Knowledge catalogue
arXiv:2407.07639v2 Announce Type: replace-cross Abstract: Similarity search is a fundamental task for exploiting information in various applications dealing with graph data, such as citation networks
arXiv:2605.10054v1 Announce Type: new Abstract: Deep neural networks for medical image diagnosis often achieve high predictive accuracy while relying on spurious or clinically irrelevant visual cues,
arXiv:2605.08671v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed not only to make decisions but to explain them. While AI decision fairness has been studied ext
arXiv:2509.20599v2 Announce Type: replace Abstract: Backpropagation through (neural) SDE solvers is traditionally approached in two ways: discretise-then-optimise, which offers accurate gradients but
arXiv:2511.18374v2 Announce Type: replace Abstract: We derive a computable closed-form upper bound on the Hausdorff distance between a truncated minimal robust positively invariant (mRPI) set and its
arXiv:2509.13332v2 Announce Type: replace Abstract: As Large Language Models (LLMs) are increasingly adopted as automated judges in benchmarking and reward modeling, ensuring their reliability, effici
arXiv:2605.09944v1 Announce Type: new Abstract: Robust humanoid stair climbing remains challenging due to geometric discontinuities, sensitivity to step height variations, and perception uncertainty i
arXiv:2605.09773v1 Announce Type: cross Abstract: We use sparse autoencoder (SAE) feature steering to amplify Dark Triad personality traits (Machiavellianism, narcissism, and psychopathy) in Llama-3.3
arXiv:2605.09853v1 Announce Type: new Abstract: Post-training techniques combined with inference-time scaling significantly enhance the reasoning and alignment capabilities of large language models (L
arXiv:2604.06720v2 Announce Type: replace Abstract: We present DeSOPE, a large-scale dataset for 6DoF deformed objects. Most 6D object pose methods assume rigid or articulated objects, an assumption t
arXiv:2605.08398v1 Announce Type: cross Abstract: In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capaci
arXiv:2603.00166v2 Announce Type: replace-cross Abstract: Recent advances in generative AI have shown human-level performance in complex content creation. However, we identify a 'Paradox of Simplicity
arXiv:2605.09923v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for LLM mathematical reasoning, where Group Relative Policy Optim
arXiv:2605.10206v1 Announce Type: cross Abstract: Distributional causal inference requires estimating not only average treatment effects but also interventional outcome distributions, including quanti
arXiv:2605.10318v1 Announce Type: new Abstract: Large language models (LLMs) allow users to query databases using natural language by translating questions into executable queries. Despite strong prog
arXiv:2605.10045v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models have emerged as a strong alternative to diffusion for image synthesis, yet their fixed training resolution prevents d
arXiv:2605.08971v1 Announce Type: cross Abstract: Computer-Aided Design is ubiquitous in todays world, as almost every manufactured object begins as a digital model across industries. At the same time
arXiv:2602.05946v3 Announce Type: replace Abstract: Recent work shows that preference alignment objectives can be interpreted as divergence estimators between aligned (preferred) & unaligned (less-pre
arXiv:2605.09081v1 Announce Type: cross Abstract: We introduce the first universal pretraining corpus for industrial time-series data: FactoryNet. 51M datapoints across 23k end-to-end task executions
arXiv:2605.10795v1 Announce Type: cross Abstract: Large language models demonstrate remarkable ability in factual recall, yet the fundamental limits of storing and retrieving input--output association
arXiv:2605.08434v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a promising paradigm for scalable robotic manipulation, yet their reliance on success-only behavioral clonin
arXiv:2605.08198v1 Announce Type: cross Abstract: We present FairHealth, an open-source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applica
arXiv:2605.10604v1 Announce Type: cross Abstract: Designing fair algorithmic decision systems requires balancing model performance with fairness toward affected individuals: More fairness might requir
arXiv:2605.09827v1 Announce Type: cross Abstract: We present Fashion Florence, a Florence-2 vision-language model fine-tuned with LoRA to extract structured fashion attributes from clothing images. Gi
arXiv:2605.10127v1 Announce Type: new Abstract: Recent research work on fashion outfit generation focuses on promoting visual consistency of garments by leveraging key information from reference image
arXiv:2510.23074v2 Announce Type: replace-cross Abstract: We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) again
arXiv:2605.09214v1 Announce Type: cross Abstract: Kullback-Leibler (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of reverse or forward KL. Recent studies have demo
arXiv:2605.10330v1 Announce Type: cross Abstract: We propose a novel adaptive Mixture-of-Experts (MoE) framework for time series forecasting that enhances expert specialization by incorporating expert
arXiv:2509.21000v2 Announce Type: replace Abstract: Integer Linear Programs (ILPs) are central to real-world optimizations but notoriously difficult to solve. Learning to Optimize (L2O) has emerged as
