Knowledge catalogue
arXiv:2512.11587v2 Announce Type: replace Abstract: Even for the gradient descent (GD) method applied to neural network training, understanding its optimization dynamics, including convergence rate, i
arXiv:2605.21502v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are increasingly used to model biological systems, yet the reliability of post-hoc explanation methods for recovering mea
arXiv:2605.22566v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents demonstrate strong reasoning and execution capabilities on complex tasks when guided by structured instructions,
arXiv:2605.21516v1 Announce Type: new Abstract: Harness engineering has emerged as an important inference-time technique for large language model (LLM) agents, aiming to improve long-term performance
arXiv:2605.22612v1 Announce Type: cross Abstract: Benchmarks are necessary for healthcare evaluation, but are not sufficient for predicting deployment performance. Our position is that the evaluation-
arXiv:2602.05286v3 Announce Type: replace Abstract: Healthcare facility visit prediction is essential for optimizing healthcare resource allocation and informing public health policy. Despite advanced
arXiv:2603.02604v2 Announce Type: replace Abstract: We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem th
arXiv:2605.21773v1 Announce Type: cross Abstract: Recent benchmark efforts have advanced the evaluation of large language models (LLMs) in cybersecurity, including tasks such as penetration testing an
arXiv:2605.22666v1 Announce Type: cross Abstract: A fuzzy Boolean function is a map f:ube^no [0,1], where ninmathbb N. We introduce and compare three ways of saying that such a function has bounded co
arXiv:2605.22235v1 Announce Type: new Abstract: Complex dynamical systems governed by holomorphic maps such as z^2 + c exhibit fractal boundaries with extreme sensitivity to initial conditions. Accura
arXiv:2605.22223v1 Announce Type: new Abstract: We study how we can leverage only a handful of characteristics of a transformer's architecture to closely predict the number of different sequences it c
arXiv:2605.21972v1 Announce Type: new Abstract: Unstructured magnitude pruning at high sparsity can reduce neural network accuracy to near-random performance, while labeled retraining may be unavailab
arXiv:2605.22387v1 Announce Type: new Abstract: Accurate electricity price forecasting (EPF) is essential for market participants to support operational planning and risk management, yet remains chall
arXiv:2601.20205v3 Announce Type: replace Abstract: Mixture-of-Experts (MoE) layers have emerged as an important tool in scaling up modern neural networks by decoupling total trainable parameters from
arXiv:2605.21731v1 Announce Type: new Abstract: Deep learning models are increasingly used in scientific prediction tasks where strong benchmark performance is often interpreted as evidence of scienti
arXiv:2605.22182v1 Announce Type: new Abstract: Neural operators have achieved significant success in modern scientific computing due to their flexibility and strong generalization capabilities. Exist
arXiv:2605.22480v1 Announce Type: new Abstract: Mini-batch training of Graph Neural Networks (GNNs) is fundamentally different from training on i.i.d. data: sampling a subgraph alters the topology and
arXiv:2605.22556v1 Announce Type: new Abstract: Digital elevation models (DEMs) underpin terrain analysis in Geographic Information Systems (GIS), but in their common raster form, they rely on interpo
arXiv:2605.22604v1 Announce Type: cross Abstract: The advent of cardless artificial intelligence (AI) banking heralds a paradigm shift in the financial landscape, offering users unprecedented security
arXiv:2605.22820v1 Announce Type: new Abstract: We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-dema
arXiv:2602.22719v2 Announce Type: replace Abstract: State-space models (SSMs) have emerged as an efficient strategy for building powerful language models, avoiding the quadratic complexity of computin
arXiv:2605.22207v1 Announce Type: cross Abstract: Safety has been a major concern when deploying deep reinforcement learning algorithms in the real world. A promising direction that ensures that the l
arXiv:2605.22054v1 Announce Type: new Abstract: The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayes
arXiv:2602.05873v2 Announce Type: replace Abstract: Bayesian (deep) neural networks (BNN) are often more attractive than the vanilla point-estimate deep learning in various aspects including uncertain
arXiv:2605.22786v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems increasingly rely on intermediate communication to coordinate complex tasks. While most existing
arXiv:2605.22335v1 Announce Type: new Abstract: In-context learning for tabular data sets strong predictive standards in observational settings; it however primarily relies on correlational structure,
arXiv:2601.05157v2 Announce Type: replace-cross Abstract: In this work, we give a {rm poly}(d,k) time and sample algorithm for efficiently learning the parameters of a mixture of k spherical distribut
arXiv:2601.21853v2 Announce Type: replace-cross Abstract: Multi-vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single-vecto
arXiv:2605.21565v1 Announce Type: new Abstract: Multimodal Emotion Recognition in Conversations (MERC) is a crucial task for understanding human interactions, where multimodal approaches integrating l
arXiv:2602.01935v2 Announce Type: replace Abstract: LLM-guided compiler optimization has recently shown promise, but existing approaches rely on a single large LLM throughout search, making them expen
