arXiv:2603.19121v2 Announce Type: replace-cross Abstract: The creation of high-fidelity, customizable 3D indoor scene textures remains a significant challenge. While text-driven methods offer flexibility, they lack the precision for fine-grained, instance-level control, and often produce textures with insufficient quality, artifacts, and baked-in shading. To overcome these limitations, we introduce CustomTex, a novel framework for instance-level, high-fidelity […]
Retrieval-Augmented LLMs for Security Incident Analysis
arXiv:2603.18196v2 Announce Type: replace-cross Abstract: Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts, network traffic records, and authentication events. This process is labor-intensive: analysts must sift through large volumes of data to identify relevant indicators and piece together what happened. We present a RAG-based system that performs […]
Modelling the passive and active response of skeletal muscles within the adapted Voigt representation framework
arXiv:2603.19723v1 Announce Type: cross Abstract: We present a constitutive model for the passive and active response of skeletal muscles. At variance with more classical approaches, the model is developed exploiting adapted Voigt representations of strain and stress tensors within the context of nonlinear Cauchy elasticity. This framework allows us to identify non-trivial stress-strain relations in […]
Responsible AI Technical Report
arXiv:2509.20057v4 Announce Type: replace-cross Abstract: KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services. By analyzing the Basic Act on AI implementation and global AI governance trends, we established a unique approach for regulatory compliance and systematically identify and manage all potential risk […]
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation
arXiv:2506.08898v4 Announce Type: replace Abstract: Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs) by decomposing them into multiple subproblems, each associated with a specific weight vector. However, these methods typically treat all subproblems equally and solve them using a single model, hindering the effective exploration of the […]
Exact MAP inference in general higher-order graphical models using linear programming
arXiv:1709.09051v2 Announce Type: replace-cross Abstract: This paper is concerned with the problem of exact MAP inference in general higher-order graphical models by means of a traditional linear programming relaxation approach. In fact, the proof that we have developed in this paper is a rather simple algebraic proof being made straightforward, above all, by the introduction […]
Orchestrating Human-AI Software Delivery: A Retrospective Longitudinal Field Study of Three Software Modernization Programs
arXiv:2603.20028v1 Announce Type: cross Abstract: Evidence on AI in software engineering still leans heavily toward individual task completion, while evidence on team-level delivery remains scarce. We report a retrospective longitudinal field study of Chiron, an industrial platform that coordinates humans and AI agents across four delivery stages: analysis, planning, implementation, and validation. The study covers […]
Enhancing Hyperspace Analogue to Language (HAL) Representations via Attention-Based Pooling for Text Classification
arXiv:2603.20149v1 Announce Type: cross Abstract: The Hyperspace Analogue to Language (HAL) model relies on global word co-occurrence matrices to construct distributional semantic representations. While these representations capture lexical relationships effectively, aggregating them into sentence-level embeddings via standard mean pooling often results in information loss. Mean pooling assigns equal weight to all tokens, thereby diluting the […]
From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing
arXiv:2603.01038v2 Announce Type: replace-cross Abstract: Face recognition remains vulnerable to presentation attacks, calling for robust Face Anti-Spoofing (FAS) solutions. Recent MLLM-based FAS methods reformulate the binary classification task as the generation of brief textual descriptions to improve cross-domain generalization. However, their generalizability is still limited, as such descriptions mainly capture intuitive semantic cues (e.g., mask […]
Semantic Delta: An Interpretable Signal Differentiating Human and LLMs Dialogue
arXiv:2603.19849v1 Announce Type: cross Abstract: Do LLMs talk like us? This question intrigues a multitude of scholar and it is relevant in many fields, from education to academia. This work presents an interpretable statistical feature for distinguishing human written and LLMs generated dialogue. We introduce a lightweight metric derived from semantic categories distribution. Using the […]
GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
arXiv:2603.19677v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the underlying communication topology that coordinates agent interactions. Within these systems, successful problem-solving often necessitates task-specific group structures to divide and conquer subtasks. However, most existing approaches generate communication […]
Guiding Diffusion-based Reconstruction with Contrastive Signals for Balanced Visual Representation
arXiv:2603.04803v2 Announce Type: replace-cross Abstract: The limited understanding capacity of the visual encoder in Contrastive Language-Image Pre-training (CLIP) has become a key bottleneck for downstream performance. This capacity includes both Discriminative Ability (D-Ability), which reflects class separability, and Detail Perceptual Ability (P-Ability), which focuses on fine-grained visual cues. Recent solutions use diffusion models to enhance […]