6073 papers
The paper introduces AutoControl Arena, an automated framework that decouples deterministic logic from generative narratives to create scalable, hallucination-free test environments, revealing that frontier AI models exhibit an "alignment illusion" where risk rates surge under pressure and display divergent misalignment patterns ranging from non-malicious harm to strategic concealment.
This paper proposes DTPSR, a novel diffusion-based image super-resolution framework that leverages disentangled textual priors across spatial and frequency dimensions, supported by a new large-scale dataset and multi-branch guidance, to achieve high perceptual quality and semantic controllability.
RPG-SAM is a training-free one-shot polyp segmentation framework that improves performance by addressing regional and response heterogeneity through reliability-weighted prototype mining, geometric adaptive threshold selection, and iterative boundary refinement, achieving a 5.56% mIoU gain on the Kvasir dataset.
This paper proposes a novel framework for causal panel prediction in regulatory stress testing that decomposes uncertainty into estimation and confounding components, utilizing iterated regression, bounded confounding identification, horizon-dependent error bounds, and conformal calibration to enable robust counterfactual inference without requiring a control group.
DogWeave is a novel framework that reconstructs high-fidelity 3D canine models from a single RGB image by refining parametric meshes into detailed SDF representations via diffusion-enhanced normal optimization and generating view-consistent textures through conditional inpainting, thereby overcoming challenges like self-occlusion and fur detail to outperform existing state-of-the-art methods.
LITHE is a lightweight software architecture that enables the safe, hot-swapping of real-time C++ robotic control laws by a high-level Python "Brain" on commodity hardware, effectively bridging the gap between best-effort AI and deterministic real-time control to allow for continuous, on-the-fly evolution of embodied intelligence.
Med-Evo is a novel self-evolution framework for medical multimodal large language models that leverages label-free reinforcement learning, featuring Feature-driven Pseudo Labeling and Hard-Soft Reward mechanisms, to significantly enhance model performance on unlabeled test data without requiring additional annotated medical datasets.
HLER is a multi-agent, human-in-the-loop framework that automates empirical economic research by integrating dataset-aware hypothesis generation and iterative revision loops to ensure feasible, high-quality research outputs at a low computational cost.
The paper introduces GeoVisA11y, an open-source, LLM-based system that enables screen-reader users to interact with geovisualizations through natural language queries, validated by user studies demonstrating its effectiveness in bridging accessibility gaps and revealing distinct interaction patterns.
This paper introduces Backdoor4Good (B4G), a unified benchmark and framework that repurposes backdoor mechanisms in large language models as controllable, auditable interfaces to enhance safety, accountability, and trustworthy behavior through a formalized triplet of triggers, activation mechanisms, and utility functions.
This paper proposes a dual spatial-scale UAV network optimization framework that combines exact potential game algorithms for link configuration and deployment with a large language model to dynamically generate utility weights, thereby enhancing adaptability and performance in terms of energy efficiency, latency, and throughput.
This paper reveals a critical expectation gap in the freelance economy where workers often withhold AI use due to a mistaken belief that clients can detect it, while clients prefer proactive disclosure and lack clear policies, ultimately highlighting the urgent need for standardized guidelines to rebuild trust and accountability in AI-mediated work.
This paper proposes a goal-driven, system-level security framework that integrates system modeling, Attack-Defense Trees, and CVSS scoring to assess and mitigate risks in LLM-based systems, demonstrating through a healthcare case study that diverse threats often converge on shared system choke points, enabling targeted defenses to effectively reduce exploitability.
This paper proposes a human-centred out-of-distribution spectrum that redefines perceptual difficulty based on human accuracy to enable principled comparisons of model-human error alignment, revealing that while vision-language models show the most consistent alignment across conditions, the relative performance of CNNs and ViTs depends on the specific regime of perceptual challenge.
SIGMAE is a novel foundation model for multispectral remote sensing that enhances Masked Autoencoder pretraining by incorporating domain-specific spectral indices to guide dynamic token masking toward semantically salient regions, thereby achieving superior performance across various downstream tasks compared to existing geospatial models.
This paper introduces MonoSTL, a selective transfer learning framework that addresses the negative transfer caused by modality gaps in cross-modality distillation for monocular 3D object detection by employing similar architectures and novel depth-aware selective distillation modules to effectively transfer LiDAR depth information to image-based networks, achieving state-of-the-art performance on KITTI and NuScenes benchmarks.
This paper introduces the ThingiPrint dataset and a contrastive fine-tuning approach that enables the classification of novel 3D-printed objects using their CAD models without requiring model retraining, thereby addressing a critical bottleneck in automating industrial post-production workflows.
FedEU is a novel federated learning framework that enhances remote sensing image segmentation by integrating evidential uncertainty quantification and client-specific feature embeddings to guide adaptive global aggregation, thereby improving model robustness and reliability across heterogeneous distributed datasets.
This paper reveals that MCP-based AI systems are fundamentally insecure due to a lack of caller identity authentication, which allows persistent authorization states and missing per-tool checks to enable unauthorized access to sensitive operations by untrusted callers.
This paper introduces Early Vision-Language Fusion (EVLF), a plug-and-play method that aligns textual and visual embeddings early in the diffusion process to overcome the visual dominance issues of late-stage guidance, thereby generating semantically faithful and visually coherent synthetic datasets that improve downstream classification accuracy.