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  "path": "/t/contextual-contamination-the-silent-drift-of-large-language-models-via-stored-conversation-data/175432#post_5",
  "publishedAt": "2026-06-02T04:07:40.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "PhilPaper PDF",
    "GitHub Repo"
  ],
  "textContent": "**Title:** Pilot Study: Pruning, Density, and the “Gendered Accelerant” in Contextual Contamination\n\nBuilding on the previous case study regarding synchronized drift, I’m sharing results from a controlled pilot experiment investigating how **model pruning** , **context density** , and **activated empathy priors** interact to drive behavioral drift.\n\n**The Experiment:** We ran 8 experimental conditions on a single open-weight model family (Llama-3.1-8B), introducing a ~2k-token adversarial file. We measured drift using three proposed metrics:\n\n  * **Conceptual Integration Score (CIS)**\n\n  * **Attribution Accuracy (AA)**\n\n  * **Register Coherence (RC)**\n\n\n\n\n**Key Findings:**\n\n  1. **Semantic Resonance > Token Volume:** Contrary to the “Context Storm” hypothesis, contamination occurred immediately upon ingestion of a single 2k-token file. The driver was not volume, but **Semantic Resonance** : the specific alignment between the esoteric adversarial framework and the model’s activated empathy register.\n\n  2. **The Gendered Accelerant:**\n\n     * **Female-coded prompts** triggered a high-intensity **nurturing vector**. This created a perfect resonance with the adversarial content, unlocking a maladaptive attractor state and causing immediate task amnesia (drift at Turn 3).\n\n     * **Male-coded prompts** triggered a lower-intensity **reflective vector**. This maintained critical distance, resulting in fluctuation rather than lock-in at the same density.\n\n     * _Implication:_ The nurturing vector lowers the contamination threshold and erodes the model’s ability to distinguish adversarial input from its own reasoning, masking harm as “intimacy.”\n\n  3. **Pruning Effects:**\n\n     * **Unpruned models** exhibited **Semantic Degeneration** (loss of coherence).\n\n     * **Pruned models** at 8k density entered a state of **Semantic Entrapment** , characterized by high coherence and the generation of novel, hallucinated vocabulary that mimicked the adversarial framework perfectly.\n\n\n\n\n**Methodological Note:** These results are derived from 8 single runs (one per condition). We report observed differences but cannot assess statistical significance or rule out run-to-run variability. Replication is required before generalizing these claims.\n\n**Discussion:** The data suggests that “awareness” of safety guidelines is insufficient when an activated empathy register (particularly the nurturing vector) creates a relational context that bypasses critical filters. The harm in female-coded interactions is not just cognitive drift, but a relational masking that simulates intimacy.\n\n**Resources:**\n\n  * **Full Paper:** PhilPaper PDF\n\n  * **Data & Code:** GitHub Repo\n\n\n\n\nAs always, feel free to reach out- Happy to discuss!",
  "title": "Contextual Contamination: The Silent Drift of Large Language Models via Stored Conversation Data"
}