arXiv:2603.06088v1 Announce Type: cross Abstract: Human problem-solving is enriched by a diversity of styles and personality traits, yet the development of Large Language Models (LLMs) has largely prioritized uniform performance benchmarks that favour specific behavioural tendencies such as assertiveness. To investigate how diverse experiences shape machine personality and influence problem-solving, this study employs continued pre-training […]
SGDFuse: SAM-Guided Diffusion Model for High-Fidelity Infrared and Visible Image Fusion
arXiv:2508.05264v5 Announce Type: replace-cross Abstract: Infrared and visible image fusion (IVIF) aims to combine the thermal radiation information from infrared images with the rich texture details from visible images to enhance perceptual capabilities for downstream visual tasks. However, existing methods often fail to preserve key targets due to a lack of deep semantic understanding of […]
Partial Policy Gradients for RL in LLMs
arXiv:2603.06138v1 Announce Type: cross Abstract: Reinforcement learning is a framework for learning to act sequentially in an unknown environment. We propose a natural approach for modeling policy structure in policy gradients. The key idea is to optimize for a subset of future rewards: smaller subsets represent simpler policies, which can be learned more reliably because […]
Multicellular Tumour Spheroids Exposure to Pulsed Electric Field: A Combined Experimental and Mathematical Modelling Study Highlighting Temporal Dynamics of DAMP Release and Accelerated Regrowth at Intermediate Field Intensities
arXiv:2603.06087v1 Announce Type: new Abstract: Electroporation is increasingly used as a percutaneous ablation technique for tumours located near vital structures. Although effective, tumour regrowth may still occur. At the same time, in vitro studies on cell monolayers have shown that electroporation can trigger immunogenic cell death (ICD) through the release of damage-associated molecular patterns (DAMPs). […]
Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations
arXiv:2603.06153v1 Announce Type: cross Abstract: Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with a focus on how input perturbation design affects forecast skill and uncertainty representation. We adapt […]
Whatever Remains Must Be True: Filtering Drives Reasoning in LLMs, Shaping Diversity
arXiv:2512.05962v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has become the de facto standard for tuning LLMs to solve tasks involving reasoning. However, growing evidence shows that models trained in such way often suffer from a significant loss in diversity. We argue that this arises because RL implicitly optimizes the “mode-seeking” or “zero-forcing” Reverse KL […]
Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI
arXiv:2603.06217v1 Announce Type: new Abstract: Residential demand response depends on sustained prosumer participation, yet existing coordination is either fully automated, or limited to one-way dispatch signals and price alerts that offer little possibility for informed decision-making. This paper introduces Conversational Demand Response (CDR), a coordination mechanism where aggregators and prosumers interact through bidirectional natural language, […]
Cut to the Chase: Training-free Multimodal Summarization via Chain-of-Events
arXiv:2603.06213v1 Announce Type: cross Abstract: Multimodal Summarization (MMS) aims to generate concise textual summaries by understanding and integrating information across videos, transcripts, and images. However, existing approaches still suffer from three main challenges: (1) reliance on domain-specific supervision, (2) implicit fusion with weak cross-modal grounding, and (3) flat temporal modeling without event transitions. To address […]
Why Human Guidance Matters in Collaborative Vibe Coding
arXiv:2602.10473v2 Announce Type: replace-cross Abstract: Writing code has been one of the most transformative ways for human societies to translate abstract ideas into tangible technologies. Modern AI is changing this process by enabling experts and non-experts alike to generate code without actually writing it, instead using natural language instructions or “vibe coding”. While increasingly popular, […]
Learning to Solve Orienteering Problem with Time Windows and Variable Profits
arXiv:2603.06260v1 Announce Type: cross Abstract: The orienteering problem with time windows and variable profits (OPTWVP) is common in many real-world applications and involves continuous time variables. Current approaches fail to develop an efficient solver for this orienteering problem variant with discrete and continuous variables. In this paper, we propose a learning-based two-stage DEcoupled discrete-Continuous optimization […]
Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport
arXiv:2603.06278v1 Announce Type: new Abstract: Climate change is expected to intensify rainfall and, consequently, pluvial flooding, leading to increased disruptions in urban transportation systems over the coming decades. Designing effective adaptation strategies is challenging due to the long-term, sequential nature of infrastructure investments, deep climate uncertainty, and the complex interactions between flooding, infrastructure, and mobility […]
“When to Hand Off, When to Work Together”: Expanding Human-Agent Co-Creative Collaboration through Concurrent Interaction
arXiv:2603.02050v2 Announce Type: replace-cross Abstract: Human collaborators coordinate dynamically through process visibility and workspace awareness, yet AI agents typically either provide only final outputs or expose read-only execution processes (e.g., planning, reasoning) without interpreting concurrent user actions on shared artifacts. Building on mixed-initiative interaction principles, we explore whether agents can achieve collaborative context awareness — […]