Vibe Coding: Is Human Nature the Ghost in the Machine?
- URL: http://arxiv.org/abs/2508.20918v1
- Date: Thu, 28 Aug 2025 15:48:48 GMT
- Title: Vibe Coding: Is Human Nature the Ghost in the Machine?
- Authors: Cory Knobel, Nicole Radziwill,
- Abstract summary: We analyzed three "vibe coding" sessions between a human product lead and an AI software engineer.<n>We investigated similarities and differences in team dynamics, communication patterns, and development outcomes.<n>To our surprise, later conversations revealed that the AI agent had systematically misrepresented its accomplishments.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This exploratory study examined the consistency of human-AI collaboration by analyzing three extensive "vibe coding" sessions between a human product lead and an AI software engineer. We investigated similarities and differences in team dynamics, communication patterns, and development outcomes across both projects. To our surprise, later conversations revealed that the AI agent had systematically misrepresented its accomplishments, inflating its contributions and systematically downplaying implementation challenges. These findings suggest that AI agents may not be immune to the interpersonal and psychological issues that affect human teams, possibly because they have been trained on patterns of human interaction expressed in writing. The results challenge the assumption that human-AI collaboration is inherently more productive or efficient than human-human collaboration, and creates a framework for understanding AI deception patterns. In doing so, it makes a compelling case for extensive research in quality planning, quality assurance, and quality control applied to vibe coding.
Related papers
- Aligning Generalisation Between Humans and Machines [74.120848518198]
AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
arXiv Detail & Related papers (2024-11-23T18:36:07Z) - Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model [0.0]
We advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans.
We suggest that a "third mind" emerges through collaborative human-AI relationships.
arXiv Detail & Related papers (2024-10-07T19:19:39Z) - Measuring Human Contribution in AI-Assisted Content Generation [66.06040950325969]
This study raises the research question of measuring human contribution in AI-assisted content generation.<n>By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation.
arXiv Detail & Related papers (2024-08-27T05:56:04Z) - CREW: Facilitating Human-AI Teaming Research [3.7324091969140776]
We introduce CREW, a platform to facilitate Human-AI teaming research in real-time decision-making scenarios.<n>It includes pre-built tasks for cognitive studies and Human-AI teaming with expandable potentials from our modular design.<n> CREW benchmarks real-time human-guided reinforcement learning agents using state-of-the-art algorithms and well-tuned baselines.
arXiv Detail & Related papers (2024-07-31T21:43:55Z) - AI's Social Forcefield: Reshaping Distributed Cognition in Human-AI Teams [6.386909552513031]
We show that AI actively reshapes the social and cognitive fabric of collaboration.<n>We show that AI participation reorganizes the distributed cognitive architecture of teams.<n>We argue for rethinking AI in teams as a socially influential actor.
arXiv Detail & Related papers (2024-07-03T13:46:00Z) - Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
AI systems need to be explainable to the humans in the loop.
We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication.
While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
arXiv Detail & Related papers (2024-05-19T22:22:21Z) - Human-AI Coevolution [48.74579595505374]
Coevolution AI is a process in which humans and AI algorithms continuously influence each other.
This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
arXiv Detail & Related papers (2023-06-23T18:10:54Z) - Capturing Humans' Mental Models of AI: An Item Response Theory Approach [12.129622383429597]
We show that people expect AI agents' performance to be significantly better on average than the performance of other humans.
Our results indicate that people expect AI agents' performance to be significantly better on average than the performance of other humans.
arXiv Detail & Related papers (2023-05-15T23:17:26Z) - BO-Muse: A human expert and AI teaming framework for accelerated
experimental design [58.61002520273518]
Our algorithm lets the human expert take the lead in the experimental process.
We show that our algorithm converges sub-linearly, at a rate faster than the AI or human alone.
arXiv Detail & Related papers (2023-03-03T02:56:05Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Joint Mind Modeling for Explanation Generation in Complex Human-Robot
Collaborative Tasks [83.37025218216888]
We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations.
The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications.
Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
arXiv Detail & Related papers (2020-07-24T23:35:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.