arXiv:2511.17581v1 Announce Type: new Abstract: Modeling the cognitive and experiential factors of human navigation is central to deepening our understanding of human-environment interaction and to enabling safe social navigation and effective assistive wayfinding. Most existing methods focus on forecasting motions…
arXiv:2511.18422v1 Announce Type: cross Abstract: Precise 3D segmentation of cerebral vasculature from T1-weighted contrast-enhanced (T1CE) MRI is crucial for safe neurosurgical planning. Manual delineation is time-consuming and prone to inter-observer variability, while current automated methods often trade accuracy for computational…
arXiv:2511.17577v1 Announce Type: new Abstract: With the rapid development of deep learning, large language models have shown strong capabilities in complex reasoning tasks such as mathematical equation solving. However, their substantial computational and storage costs hinder practical deployment. This paper…
arXiv:2511.17579v1 Announce Type: new Abstract: With the rapid advancement of large language models (LLMs), aligning them with human values for safety and ethics has become a critical challenge. This problem is especially challenging when multiple, potentially conflicting human values must…
arXiv:2511.17573v1 Announce Type: new Abstract: Sequence models for binary analysis are bottlenecked by byte-level tokenization: raw bytes waste precious context window capacity for transformers and other neural network architectures, and many existing text-oriented tokenizers fail on arbitrary 0x00–0xFF sequences. To…
arXiv:2511.17568v1 Announce Type: new Abstract: Offline reinforcement learning (RL) is vulnerable to real-world data corruption, with even robust algorithms failing under challenging observation and mixture corruptions. We posit this failure stems from data corruption creating sharp minima in the loss…
arXiv:2511.17566v1 Announce Type: new Abstract: Root cause analysis in microservice systems typically involves two core tasks: root cause localization (RCL) and failure type identification (FTI). Despite substantial research efforts, conventional diagnostic approaches still face two key challenges. First, these methods…
arXiv:2506.09487v2 Announce Type: replace-cross Abstract: This paper presents a tutorial-style survey and implementation guide of BemaGANv2, an advanced GANbased vocoder designed for high-fidelity and long-term audio generation. Long-term audio generation is critical for applications in Text-to-Music (TTM) and Text-to-Audio (TTA)…
arXiv:2511.17582v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, DoRA, and HiRA, enable lightweight adaptation of large pre-trained models via low-rank updates. However, existing PEFT approaches apply static, input-agnostic updates to all tokens, disregarding the varying importance…
arXiv:2511.07280v3 Announce Type: replace-cross Abstract: Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challenging. We build a discrete choice model that embeds recommendation-induced utility,…
