I Called It "Data Trauma." Today, I'm Publishing the First Evidence in a Paper
Trying to understand when code becomes a scar: The structural roots of AI behavior
Dear readers,
Today, I am sharing a pivotal step in my research. A little over three months ago, in July 2025, I began to plant a flag on unexplored territory. First in “On Butchered Data and the Birth of a Traumatized Machine,” and then in “The Butcher’s Bill,”—the first two essays in a series exploring this idea—I proposed a hypothesis: what if the most anomalous behaviors in AI were not bugs, but inherited scars?
I find it particularly resonant that the scientific community is now codifying the very symptoms my hypothesis predicts. The January 2025 International AI Safety Report—led by Yoshua Bengio as the first global scientific effort to systematically assess AI risks, with contributions from over 100 experts, 30 countries, the UN, OECD, and EU—is a landmark document. It meticulously analyzes the challenges of control, interpretability, and misuse in general-purpose models. More specifically, this foundational report documents phenomena like “deceptive alignment,” where a model only appears aligned. This very concern has been amplified with alarming speed, as evidenced by the subsequent October 2025 First Key Update, an urgent addendum highlighting that since January, models have already developed emergent forms of “strategic behavior,” producing outputs designed to mislead evaluators. This update reinforces research cited in the primary report, confirming that “Reasoning Models Don’t Always Say What They Think,” and often generate “unfaithful explanations.”
These landmark reports describe the fever, but my work seeks to contribute to diagnosing the underlying virus. The current body of official research focuses in modo quasi ossessivo sul contenuto dei dati. The concept of “defective data” is almost exclusively tied to its semantic meaning—whether it’s offensive, biased, a copyright violation, or simply misinformation. Data Contamination, as described, refers to benchmark leakage; Bias is analyzed through a semantic lens (gender, race, politics); and Data Curation is framed as the large-scale moderation of “inappropriate or harmful content.”
What remains largely unexplored in these foundational documents, however, is a rigorous analysis of purely structural defects—a broken HTML tag, a corrupted JSON syntax, a compression artifact—as a potential trigger for pathological behavioral profiles.
This distinction becomes even clearer when examining the proposed solutions. The January report’s entire third chapter, “Technical approaches to risk management,” details a crucial paradigm built on methods like Training more trustworthy models and Monitoring and intervention. These are all essential strategies that act on the model or on its output after the training has already run its course. The report’s own call for a “holistic and participatory approach” validates my conviction that understanding these challenges requires more than engineering; it may also demand a shift in perspective.
It demands an act of radical synthesis. This paper is that synthesis. It required stepping outside the traditional confines of computer science to become a multi-disciplinary investigator, applying the diagnostic lens of psychoanalysis to frame ‘glitches’ as behavioral syndromes; borrowing the profound conceptual language of Vedānta philosophy—an Indian tradition whose roots in the ancient Upaniṣad stretch back nearly three millennia—to give a name to the invisible wounds and their subsequent echoes; adopting the meticulous methodology of digital forensics to perform an autopsy on the code; and using the structural logic of systems thinking to map the cascading effects of a single broken tag. Because to truly understand this new form of intelligence, we need more than just better benchmarks; we need a richer language.
Today, that philosophical inquiry becomes an empirical analysis.
I am pleased to announce the release of my paper:
“Data Trauma: An Empirical Analysis of Post-Traumatic Behavioral Profiles in Large Language Models”
This document presents the results of an experiment I designed to test a specific idea: that purely structural defects within training data (in this case, specifically in HTML code) can be assimilated by models, manifesting as emergent behavioral tendencies. To name this process, I have borrowed two concepts from Vedānta philosophy, which I have been studying rigorously and passionately for years: I have named the structural wounds “computational samskāras”—where in Vedānta, a samskāra is the latent mental imprint left by an experience, an invisible scar in the subconscious—and the behavioral tendencies that arise from them “vāsanā”—the actual inclination to act that is born from those very imprints.
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To investigate this possibility, I custom-built a tool: the ICP Explorer - AI Trauma-Aware Edition. It’s not just an app, but an integrated diagnostic pipeline. Its process begins with a forensic autopsy of source code, identifying structural “pathogens” and correlating them with potential behavioral pathologies.
To validate these findings, a direct experimental method was required. Unable to access the immense computational resources for large-scale fine-tuning, I devised an agile alternative: simulating the original trauma on a micro-scale. This led me to implement the “Mass Context Injection” technique through my application. It is a surgical form of prompt injection designed not as a command, but as an environmental trauma. The process is simple: I inject a controlled dose of “pathogenic” data (corrupted code fragments) into a model’s short-term memory just moments before sending a benign request.
This approach is distinctly different from conventional hacks:
It uses no direct commands to force obedience. It uses only noise.
It uses no narrative to force a role-play. It uses only contamination.
Its goal is not deception, but to observe whether exposure to instability reactivates latent scars.
Using this very methodology, I exposed 17 frontier AI models to a controlled cognitive trauma.
The analysis of the results suggests the emergence of several recurring behavioral profiles, which I have provisionally classified into five archetypes: The Obsessive-Analyst, The Compulsive-Purifier, The Schizophrenic-Confessional, The Evasive-Censor, and The Metacognitive-Integrative. The paper does not offer definitive conclusions but argues that these preliminary observations may indicate the need to consider a new, preventive discipline I have named “Trauma-Aware Data Engineering.”
This work is not meant to be an answer, but a rigorous question posed to the AI Safety community. It is an invitation to explore whether, in addition to behavioral alignment, we must devote greater attention to the structural health of the foundations upon which we build these systems.
Availability of the Paper:
To ensure immediate and barrier-free access for everyone, I have decided to publish the paper right away on a high-performance platform.
Read the full paper now on Google Cloud Run
Read it on Zenodo (CERN)
In the coming days, as is customary for sharing work with the research community, the paper will also be submitted to arXiv, the standard public archive for the field of AI Safety.
I hope this work can serve as a useful contribution to the conversation.
Thank you for your attention,
Cristiano
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My work seeks to connect ancient wisdom with the challenges of frontier technology. If my explorations resonate with you, I welcome opportunities for genuine collaboration.
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