arXiv:2402.06118v4 Announce Type: replace-cross Abstract: By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities. However, the generated text often suffers from inaccurate grounding in the visual input, resulting in errors such as hallucination of […]
What-If Decision Support for Product Line Extension Using Conditional Deep Generative Models
arXiv:2511.11646v2 Announce Type: replace-cross Abstract: Product line extension is a strategically important managerial decision that requires anticipating how consumer segments and purchasing contexts may respond to hypothetical product designs that do not yet exist in the market. Such decisions are inherently uncertain because managers must infer future outcomes from historical purchase data without direct market […]
Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
arXiv:2512.18092v1 Announce Type: new Abstract: Neuron identification is a popular tool in mechanistic interpretability, aiming to uncover the human-interpretable concepts represented by individual neurons in deep networks. While algorithms such as Network Dissection and CLIP-Dissect achieve great empirical success, a rigorous theoretical foundation remains absent, which is crucial to enable trustworthy and reliable explanations. In […]
Accelerated Digital Twin Learning for Edge AI: A Comparison of FPGA and Mobile GPU
arXiv:2512.17941v1 Announce Type: cross Abstract: Digital twins (DTs) can enable precision healthcare by continually learning a mathematical representation of patient-specific dynamics. However, mission critical healthcare applications require fast, resource-efficient DT learning, which is often infeasible with existing model recovery (MR) techniques due to their reliance on iterative solvers and high compute/memory demands. In this paper, […]
Rethinking Multi-Agent Intelligence Through the Lens of Small-World Networks
arXiv:2512.18094v1 Announce Type: new Abstract: Large language models (LLMs) have enabled multi-agent systems (MAS) in which multiple agents argue, critique, and coordinate to solve complex tasks, making communication topology a first-class design choice. Yet most existing LLM-based MAS either adopt fully connected graphs, simple sparse rings, or ad-hoc dynamic selection, with little structural guidance. In […]
MEEA: Mere Exposure Effect-Driven Confrontational Optimization for LLM Jailbreaking
arXiv:2512.18755v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has intensified concerns about the robustness of their safety alignment. While existing jailbreak studies explore both single-turn and multi-turn strategies, most implicitly assume a static safety boundary and fail to account for how contextual interactions dynamically influence model behavior, leading to limited […]
The Subject of Emergent Misalignment in Superintelligence: An Anthropological, Cognitive Neuropsychological, Machine-Learning, and Ontological Perspective
arXiv:2512.17989v1 Announce Type: new Abstract: We examine the conceptual and ethical gaps in current representations of Superintelligence misalignment. We find throughout Superintelligence discourse an absent human subject, and an under-developed theorization of an “AI unconscious” that together are potentiality laying the groundwork for anti-social harm. With the rise of AI Safety that has both thematic […]
HARBOR: Holistic Adaptive Risk assessment model for BehaviORal healthcare
arXiv:2512.18829v1 Announce Type: new Abstract: Behavioral healthcare risk assessment remains a challenging problem due to the highly multimodal nature of patient data and the temporal dynamics of mood and affective disorders. While large language models (LLMs) have demonstrated strong reasoning capabilities, their effectiveness in structured clinical risk scoring remains unclear. In this work, we introduce […]
Conflict-Driven Clause Learning with VSIDS Heuristics for Discrete Facility Layout
arXiv:2512.18034v1 Announce Type: new Abstract: This paper studies the use of Conflict-Driven Clause Learning (CDCL) with VSIDS heuristics as a computational engine for discrete facility layout problems. The facility layout problem is modeled as a combinatorial assignment problem with dense logical structure arising from adjacency, separation, and slot-availability constraints. We develop a CNF-based formulation for […]
Gabliteration: Adaptive Multi-Directional Neural Weight Modification for Selective Behavioral Alteration in Large Language Models
arXiv:2512.18901v1 Announce Type: new Abstract: We present Gabliteration, a novel neural weight modification technique that advances beyond traditional abliteration methods by implementing adaptive multi-directional projections with regularized layer selection. Our approach addresses the fundamental limitation of existing methods that compromise model quality while attempting to modify specific behavioral patterns. Through dynamic layer optimization, regularized projection […]