arXiv:2605.08119v1 Announce Type: cross Abstract: Tian (2025) proves a repulsion theorem (Theorem 6) for the matrix B = (widetilde{F}^op widetilde{F} + eta I)^{-1} during the interactive feature-learn
arXiv:2605.08149v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) decompose large language model representations into interpretable features, but how these features interact under uncertain
arXiv:2603.16231v2 Announce Type: replace-cross Abstract: Numerical optimal control has long been split between globally structured but dimensionally intractable Hamilton--Jacobi--Bellman (HJB) method
arXiv:2601.22204v2 Announce Type: replace Abstract: Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client particip
arXiv:2605.09428v1 Announce Type: new Abstract: Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate fr
arXiv:2602.04093v2 Announce Type: replace Abstract: Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept ann
arXiv:2605.09986v1 Announce Type: cross Abstract: Training a language model on data scattered across bandwidth-limited nodes that cannot be centralized is a setting that arises in clinical networks, e
arXiv:2605.08760v1 Announce Type: new Abstract: Federated Learning (FL) facilitates collaborative model training across decentralized clients while preserving data privacy by avoiding raw data exchang
arXiv:2605.09144v1 Announce Type: new Abstract: Sharpness-aware minimization (SAM) is an effective method for improving the generalization of federated learning (FL) by steering local training toward
arXiv:2605.10082v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across
arXiv:2605.09750v1 Announce Type: new Abstract: This article presents a novel approach to keyframe detection in ultrasound videos, with a particular focus on fetal brain imaging. The proposed model is
arXiv:2605.08925v1 Announce Type: new Abstract: Interactive segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical whe
arXiv:2605.09089v1 Announce Type: cross Abstract: Digital identity verification systems used in remote onboarding rely on document images to authenticate users, making them vulnerable to localized man
arXiv:2605.10439v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing diffusion models, enabling users to inject new visual concepts or styles t
arXiv:2605.09106v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in financial contexts, raising critical concerns about reliability, alignment, and susceptibility
arXiv:2602.07126v2 Announce Type: replace Abstract: Synthetic tabular data has gained attention for enabling privacy-preserving data sharing. While substantial progress has been made in single-table s
arXiv:2605.08565v1 Announce Type: new Abstract: Microscaling is a critical technique for preserving the quality of Large Language Models (LLMs) quantized to ultra-low precision formats. Intuitively, f
arXiv:2605.09469v1 Announce Type: new Abstract: This paper explores the use of emojis in financial sentiment analysis, focusing on the social media platform StockTwits. Emojis, increasingly prevalent
arXiv:2502.18834v2 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can
arXiv:2508.09042v3 Announce Type: replace Abstract: The most dangerous mistakes a novice counselor makes are not the obvious ones: they are utterances that sound caring while quietly violating profess
arXiv:2605.09277v1 Announce Type: new Abstract: We revisit combinatorial Thompson sampling (CTS) for semi-bandits with sleeping arms, where arm availability varies over time and actions must satisfy c
arXiv:2605.08894v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance but incur high deployment costs, motivating extremely low-bit but lossy quantization. Existing
arXiv:2605.08657v1 Announce Type: cross Abstract: We study learnable logic gate networks that stack layers of 2-input Boolean gates to build combinational circuits. Every 2-input gate has a unique mul
arXiv:2605.10183v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss within a fixed parameter-space radius neighborhood. SAM and
arXiv:2605.10792v1 Announce Type: cross Abstract: We propose an implicit neural formulation of optimal transport that eliminates adversarial min--max optimization and multi-network architectures commo
arXiv:2605.09861v1 Announce Type: cross Abstract: Alignment, the tendency of adjacent weight matrices in deep networks to develop compatible subspace orientations, underlies gradient flow, Neural Coll
arXiv:2605.09355v1 Announce Type: new Abstract: Real-world model deployment across multiple domains requires multimodal models to operate under two complementary regimes: (1) multi-task pretraining, t
arXiv:2605.09218v1 Announce Type: cross Abstract: 3D scene understanding spans reasoning about free space, object grounding, hypothetical object insertions, complex geometric relationships, and integr
arXiv:2605.08594v1 Announce Type: cross Abstract: Systolic arrays are the dominant compute fabric for neural network inference. Prior work has addressed column-level fault detection efficiently with u
arXiv:2605.09430v1 Announce Type: new Abstract: Large-scale autoregressive models have demonstrated remarkable capabilities in image generation. However, their sequential raster-scan decoding relies o
arXiv:2605.09003v1 Announce Type: new Abstract: Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, th