arXiv:2510.19964v1 Announce Type: new Abstract: The study explores the potential of AI technologies in personalized learning, suggesting the prediction of academic success through leadership personality traits and machine learning modelling. The primary data were obtained from 129 master’s students in the Environmental Engineering Department, who underwent five leadership personality tests with 23 characteristics. Students used […]
FLAS: a combination of proactive and reactive auto-scaling architecture for distributed services
arXiv:2510.20388v1 Announce Type: cross Abstract: Cloud computing has established itself as the support for the vast majority of emerging technologies, mainly due to the characteristic of elasticity it offers. Auto-scalers are the systems that enable this elasticity by acquiring and releasing resources on demand to ensure an agreed service level. In this article we present […]
HauntAttack: When Attack Follows Reasoning as a Shadow
arXiv:2506.07031v4 Announce Type: replace-cross Abstract: Emerging Large Reasoning Models (LRMs) consistently excel in mathematical and reasoning tasks, showcasing remarkable capabilities. However, the enhancement of reasoning abilities and the exposure of internal reasoning processes introduce new safety vulnerabilities. A critical question arises: when reasoning becomes intertwined with harmfulness, will LRMs become more vulnerable to jailbreaks in […]
MolBridge: Atom-Level Joint Graph Refinement for Robust Drug-Drug Interaction Event Prediction
arXiv:2510.20448v1 Announce Type: cross Abstract: Drug combinations offer therapeutic benefits but also carry the risk of adverse drug-drug interactions (DDIs), especially under complex molecular structures. Accurate DDI event prediction requires capturing fine-grained inter-drug relationships, which are critical for modeling metabolic mechanisms such as enzyme-mediated competition. However, existing approaches typically rely on isolated drug representations and […]
LLMs can hide text in other text of the same length.ipynb
arXiv:2510.20075v1 Announce Type: new Abstract: A meaningful text can be hidden inside another, completely different yet still coherent and plausible, text of the same length. For example, a tweet containing a harsh political critique could be embedded in a tweet that celebrates the same political leader, or an ordinary product review could conceal a secret […]
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging
arXiv:2510.20479v1 Announce Type: cross Abstract: We unveil that internal representations in large language models (LLMs) serve as reliable proxies of learned knowledge, and propose RECALL, a novel representation-aware model merging framework for continual learning without access to historical data. RECALL computes inter-model similarity from layer-wise hidden representations over clustered typical samples, and performs adaptive, hierarchical […]
Fine-Tuning Multilingual Language Models for Code Review: An Empirical Study on Industrial C# Projects
arXiv:2507.19271v2 Announce Type: replace-cross Abstract: Code review is essential for maintaining software quality but often time-consuming and cognitively demanding, especially in industrial environments. Recent advancements in language models (LMs) have opened new avenues for automating core review tasks. This study presents the empirical evaluation of monolingual fine-tuning on the performance of open-source LMs across three […]
Metis-HOME: Hybrid Optimized Mixture-of-Experts for Multimodal Reasoning
arXiv:2510.20519v1 Announce Type: cross Abstract: Inspired by recent advancements in LLM reasoning, the field of multimodal reasoning has seen remarkable progress, achieving significant performance gains on intricate tasks such as mathematical problem-solving. Despite this progress, current multimodal large reasoning models exhibit two key limitations. They tend to employ computationally expensive reasoning even for simple queries, […]
AI PB: A Grounded Generative Agent for Personalized Investment Insights
arXiv:2510.20099v1 Announce Type: new Abstract: We present AI PB, a production-scale generative agent deployed in real retail finance. Unlike reactive chatbots that answer queries passively, AI PB proactively generates grounded, compliant, and user-specific investment insights. It integrates (i) a component-based orchestration layer that deterministically routes between internal and external LLMs based on data sensitivity, (ii) […]
GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning
arXiv:2510.20548v1 Announce Type: cross Abstract: Reinforcement learning has recently shown promise in improving retrieval-augmented generation (RAG). Despite these advances, its effectiveness in multi-hop question answering (QA) remains limited by two fundamental limitations: (i) global planning absence to structure multi-step reasoning, and (ii) unfaithful execution, which hinders effective query formulation and consistent use of retrieved evidence. […]