arXiv:2603.13036v1 Announce Type: cross Abstract: Generative AI is known for its tendency to homogenize, often reproducing dominant style conventions found in training data. However, it remains unclear how these homogenizing effects extend to complex structural tasks like web design. As lay creators increasingly turn to LLMs to ‘vibe-code’ websites — prompting for aesthetic and functional […]
Clustering Astronomical Orbital Synthetic Data Using Advanced Feature Extraction and Dimensionality Reduction Techniques
arXiv:2603.13177v1 Announce Type: cross Abstract: The dynamics of Saturn’s satellite system offer a rich framework for studying orbital stability and resonance interactions. Traditional methods for analysing such systems, including Fourier analysis and stability metrics, struggle with the scale and complexity of modern datasets. This study introduces a machine learning-based pipeline for clustering approximately 22,300 simulated […]
Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation
arXiv:2603.12581v1 Announce Type: cross Abstract: Although diffusion models have achieved remarkable progress in multi-modal magnetic resonance imaging (MRI) translation tasks, existing methods still tend to suffer from anatomical inconsistencies or degraded texture details when handling arbitrary missing-modality scenarios. To address these issues, we propose a latent diffusion-based multi-modal MRI translation framework, termed MSG-LDM. By leveraging […]
ODRL Policy Comparison Through Normalisation
arXiv:2603.12926v1 Announce Type: new Abstract: The ODRL language has become the standard for representing policies and regulations for digital rights. However its complexity is a barrier to its usage, which has caused many related theoretical and practical works to focus on different, and not interoperable, fragments of ODRL. Moreover, semantically equivalent policies can be expressed […]
Human-AI Governance (HAIG): A Trust-Utility Approach
arXiv:2505.01651v3 Announce Type: replace Abstract: This paper introduces the Human-AI Governance (HAIG) framework, contributing to the AI governance (AIG) field by foregrounding the relational dynamics between human and AI actors rather than treating AI systems as objects of governance alone. Current categorical frameworks (e.g., human-in-the-loop models) inadequately capture how AI systems evolve from tools to […]
Optimize Wider, Not Deeper: Consensus Aggregation for Policy Optimization
arXiv:2603.12596v1 Announce Type: cross Abstract: Proximal policy optimization (PPO) approximates the trust region update using multiple epochs of clipped SGD. Each epoch may drift further from the natural gradient direction, creating path-dependent noise. To understand this drift, we can use Fisher information geometry to decompose policy updates into signal (the natural gradient projection) and waste […]
Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
arXiv:2603.12933v1 Announce Type: new Abstract: Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the quality–cost trade-off space. Despite these advances, real-world deployment is often constrained by high inference cost, latency, and limited transparency, which hinders scalable and efficient routing. Existing […]
Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning
arXiv:2603.12606v1 Announce Type: cross Abstract: Current vision-language detection and grounding models predominantly focus on prompts with positive semantics and often struggle to accurately interpret and ground complex expressions containing negative semantics. A key reason for this limitation is the lack of high-quality training data that explicitly captures discriminative negative samples and negation-aware language descriptions. To […]
Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions
arXiv:2508.21742v2 Announce Type: replace Abstract: Understanding causal relations between temporal variables is a central challenge in time series analysis, particularly when the full causal structure is unknown. Even when the full causal structure cannot be fully specified, experts often succeed in providing a high-level abstraction of the causal graph, known as a summary causal graph, […]
FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control
arXiv:2603.12612v1 Announce Type: cross Abstract: Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a formidable challenge, as the “curse of dimensionality” induces severe exploration inefficiency and training instability in expansive action spaces. Consequently, recent high-throughput paradigms have largely converged on deterministic policy gradients combined with massive parallel simulation. We challenge this compromise […]
Structured Distillation for Personalized Agent Memory: 11x Token Reduction with Retrieval Preservation
arXiv:2603.13017v1 Announce Type: new Abstract: Long conversations with an AI agent create a simple problem for one user: the history is useful, but carrying it verbatim is expensive. We study personalized agent memory: one user’s conversation history with an agent, distilled into a compact retrieval layer for later search. Each exchange is compressed into a […]
When Drafts Evolve: Speculative Decoding Meets Online Learning
arXiv:2603.12617v1 Announce Type: cross Abstract: Speculative decoding has emerged as a widely adopted paradigm for accelerating large language model inference, where a lightweight draft model rapidly generates candidate tokens that are then verified in parallel by a larger target model. However, due to limited model capacity, drafts often struggle to approximate the target distribution, resulting […]