arXiv:2511.11551v3 Announce Type: replace Abstract: The deployment of decision-making AI agents presents a critical challenge in maintaining alignment with human values or guidelines while operating in complex, dynamic environments. Agents trained solely to achieve their objectives may adopt harmful behavior, exposing a key trade-off between maximizing the reward function and maintaining alignment. For pre-trained agents, […]
The Loss of Control Playbook: Degrees, Dynamics, and Preparedness
arXiv:2511.15846v5 Announce Type: replace-cross Abstract: This research report addresses the absence of an actionable definition for Loss of Control (LoC) in AI systems by developing a novel taxonomy and preparedness framework. Despite increasing policy and research attention, existing LoC definitions vary significantly in scope and timeline, hindering effective LoC assessment and mitigation. To address this […]
SPFFNet: Strip Perception and Feature Fusion Spatial Pyramid Pooling for Fabric Defect Detection
arXiv:2502.01445v3 Announce Type: replace-cross Abstract: Defect detection in fabrics is critical for quality control, yet existing methods often struggle with complex backgrounds and shape-specific defects. In this paper, we propose an improved fabric defect detection model based on YOLOv11. To enhance the detection of strip defects, we introduce a Strip Perception Module (SPM) that improves […]
DGGAN: Degradation Guided Generative Adversarial Network for Real-time Endoscopic Video Enhancement
arXiv:2512.07253v1 Announce Type: cross Abstract: Endoscopic surgery relies on intraoperative video, making image quality a decisive factor for surgical safety and efficacy. Yet, endoscopic videos are often degraded by uneven illumination, tissue scattering, occlusions, and motion blur, which obscure critical anatomical details and complicate surgical manipulation. Although deep learning-based methods have shown promise in image […]
DRWKV: Focusing on Object Edges for Low-Light Image Enhancement
arXiv:2507.18594v3 Announce Type: replace-cross Abstract: Low-light image enhancement remains a challenging task, particularly in preserving object edge continuity and fine structural details under extreme illumination degradation. In this paper, we propose a novel model, DRWKV (Detailed Receptance Weighted Key Value), which integrates our proposed Global Edge Retinex (GER) theory, enabling effective decoupling of illumination and […]
Data Fusion-Enhanced Decision Transformer for Stable Cross-Domain Generalization
arXiv:2511.09173v2 Announce Type: replace-cross Abstract: Cross-domain shifts present a significant challenge for decision transformer (DT) policies. Existing cross-domain policy adaptation methods typically rely on a single simple filtering criterion to select source trajectory fragments and stitch them together. They match either state structure or action feasibility. However, the selected fragments still have poor stitchability: state […]
Hallucination reduction with CASAL: Contrastive Activation Steering For Amortized Learning
arXiv:2510.02324v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit impressive capabilities but often hallucinate, confidently providing incorrect answers instead of admitting ignorance. Prior work has shown that models encode linear representations of their own knowledge and that activation steering can reduce hallucinations. These approaches, however, require real-time monitoring and intervention during inference. We introduce […]
IFFair: Influence Function-driven Sample Reweighting for Fair Classification
arXiv:2512.07249v1 Announce Type: cross Abstract: Because machine learning has significantly improved efficiency and convenience in the society, it’s increasingly used to assist or replace human decision-making. However, the data-based pattern makes related algorithms learn and even exacerbate potential bias in samples, resulting in discriminatory decisions against certain unprivileged groups, depriving them of the rights to […]
ProofBridge: Auto-Formalization of Natural Language Proofs in Lean via Joint Embeddings
arXiv:2510.15681v2 Announce Type: replace-cross Abstract: Translating human-written mathematical theorems and proofs from natural language (NL) into formal languages (FLs) like Lean 4 has long been a significant challenge for AI. Most state-of-the-art methods either focus on theorem-only NL-to-FL auto-formalization or on FL proof synthesis from FL theorems. In practice, auto-formalization of both theorem and proof […]
HN-MVTS: HyperNetwork-based Multivariate Time Series Forecasting
arXiv:2511.08340v2 Announce Type: replace-cross Abstract: Accurate forecasting of multivariate time series data remains a formidable challenge, particularly due to the growing complexity of temporal dependencies in real-world scenarios. While neural network-based models have achieved notable success in this domain, complex channel-dependent models often suffer from performance degradation compared to channel-independent models that do not consider […]