Alignment, bias, regulation, and responsible AI
![[2511.21331] The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2511.21331: The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment
Abstract page for arXiv paper 2509.22367: What Is The Political Content in LLMs' Pre- and Post-Training Data?
Abstract page for arXiv paper 2507.22264: SmartCLIP: Modular Vision-language Alignment with Identification Guarantees
The article argues that while superintelligence may not be a total myth, current AI technologies are far from achieving it, questioning t...
The Vergecast discusses the FCC's chilling impact on late-night TV, focusing on CBS's decision to prevent Stephen Colbert from airing an ...
The article discusses the current dynamics in the AI job market, highlighting how top talent is increasingly motivated by ideological mis...
The SoftDTW-CUDA for PyTorch package offers a fast and memory-efficient implementation of Soft Dynamic Time Warping, optimized for GPU us...
At the India AI Impact Summit, OpenAI's Sam Altman and Anthropic's Dario Amodei notably refrained from joining a unity gesture led by Pri...
The article discusses the dual impact of AI coding tools on open-source software, highlighting both the ease of feature development and t...
EVMbench is an open-source benchmark developed by OpenAI and Paradigm to evaluate AI agents' capabilities in handling smart contract secu...
This edition of The Download covers advancements in autonomous narco submarines, ethical concerns surrounding AI chatbots, and the evolvi...
This paper explores the dynamics of iterative training on contaminated data sources, demonstrating that model performance can improve des...
The paper introduces Randomized Masked Finetuning (RMFT), a technique designed to reduce the memorization of personally identifiable info...
This paper explores the effectiveness of test-time verification over policy learning in enhancing Vision-Language-Action (VLA) alignment,...
This paper explores the integration of vision-language models in autonomous driving, focusing on safety assessment and decision-making th...
This article examines whether vision-language models (VLMs) respect contextual integrity when disclosing location information, highlighti...
The paper presents VerifiableFL, a system for federated learning that ensures verifiable claims about model training using exclaves, enha...
This article presents a refined Bayesian optimization framework for efficient beam alignment in intelligent indoor wireless environments,...
This paper explores the importance of instruction hierarchy in large language models (LLMs) for enhancing their controllability and relia...
The paper explores the challenges of spatio-temporal models in machine learning, focusing on biases in temporal attention mechanisms and ...
The paper introduces a novel approach to using features as rewards in reinforcement learning for open-ended tasks, focusing on reducing h...
The paper presents adaptive exploration strategies for latent-state bandits, addressing challenges in reward estimation and action select...
The article presents PolicyPad, an interactive system designed for collaborative prototyping of policies governing large language models ...
Get the latest news, tools, and insights delivered to your inbox.
Daily or weekly digest • Unsubscribe anytime