arXiv:2506.21035v5 Announce Type: replace Abstract: Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting. Existing LoRA-based M
arXiv:2605.22717v1 Announce Type: cross Abstract: Interactive streaming music generation promises the use of generative models for live performance and co-creation that is impossible with offline mode
arXiv:2605.21548v1 Announce Type: cross Abstract: We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal st
arXiv:2605.22291v1 Announce Type: new Abstract: Long-term fairness algorithms aim to satisfy fairness beyond static and short-term notions by accounting for the dynamics between decision-making polici
arXiv:2605.22471v1 Announce Type: new Abstract: Transformers have become a central architecture for graph learning, but their application to graphs requires first choosing a tokenization: a graph-to-t
arXiv:2605.22756v1 Announce Type: new Abstract: Random forests are widely used in fields involving sensitive tabular data, but existing approaches to enforcing differential privacy (DP) typically degr
arXiv:2605.21762v1 Announce Type: new Abstract: Non-contrast computed tomography calcium scoring (CTCS) is a cost-effective imaging modality widely used to detect coronary artery calcifications. This
arXiv:2605.22775v1 Announce Type: new Abstract: Real-time cognitive load assessment from eye-tracking signals could potentially enable adaptive human-centered-AI such as safety-critical applications s
arXiv:2605.21770v1 Announce Type: new Abstract: Large language models frequently produce errors in reasoning tasks despite possessing the underlying knowledge required for correct reasoning. One possi
arXiv:2602.18600v3 Announce Type: replace Abstract: Systematic evaluation of Multimodal Large Language Models (MLLMs) is crucial for advancing Artificial General Intelligence (AGI). However, existing
arXiv:2603.16077v3 Announce Type: replace Abstract: Masked diffusion models (MDM) exhibit superior generalization when learned using a Partial masking scheme (Prime). This approach converts tokens int
arXiv:2605.22168v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) map complex visual inputs to semantic spaces, but interpreting the cross-modal reasoning of VLMs currently relies on pos
arXiv:2506.16659v3 Announce Type: replace Abstract: Training large language models (LLMs) relies on adaptive optimizers such as Adam, which introduce extra operations and require significantly more me
arXiv:2605.21768v1 Announce Type: new Abstract: Memory-augmented LLM agents enable interactions that extend beyond finite context windows by storing, updating, and reusing information across sessions.
arXiv:2603.19310v3 Announce Type: replace Abstract: Reinforcement learning has emerged as a powerful paradigm for improving large language model (LLM) reasoning, where rollouts are sampled from the po
arXiv:2605.21722v1 Announce Type: cross Abstract: Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent disc
arXiv:2605.22410v1 Announce Type: new Abstract: Spectral clustering largely depends on the affinity graph, yet constructing a graph that preserves reliable local connectivity while adapting to heterog
arXiv:2605.21783v1 Announce Type: new Abstract: Test-time adaptation (TTA) methods improve model performance under distribution shift but lack formal guarantees connecting shift magnitude to predictio
arXiv:2605.21751v1 Announce Type: new Abstract: Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coeffi
arXiv:2602.13372v2 Announce Type: replace-cross Abstract: Evaluating moral alignment in agents navigating conflicting, hierarchically structured human norms is a critical challenge at the intersection
arXiv:2605.22794v1 Announce Type: cross Abstract: Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the nex
arXiv:2605.20747v1 Announce Type: cross Abstract: Long non-coding RNAs (lncRNAs) are emerging regulatory molecules implicated in chronic disease pathogenesis, including Type 2 Diabetes Mellitus (T2D).
arXiv:2605.22724v1 Announce Type: new Abstract: We study the approximation and statistical complexity of learning collections of operators in a shared multi-task setting, with a focus on the Multiple
arXiv:2508.06884v2 Announce Type: replace-cross Abstract: We study first-order methods for convex optimization problems with functions f satisfying the recently proposed ell-smoothness condition ||nab
arXiv:2605.21519v1 Announce Type: cross Abstract: Graph Partitioning is a critical problem in numerous scientific and engineering domains including social network analysis, VLSI design, and many more.
arXiv:2605.22557v1 Announce Type: new Abstract: We introduce an abstract neural flow framework for neural networks and neural operators. The framework contains two continuous-depth models, namely neur
arXiv:2502.01476v4 Announce Type: replace Abstract: Analytical solutions to differential equations offer exact, interpretable insight but are rarely available because discovering them requires expert
arXiv:2605.22248v1 Announce Type: new Abstract: Climate emulation is an out-of-distribution (OOD) projection task. This is precisely the challenge where modern Machine Learning (ML) methods are most p
arXiv:2605.21911v1 Announce Type: new Abstract: We develop a principled framework for analyzing and designing noise schedules in diffusion models. We show that one can recast this design problem as an
arXiv:2605.21561v1 Announce Type: new Abstract: Unsupervised feature selection is commonly formulated as a multiobjective optimisation problem that jointly optimises subset quality and subset size. Ye