arXiv:2604.12311v1 Announce Type: cross Abstract: The emergence of vibe coding, a paradigm where non-technical users instruct Large Language Models (LLMs) to generate executable codes via natural language, presents both significant opportunities and severe risks for the construction industry. While empowering construction personnel such as the safety managers, foremen, and workers to develop tools and software, […]
Long-Horizon Plan Execution in Large Tool Spaces through Entropy-Guided Branching
arXiv:2604.12126v1 Announce Type: new Abstract: Large Language Models (LLMs) have significantly advanced tool-augmented agents, enabling autonomous reasoning via API interactions. However, executing multi-step tasks within massive tool libraries remains challenging due to two critical bottlenecks: (1) the absence of rigorous, plan-level evaluation frameworks and (2) the computational demand of exploring vast decision spaces stemming from […]
FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation
arXiv:2604.12344v1 Announce Type: cross Abstract: The exponential growth of data from modern radio telescopes presents a significant challenge to traditional single-pulse search algorithms, which are computationally intensive and prone to high false-positive rates due to Radio Frequency Interference (RFI). In this work, we introduce FRTSearch, an end-to-end framework unifying the detection and physical characterization of […]
INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT
arXiv:2512.14732v2 Announce Type: replace-cross Abstract: Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established guidelines. Traditional manual inspection by radiologists is time-consuming and variable. This paper proposes a novel framework that leverages large language models (LLMs) and foundational vision-language models (VLMs) in a plan-and-execute agentic […]
SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models
arXiv:2604.12377v1 Announce Type: cross Abstract: Korean is a morphologically rich language with a featural writing system in which each character is systematically composed of subcharacter units known as Jamo. These subcharacters not only determine the visual structure of Korean but also encode frequent and linguistically meaningful morphophonological processes. However, most current Korean language models (LMs) […]
Aethon: A Reference-Based Replication Primitive for Constant-Time Instantiation of Stateful AI Agents
arXiv:2604.12129v1 Announce Type: new Abstract: The transition from stateless model inference to stateful agentic execution is reshaping the systems assumptions underlying modern AI infrastructure. While large language models have made persistent, tool-using, and collaborative agents technically viable, existing runtime architectures remain constrained by materialization-heavy instantiation models that impose significant latency and memory overhead. This paper […]
RACF: A Resilient Autonomous Car Framework with Object Distance Correction
arXiv:2604.12418v1 Announce Type: cross Abstract: Autonomous vehicles are increasingly deployed in safety-critical applications, where sensing failures or cyberphysical attacks can lead to unsafe operations resulting in human loss and/or severe physical damages. Reliable real-time perception is therefore critically important for their safe operations and acceptability. For example, vision-based distance estimation is vulnerable to environmental degradation […]
Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage
arXiv:2603.08819v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems combine document retrieval with a generative model to address complex information seeking tasks like report generation. While the relationship between retrieval quality and generation effectiveness seems intuitive, it has not been systematically studied. We investigate whether upstream retrieval metrics can serve as reliable early indicators of […]
Euler-inspired Decoupling Neural Operator for Efficient Pansharpening
arXiv:2604.12463v1 Announce Type: cross Abstract: Pansharpening aims to synthesize high-resolution multispectral (HR-MS) images by fusing the spatial textures of panchromatic (PAN) images with the spectral information of low-resolution multispectral (LR-MS) images. While recent deep learning paradigms, especially diffusion-based operators, have pushed the performance boundaries, they often encounter spectral-spatial blurring and prohibitive computational costs due to […]
Towards Platonic Representation for Table Reasoning: A Foundation for Permutation-Invariant Retrieval
arXiv:2604.12133v1 Announce Type: new Abstract: Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Natural Language Processing (NLP). We argue that this linearization of tables discards their essential geometric and relational structure, creating representations that are brittle to layout permutations. This paper introduces the Platonic Representation Hypothesis (PRH) for tables, […]
Social Learning Strategies for Evolved Virtual Soft Robots
arXiv:2604.12482v1 Announce Type: cross Abstract: Optimizing the body and brain of a robot is a coupled challenge: the morphology determines what control strategies are effective, while the control parameters influence how well the morphology performs. This joint optimization can be done through nested loops of evolutionary and learning processes, where the control parameters of each […]
MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization
arXiv:2604.06798v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binarization provides extreme efficiency, yet existing binary methods designed for dense LLMs struggle with MoE-specific issues, including cross-expert redundancy, task-agnostic importance estimation, and quantization-induced routing shifts. To this end, we propose […]