Ai Safety
Newsom signs executive order requiring AI companies to have safety, privacy guardrails
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Reddit - Artificial Intelligence
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1 min
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AI Safety & Ethics
Alignment, bias, regulation, and responsible AI
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![[2511.16417] Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling of ESG Report](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Ai Safety
[2511.16417] Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling of ESG Report
Abstract page for arXiv paper 2511.16417: Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling...
arXiv - AI
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4 min
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Llms
[2510.08847] What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment
Abstract page for arXiv paper 2510.08847: What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment
arXiv - AI
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4 min
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All Content
Machine Learning
[2602.22488] Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection Under Resource Constraints
This article evaluates transfer learning models for IoT DDoS detection, focusing on explainability and resource constraints. It analyzes ...
arXiv - AI
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3 min
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Llms
[2602.22481] Sydney Telling Fables on AI and Humans: A Corpus Tracing Memetic Transfer of Persona between LLMs
This article explores the relationship between AI and humans through the lens of large language models (LLMs), focusing on the Sydney per...
arXiv - AI
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4 min
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Llms
[2602.22450] Silent Egress: When Implicit Prompt Injection Makes LLM Agents Leak Without a Trace
The paper discusses the security risks posed by implicit prompt injection in large language model (LLM) agents, demonstrating how adversa...
arXiv - AI
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4 min
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Nlp
[2602.22449] A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection
This paper presents a novel framework combining BanglaBERT and a two-layer stacked LSTM for effective multi-label cyberbullying detection...
arXiv - Machine Learning
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4 min
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Llms
[2602.22427] HubScan: Detecting Hubness Poisoning in Retrieval-Augmented Generation Systems
The paper presents HubScan, a tool designed to detect hubness poisoning in Retrieval-Augmented Generation systems, addressing a critical ...
arXiv - AI
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4 min
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Nlp
[2602.22282] Differentially Private Truncation of Unbounded Data via Public Second Moments
This paper presents a novel approach to differentially private data truncation using public second moments, enhancing privacy without com...
arXiv - Machine Learning
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4 min
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Llms
[2602.22246] Self-Purification Mitigates Backdoors in Multimodal Diffusion Language Models
This article presents a framework called DiSP (Diffusion Self-Purification) to mitigate backdoor attacks in Multimodal Diffusion Language...
arXiv - Machine Learning
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4 min
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Llms
[2602.22347] Enabling clinical use of foundation models in histopathology
This article discusses the application of foundation models in histopathology, highlighting a novel approach that improves robustness and...
arXiv - AI
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4 min
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Llms
[2602.22236] CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction
The article presents CrossLLM-Mamba, a novel framework for RNA interaction prediction that utilizes multimodal state space fusion of larg...
arXiv - Machine Learning
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4 min
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Llms
[2602.23353] SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
The paper introduces SOTAlign, a semi-supervised framework for aligning unimodal vision and language models using minimal paired data and...
arXiv - AI
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4 min
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Machine Learning
[2602.22258] Poisoned Acoustics
The paper 'Poisoned Acoustics' explores training-data poisoning attacks on deep neural networks, demonstrating significant vulnerabilitie...
arXiv - AI
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3 min
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Machine Learning
[2602.23336] Differentiable Zero-One Loss via Hypersimplex Projections
This paper presents a novel differentiable approximation to the zero-one loss, enhancing gradient-based optimization in machine learning ...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.23296] Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
This article presents FedWQ-CP, a novel approach to federated uncertainty quantification that addresses dual heterogeneity in data and mo...
arXiv - Machine Learning
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4 min
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Llms
[2602.22242] Analysis of LLMs Against Prompt Injection and Jailbreak Attacks
This paper analyzes the vulnerabilities of Large Language Models (LLMs) to prompt injection and jailbreak attacks, evaluating various def...
arXiv - AI
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3 min
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Machine Learning
[2602.22238] TT-SEAL: TTD-Aware Selective Encryption for Adversarially-Robust and Low-Latency Edge AI
The paper presents TT-SEAL, a selective encryption framework designed for Tensor-Train Decomposed (TTD) networks, enhancing security and ...
arXiv - AI
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3 min
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Machine Learning
[2602.22235] Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling
This article presents a novel approach for unsupervised denoising of diffusion-weighted images (dMRI) by addressing noise bias and varian...
arXiv - AI
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4 min
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Llms
[2602.22221] Misinformation Exposure in the Chinese Web: A Cross-System Evaluation of Search Engines, LLMs, and AI Overviews
This article evaluates misinformation exposure on the Chinese web by comparing traditional search engines, LLMs, and AI-generated overvie...
arXiv - AI
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3 min
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Llms
[2602.23164] MetaOthello: A Controlled Study of Multiple World Models in Transformers
The paper presents MetaOthello, a study exploring how transformers manage multiple world models through a controlled suite of Othello var...
arXiv - Machine Learning
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4 min
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Machine Learning
[2602.23128] Bound to Disagree : Generalization Bounds via Certifiable Surrogates
The paper presents new disagreement-based certificates for generalization bounds in deep learning models, addressing limitations of exist...
arXiv - Machine Learning
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3 min
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Machine Learning
[2602.23116] Regularized Online RLHF with Generalized Bilinear Preferences
This paper explores contextual online Reinforcement Learning with Human Feedback (RLHF) using a Generalized Bilinear Preference Model to ...
arXiv - Machine Learning
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3 min
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