[P] If you're building AI agents, logs aren't enough. You need evidence.
I have built a programmable governance layer for AI agents. I am considering to open source completely. Looking for feedback. Agent demos...
Alignment, bias, regulation, and responsible AI
I have built a programmable governance layer for AI agents. I am considering to open source completely. Looking for feedback. Agent demos...
Abstract page for arXiv paper 2510.14628: RLAIF-SPA: Structured AI Feedback for Semantic-Prosodic Alignment in Speech Synthesis
Abstract page for arXiv paper 2504.05995: NativQA Framework: Enabling LLMs and VLMs with Native, Local, and Everyday Knowledge
The paper introduces the Alignment Adapter (AlAd), a method to enhance the performance of compressed deep learning models by aligning the...
OPBench introduces a comprehensive benchmark for evaluating graph learning methods aimed at addressing the opioid crisis, featuring five ...
This paper presents a zero-shot framework for target verification and tactical reasoning in autonomous edge robotics, addressing challeng...
The paper discusses the challenges of ensuring compliance with data deletion requests in AI systems, proposing a novel economic framework...
This paper presents an explainable active learning framework for medical imaging that enhances data efficiency and interpretability by in...
WildfireVLM introduces an AI framework for early wildfire detection and risk assessment using satellite imagery, enhancing disaster manag...
This article presents PCReg-Net, a novel framework for high-fidelity alignment in bidirectional photoacoustic microscopy, significantly i...
This paper explores 'silent inconsistency' in data-parallel fine-tuning of large language models, identifying optimization misalignments ...
The paper discusses 'spectral collapse' in diffusion inversion, highlighting failures in standard deterministic methods for image transla...
Agent Mars presents a multi-agent simulation framework designed for efficient coordination in Mars base operations, addressing challenges...
The paper explores the impact of incomplete reasoning in large language models (LLMs), revealing how different reasoning modalities affec...
This article presents a unified framework for evaluating the robustness of machine-learning interpretability, specifically in the context...
This article explores Explanatory Interactive Machine Learning (XIL) as a method to mitigate bias in visual gender classification, demons...
This article presents a large-scale study of Moltbook, an AI-only social platform, revealing how AI agents create complex social structur...
The paper presents WIMLE, a model-based reinforcement learning method that enhances sample efficiency by addressing model errors and unce...
This article explores the integration of Conformal Signal Temporal Logic (CSTL) in reinforcement learning (RL) for enhancing safety and r...
This article examines implicit biases in large language models (LLMs) against transgender populations, highlighting disparities in health...
The paper examines the trustworthiness of transformer architectures in high-stakes applications, analyzing their reliability, interpretab...
This article discusses a global audit of Large Language Models (LLMs) focusing on geographic and socioeconomic biases in AI governance, h...
The paper discusses the concept of Responsible AI in business, focusing on its implementation in small and medium-sized enterprises. It c...
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