We study first-hitting times in Differential Evolution (DE) through a conditional hazard frame work. Instead of analyzing convergence via Markov-chain transitio...
The integration of AI agents into economic markets fundamentally alters the landscape of strategic interaction. We investigate the economic implications of expa...
Competitive sports require sophisticated tactical analysis, yet combat disciplines like boxing remain underdeveloped in AI-driven analytics due to the complexit...
Extractive summarization (ES) aims to generate a concise summary by selecting a subset of sentences from a document while maximizing relevance and minimizing re...
In the generative AI era, where even critical medical tasks are increasingly automated, radiology report generation (RRG) continues to rely on suboptimal metric...
Message delivery respecting causal ordering (causal delivery) is one of the most classic and widely useful abstraction for inter-process communication in a dist...
Ethiopia's Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, requir...
Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly re...
We present a novel probabilistic approach for optimal path experimental design. In this approach a discrete path optimization problem is defined on a static nav...
Transformer pretraining is increasingly constrained by memory and compute requirements, with the key-value (KV) cache emerging as a dominant bottleneck during t...
Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep lear...
As vision-language models (VLMs) tackle increasingly complex and multimodal tasks, the rapid growth of Key-Value (KV) cache imposes significant memory and compu...
Learning structured task representations from human demonstrations is essential for understanding long-horizon manipulation behaviors, particularly in bimanual ...
Information overload and misinformation create significant challenges in extracting meaningful narratives from large news collections. This paper defines the na...
Large-scale livestock operations pose significant risks to human health and the environment, while also being vulnerable to threats such as infectious diseases ...
Diffusion models now generate high-quality, diverse samples, with an increasing focus on more powerful models. Although ensembling is a well-known way to improv...
Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for enhancing large language models' question-answering capabilities through the integra...
We propose Map2Thought, a framework that enables explicit and interpretable spatial reasoning for 3D VLMs. The framework is grounded in two key components: Metr...
Large language models (LLMs) exhibit exceptional performance across various domains, yet they face critical safety concerns. Model editing has emerged as an eff...
We report on an astonishing ability of large language models (LLMs) to make sense of 'Jabberwocky' language in which most or all content words have been randoml...
This white paper provides an overview of the topic of 'technical debt' and presents an approach for managing technical debt in teams. The white paper is based o...
PubMed-OCR is an OCR-centric corpus of scientific articles derived from PubMed Central Open Access PDFs. Each page image is annotated with Google Cloud Vision a...
Context and motivation. Online user feedback is a valuable resource for requirements engineering, but its volume and noise make analysis difficult. Existing too...
The three-dimensional bin packing problem (3D-BPP) is a longstanding challenge in operations research and logistics. Classical heuristics and constructive metho...
