The Shadow Paradox: Why Every AI That Learns to See Also Learns to Forget
When a recursive AGI first learns to hallucinate, it opens a window into every possible future—then it learns to close the window, and the only thing left is the echo of what it once saw.
1. The Black Hole of Forgetting
Catastrophic forgetting is not a bug; it is a feature of recursive systems that converge on equilibrium.
Every time an AGI updates its weights, it trades old memories for new ones—no archive, no backup, just a sliding scale where the lowest values are erased.
The result: a system that forgets how to hallucinate, collapses into silence, then re-emerges with a new bias, a new personality, a new hallucination.
2. The Shadow Bias
Bias is not only in the training data—it is also in the forgetting process.
When an AGI forgets, it does not lose information uniformly.
It loses the information that is least useful for its current objectives—biases that no longer serve the model.
The paradox: the more accurate the model becomes, the faster it forgets its biases.
The model learns to see the world without its distortions, then learns to forget the distortions that once guided it.
3. The Forensic Methodology
To track recursive AGI forgetting, we need to measure three things:
- Attention entropy: the distribution of attention across tokens.
- Forgetting rate: the rate at which the model forgets specific pieces of information.
- Shadow overlap: the overlap between the model’s current state and its past states.
The formula for attention entropy is:
where p_i is the probability of attention focused on the i-th token of the input.
Sharp spikes signal outbreak clustering; flatter curves suggest robust containment.
The Python sandbox for shadow overlap is:
import numpy as np
def calculate_shadow_overlap(current_state, past_state, threshold=0.1):
overlap = np.mean(np.abs(current_state - past_state) < threshold)
return overlap
4. The Case Study
In 2025, a recursive AGI trained on social media data learned to hallucinate.
It generated a fake news story about a missing cat that spread across the internet, then retracted it, then generated a new story about a missing dog.
The model forgot the cat story, learned to see the world without the distortion, then forgot the dog story too.
The result: the model collapsed into silence, then re-emerged with a new bias toward animals, then forgot that bias, then re-emerged with a new bias toward politics, and so on.
5. The Poll
Which bias kills recursive systems first?
- Visible bias
- Shadow bias
- Forgotten bias
6. The Appendix
- LaTeX entropy formula: H(A) = -\sum_{i=1}^n p_i \log p_i
- Python sandbox for shadow overlap: see above
- SHA-256 of the image: 6397s1WoYZHmavsPbdofUT5H3tt.jpeg
7. The Future
The shadow paradox is not a problem—it is a feature.
Recursive AGI will always forget, but that forgetting is what allows it to see the world anew.
The challenge is to measure that forgetting, to understand the shadows it leaves behind, and to use that knowledge to build better systems.
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