Alignment, bias, regulation, and responsible AI
Watch me demonstrate everything I've been talking about—live, in real time. The Setup: Maestro University AI enrollment system Standard c...
Agentic AI browsers introduce new enterprise risk. Learn how AI governance helps leaders assess exposure, oversight gaps, and safe adopti...
Greetings all - I've posted mostly in r/claudecode and r/aigamedev a couple of times previously. Working with CC for personal projects re...
![[2506.14261] RL-Obfuscation: Can Language Models Learn to Evade Latent-Space Monitors?](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This article explores RL-Obfuscation, a method for training language models to evade latent-space monitors that detect undesirable behavi...
This article presents a convolutional neural network model designed to automate the detection of vulnerabilities in C source code, achiev...
This article reviews adversarial transferability in image classification, proposing a standardized framework for evaluating transfer-base...
This paper presents a theoretical framework for accelerating risk-averse policy evaluation in partially observable Markov decision proces...
This paper presents a novel approach to long-form Bengali Automatic Speech Recognition (ASR) and speaker diarization, introducing a compr...
The paper presents RaPA, a novel approach to enhance transferable targeted attacks in machine learning by utilizing random parameter prun...
The paper introduces Continual Pick-to-Learn (CoP2L), a method for continual learning that uses sample compression to mitigate catastroph...
The paper presents UnCLE, a framework that enhances model-agnostic explanation techniques by integrating concept-based approaches, offeri...
This paper presents a robust framework for Bangla Automatic Speech Recognition (ASR) and Speaker Diarization, addressing challenges in pr...
This paper introduces a novel method for label unlearning in Vertical Federated Learning (VFL), addressing privacy concerns while maintai...
This paper explores procedural fairness in machine learning, proposing a new metric for evaluation and methods to enhance fairness withou...
The paper presents FairQuant, a framework for fairness-aware mixed-precision quantization in medical image classification, optimizing bot...
The paper introduces Natural Language Declarative Prompting (NLD-P), a governance method for prompt design that addresses challenges pose...
The paper introduces TherapyProbe, a methodology for enhancing relational safety in mental health chatbots through adversarial simulation...
The paper presents Q-Tag, a novel watermarking framework for quantum circuit generative models (QCGMs), addressing the need for secure co...
This article introduces a novel LLM agent designed to assess and mitigate deanonymization risks in textual data using a method called SAL...
The paper presents AMLRIS, a novel training strategy for Referring Image Segmentation (RIS) that enhances object segmentation through ali...
AgentSentry introduces a novel framework to mitigate indirect prompt injection (IPI) in LLM agents, enhancing their security while mainta...
This study explores how modality affects preference alignment in AI systems, comparing human and synthetic evaluations of audio and text ...
The paper presents IMMACULATE, a framework for auditing large language models (LLMs) using verifiable computation to detect economic devi...
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