Alignment, bias, regulation, and responsible AI
Greetings all - I've posted mostly in r/claudecode and r/aigamedev a couple of times previously. Working with CC for personal projects re...
AI adoption is rising in the U.S., but trust remains low, with most Americans concerned about transparency, regulation, and the technolog...
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The article discusses Anthropic's decision to decline a deal with the Pentagon, highlighting concerns over user security and ethical impl...
Alex Bores discusses the RAISE Act and the influence of super PACs on AI regulation in the U.S. during his appearance on TechCrunch's Equ...
The article discusses the ongoing conflict between AI companies, particularly Anthropic, and the Pentagon over military contract terms th...
The article discusses the Pentagon's ultimatum to Anthropic regarding military access to AI technology, raising ethical concerns among te...
Euria, an AI developed by Infomaniak in Switzerland, utilizes server heat for district heating, presenting an eco-friendly alternative to...
The article discusses the recent market turmoil triggered by a report predicting significant job losses due to AI, highlighting Wall Stre...
Over 360 employees from Google and OpenAI have signed an open letter supporting Anthropic's stance against the Pentagon's demands for AI ...
The article discusses the forensic intelligence system that maps risk exposures related to enterprise AI transitions, highlighting a $2.5...
Anthropic has rejected the Pentagon's demand to remove AI safeguards for its model Claude, aiming to prevent its use in mass surveillance...
The Pentagon is advancing its efforts to develop AI tools aimed at enhancing cyber operations against China, focusing on improving nation...
![[2511.05898] Q$^2$: Quantization-Aware Gradient Balancing and Attention Alignment for Low-Bit Quantization](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents Q$^2$, a novel framework addressing gradient imbalance in low-bit quantization for complex visual tasks, enhancing per...
This paper explores the use of Small Language Models (SLMs) for translating natural language queries into Kusto Query Language (KQL) in S...
The paper presents DropVLA, an action-level backdoor attack on Vision-Language-Action models, demonstrating how minimal data poisoning ca...
The paper 'BioBlue' investigates the failure modes of LLMs in multi-objective scenarios, revealing that they can exhibit runaway optimiza...
The paper presents Dyslexify, a novel defense mechanism against typographic attacks in CLIP models, enhancing robustness without finetuni...
The paper presents a novel attack method, Adversarial PhoneTic Prompting (APT), that exploits phonetic memorization in generative AI syst...
The paper introduces Temporal Sparse Autoencoders (T-SAEs), enhancing interpretability in language models by leveraging the sequential na...
The paper presents Supervised Reinforcement Learning (SRL), a framework that enhances reasoning in Large Language Models (LLMs) by reform...
The paper presents an atlas-free brain network transformer (BNT) that improves brain network analysis by utilizing individualized brain p...
This article presents a novel feature selection method for a lightweight intrusion detection system (IDS) aimed at early detection of Adv...
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