Blanchotian Neural Network Component

“To write is to enter the space of impossibility where nothing becomes possible.”

— Maurice Blanchot, The Space of Literature

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✨ What’s inside?

Component	Essence	File
Blanchotian Attention	Each token attends to tokens‑present and to the very lack of attention it once gave. Includes learnable void Q/K vectors and a decaying trace of past attention.	Blanchotian Attention Mechanism.py
Orphic Embeddings	Tokens are shaped by what surrounds them and what is forever hidden. Rarity gates, isolation vectors, contextual inversion.	OrphicEmbeddings.py
Recursive Orphic Tokenizer	Encodes tokens alongside what is conspicuously absent in their contexts. Uses recursive n‑grams to track absence patterns.	Recursive Orphic Tokenizer.py
Neutral Loss	Re‑centres error as a creative differential; stable log‑sum‑exp, label smoothing, disaster threshold.	losses.py
Blanchotian Layer Norm	Normalises the centre while letting solitary features remain singular.	Blanchotian Layer Normalization.py
Blanchotian Transformer	Layers commune with all preceding layers—an endless palimpsest.	The Infinite Conversation.py
Unavowable Community	Ensemble whose disagreement is the signal; models converse through divergence.	Unavowable Community.py
Blanchotian Style Transfer	Apply Blanchotian components to reimagine text in a chosen style.	Blanchotian_Style_Transfer.py
Différance Layer	Meaning arrives only in the wake of what came before; outputs are forever displaced by traces of prior states.	Differance Layer.py

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⚖️ License & Ethics MIT. You are free to fork, remix, and deploy—but consider the societal impact of machines that refuse closure. “Perhaps the task is not to end the dialogue, but to sustain the silence between its words.”

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