1 Announce Type: new Abstract: Recently, masked skeleton reconstruction models have emerged as strong action representation learners, driving significant progress in self-supervised s
arXiv:2505.17623v2 Announce Type: replace-cross Abstract: Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural net
arXiv:2606.11613v1 Announce Type: cross Abstract: When many people highlight the same document, is the crowd a single consensus, or is it internally structured into reader sub-groups that mark differe
arXiv:2606.11620v1 Announce Type: cross Abstract: Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approxi
arXiv:2606.11766v1 Announce Type: cross Abstract: Distilling a large speech foundation model (SFM) into an efficient student model has been successfully applied to low-resource environments. Although
arXiv:2606.11828v1 Announce Type: cross Abstract: Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy d
arXiv:2606.12070v1 Announce Type: new Abstract: State space projections and decompositions have emerged as powerful tools to tackle the curse of dimensionality in high-dimensional, multi-robot motion
arXiv:2606.12277v1 Announce Type: new Abstract: In this paper, we propose an approach to finding sets of similar-performing models (in terms of loss/accuracy measurements) with highly different contex
arXiv:2606.12278v1 Announce Type: new Abstract: Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance. Although the Lottery Ti
arXiv:2606.12295v1 Announce Type: cross Abstract: This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMa
arXiv:2606.11833v1 Announce Type: new Abstract: Flow matching and diffusion models enable conditional generation across domains ranging from images to proteins, with recent extensions to out-of-distri
arXiv:2606.12087v1 Announce Type: new Abstract: Training deep search agents requires verifiable questions whose answers remain unavailable until sufficient evidence has been acquired through search. E
arXiv:2606.12303v1 Announce Type: new Abstract: Multimodal image fusion aims to integrate complementary information from different modalities into a fused image that preserves rich local details while
arXiv:2606.11911v1 Announce Type: cross Abstract: Persistence diagrams are common representations in topological data analysis, but they do not naturally live in a vector space, and the statistical to
arXiv:2606.11745v1 Announce Type: cross Abstract: Visual causal reasoning is essential for understanding and intervening in the physical world, requiring identification of causal variables from visual
arXiv:2606.11930v1 Announce Type: cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging multimodal learning problem because labeled datasets are li
arXiv:2606.12016v1 Announce Type: cross Abstract: Model post-training, and in particular reinforcement learning (RL), is one of the primary mechanisms by which developers can shape models' values and
arXiv:2509.25359v2 Announce Type: replace-cross Abstract: We present a systematic stress-test of geometric metrics for LLM evaluation. Rank-based geometric properties of internal representations have
arXiv:2601.00791v2 Announce Type: replace-cross Abstract: Verifying whether a language model is genuinely reasoning or pattern-matching remains an open problem: learned verifiers are expensive, and ou
arXiv:2606.11429v1 Announce Type: cross Abstract: Speech foundation models often struggle in low-resource domains due to domain mismatch and data scarcity. We propose Gumbel-BEARD, a domain adaptation
arXiv:2606.12318v1 Announce Type: cross Abstract: Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retra
arXiv:2411.12193v4 Announce Type: replace-cross Abstract: The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management. A
arXiv:2601.03326v2 Announce Type: replace Abstract: PCA can be used for rotation invariant features, describing a shape with its p_{ab}=E[(x_i-E[x_a])(x_b-E[x_b])] covariance matrix approximating shap
arXiv:2606.11818v1 Announce Type: new Abstract: The handling of flexible materials is a difficult task to fully automate due to the challenges caused by the deformability of these types of objects. Me
arXiv:2606.11884v1 Announce Type: new Abstract: This paper addresses the challenge of assessing image quality in ID cards in remote verification systems by applying capture-related quality measures fr
arXiv:2503.08445v2 Announce Type: replace Abstract: Robotics and automation are increasingly influential in logistics but remain largely confined to traditional warehouses. In grocery retail, advancem
arXiv:2512.03077v2 Announce Type: replace-cross Abstract: The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tec
arXiv:2601.07506v2 Announce Type: replace Abstract: While large language models (LLMs) are increasingly used as automatic judges for question answering (QA) and other reference-conditioned evaluation
arXiv:2602.10743v2 Announce Type: replace Abstract: State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but