Tootfinder

AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA)
Alexandre Erler, Vincent C. M\"uller
https://arxiv.org/abs/2508.16642

AI as IA: The use and abuse of artificial intelligence (AI) for human enhancement through intellectual augmentation (IA)
This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liber…

Augmentation Technologies and AI - An Ethical Design Futures Framework
Ann Hill Duin, Isabel Pedersen
https://arxiv.org/abs/2508.16615 https://arxiv.org/pd…

Augmentation Technologies and AI - An Ethical Design Futures Framework
Augmentation technologies, fueled by Artificial Intelligence (AI), are undergoing a process of adaptation and normalization geared to everyday users in various roles as practitioners, educators, and students. While new innovations, applications, and algorithms are developed as augmentation technology, Chapter 1 focuses on human subjects, contexts, and rhetorical strategies proposed for them by external actors. The chapter discusses core functions of technical and professional communication and …

Development of Mental Models in Human-AI Collaboration: A Conceptual Framework
Joshua Holstein, Gerhard Satzger
https://arxiv.org/abs/2510.08104 https://ar…

Development of Mental Models in Human-AI Collaboration: A Conceptual Framework
Artificial intelligence has become integral to organizational decision-making and while research has explored many facets of this human-AI collaboration, the focus has mainly been on designing the AI agent(s) and the way the collaboration is set up - generally assuming a human decision-maker to be "fixed". However, it has largely been neglected that decision-makers' mental models evolve through their continuous interaction with AI systems. This paper addresses this gap by conceptualizing how th…

An Approach to Grounding AI Model Evaluations in Human-derived Criteria
Sasha Mitts
https://arxiv.org/abs/2509.04676 https://arxiv.org/pdf/2509.04676

An Approach to Grounding AI Model Evaluations in Human-derived Criteria
In the rapidly evolving field of artificial intelligence (AI), traditional benchmarks can fall short in attempting to capture the nuanced capabilities of AI models. We focus on the case of physical world modeling and propose a novel approach to augment existing benchmarks with human-derived evaluation criteria, aiming to enhance the interpretability and applicability of model behaviors. Grounding our study in the Perception Test and OpenEQA benchmarks, we conducted in-depth interviews and large…

ARMimic: Learning Robotic Manipulation from Passive Human Demonstrations in Augmented Reality
Rohan Walia (Department of Computer Engineering, Munich Institute of Robotics and Machine Intelligence), Yusheng Wang (Department of Precision Engineering, Research into Artifacts, Center for Engineering), Ralf R\"omer (Department of Computer Engineering, Munich Institute of Robotics and Machine Intelligence), Masahiro Nishio (Toyota Motor Corporation, Japan), Angela P. Schoellig (Departm…

ARMimic: Learning Robotic Manipulation from Passive Human Demonstrations in Augmented Reality
Imitation learning is a powerful paradigm for robot skill acquisition, yet conventional demonstration methods--such as kinesthetic teaching and teleoperation--are cumbersome, hardware-heavy, and disruptive to workflows. Recently, passive observation using extended reality (XR) headsets has shown promise for egocentric demonstration collection, yet current approaches require additional hardware, complex calibration, or constrained recording conditions that limit scalability and usability. We pre…

Delay-Augmented Stacked Intelligent Surfaces: Potential, Challenges, and Opportunities
Hibatallah Alwazani, Omran Abbas, Loic Markley, Anas Chaaban
https://arxiv.org/abs/2510.01411

Delay-Augmented Stacked Intelligent Surfaces: Potential, Challenges, and Opportunities
Stacked intelligent surfaces (SIS)s have been proposed recently as an enabling technology for Holographic Multiple Input Multiple Output (HMIMO) and Ultra-massive MIMO (umMIMO) technologies. Their utility can extend beyond spatial wave-domain processing of signals if they are enhanced with strategically-tuned symbol-duration level delays to enable temporal processing as well. In this work, we introduce the idea of a delay-augmented SIS (DA-SIS). We shed light on the feasibility of realizing del…

"If George Boole is the 19th century’s AI scientist, then his contemporary machine learning engineers were Charles Babbage and Ada Lovelace. The Difference Engine, which would be frequently cited as the first example of a (mechanical) programmable digital computer if it had been built at the time, was explicitly designed to _replace_ rather than _augment_ human thought. Just as modern software engineering managers use Jira to avoid thinking about process engineering."

Douglas Hofstadter
You may be worried that I am going to talk about an author of books that are not about programming, and you are correct and incorrect. Correct, in that Hofstadter's books are not about programming (the intellectually hollow like to claim that they are not about anything at all, or that if you think you know what they are about then you did not understand them; this is untrue). Incorrect, in that Hofstadter's books and computer programs themselves are about the same thing.

Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
Kevin Song, Anand Jayarajan, Yaoyao Ding, Qidong Su, Zhanda Zhu, Sihang Liu, Gennady Pekhimenko
https://arxiv.org/abs/2508.19504

Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
Large Language Models (LLMs) agents augmented with domain tools promise to autonomously execute complex tasks requiring human-level intelligence, such as customer service and digital assistance. However, their practical deployment is often limited by their low success rates under complex real-world environments. To tackle this, prior research has primarily focused on improving the agents themselves, such as developing strong agentic LLMs, while overlooking the role of the system environment in …