Alignment, bias, regulation, and responsible AI
RLHF trains models on human feedback. Humans rate responses they like. And it turns out humans consistently rate confident, fluent, agree...
Rep. Josh Gottheimer, who is generally tough on China, just sent a letter to Anthropic questioning their decision to reduce certain safet...
![[2512.21106] Semantic Refinement with LLMs for Graph Representations](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2512.21106: Semantic Refinement with LLMs for Graph Representations
The Nepal Police inaugurated an Artificial Intelligence and Advanced Analytics Cell (AI-AAC) to enhance crime investigation and national ...
Anthropic accuses three Chinese AI labs of conducting distillation attacks on its Claude chatbot, claiming they illicitly extracted capab...
Anthropic accuses Chinese developers of stealing AI secrets from its Claude chatbot, sparking criticism over its own data scraping practi...
CrowdStrike reassesses its position in the cybersecurity landscape following the launch of Anthropic's Claude Code Security, an AI tool t...
The paper introduces PyraTok, a language-aligned pyramidal tokenizer designed to enhance video understanding and generation by improving ...
The APEX-Agents paper introduces a benchmark for evaluating AI agents' ability to perform complex tasks across various applications, show...
This paper explores the limitations of self-improvement in large language models (LLMs), arguing that without symbolic model synthesis, t...
Interpreto is an open-source library designed for interpreting HuggingFace transformers, offering both attribution methods and concept-ba...
The paper presents MapReduce LoRA, a novel framework for optimizing generative models by addressing multi-preference alignment issues. It...
The paper presents FAST, a novel coreset selection framework that utilizes topology-aware frequency-domain distribution matching, signifi...
This article presents a market-making framework for coordinating multi-agent large language models (LLMs), enhancing trustworthiness and ...
This article reviews the state-of-the-art in agentic AI systems within electrical power engineering, providing a taxonomy and practical a...
This paper introduces a novel method for generating controllable collision scenarios for autonomous vehicles, enhancing safety evaluation...
This article presents a novel approach to safe and near-optimal control in dynamic environments, utilizing online dynamics learning to en...
The paper discusses the impact of evidence order on the performance of transformers in binary adjudication tasks, introducing metrics to ...
This article presents a computational framework for detecting early and implicit suicidal ideation on social media by analyzing user inte...
The paper introduces DITTO, a spoofing attack framework that exploits vulnerabilities in watermarked large language models (LLMs) via kno...
The paper presents a novel method for verifying Chain-of-Thought (CoT) reasoning in AI models using Circuit-based Reasoning Verification ...
This paper explores the impact of data sharing on A/B experiments in recommendation systems, focusing on how interference affects algorit...
The paper introduces SocialHarmBench, a dataset designed to evaluate the vulnerabilities of large language models (LLMs) to socially harm...
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