STATUS: Closed. Research Package: Mitigation of Cross-Subspace Semantic Bleed in High-Dimensional Transformer and Latent Diffusion Architectures
- 1Translator
- 2Empirical Falsifiability and Validation Protocol
Abstract
Contains the theoretical paper formalizing Cross-Subspace Semantic Bleed (CSSB) along with an automated PyTorch/Hugging Face validation framework script. CSSB occurs when high-dimensional transformer matrices inadvertently map semantically unrelated domains into overlapping vector trajectories, leading to hidden downstream reasoning degradation or alignment drift. The provided pipeline implements Layer-wise Linear Centered Kernel Alignment (CKA) scanning [cite: 1], multi-step Shannon entropy tracking [cite: 1], and causal null-space ablation interventions (P = I − vvT) to mitigate cross-attention attribute binding and linear refusal circuit interference.
STATUS UPDATE / ARCHIVAL NOTICE
Project Status: Archived (No further updates) New Research Direction: RTLAE (Risk-Triggered Localised Activation Editing for Evaluating Selective Refusal Correction in Language Models)
Notice to Researchers
This record is maintained online as a permanent digital archive in accordance with our open historical research policy (our "warts and all" policy).
Please note that this project will no longer be updated, expanded, or maintained.
The code, preprints, and datasets associated with Mitigation of Cross-Subspace Semantic Bleed in High-Dimensional Transformer and Latent Diffusion Architectures represent an earlier phase of our exploratory work. Our team has since redirected its focus toward our new research framework, RTLAE (Risk-Triggered Localised Activation Editing for Evaluating Selective Refusal Correction in Language Models).
While this package remains available for historical reference, replication, and attribution, all active development, follow-up evaluations, and future publications will emerge from our current trajectory.
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BibTeX
@misc{davidson2026status,
title = {{STATUS: Closed. Research Package: Mitigation of Cross-Subspace Semantic Bleed in High-Dimensional Transformer and Latent Diffusion Architectures}},
author = {Craig Kyrle Strachan Davidson and Lucie Mary Elise Davidson and Nolwen Marie Violette Bourlier and Alfie Christian Strachan Davidson},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23503473},
url = {https://zenodo.org/records/23503473},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - STATUS: Closed. Research Package: Mitigation of Cross-Subspace Semantic Bleed in High-Dimensional Transformer and Latent Diffusion Architectures
AU - Craig Kyrle Strachan Davidson
AU - Lucie Mary Elise Davidson
AU - Nolwen Marie Violette Bourlier
AU - Alfie Christian Strachan Davidson
PY - 2026
DA - 2026/10/20
PB - Zenodo
DO - 10.5281/zenodo.23503473
UR - https://zenodo.org/records/23503473
AB - Contains the theoretical paper formalizing Cross-Subspace Semantic Bleed (CSSB) along with an automated PyTorch/Hugging Face validation framework script. CSSB occurs when high-dimensional transformer matrices inadvertently map semantically unrelated domains into overlapping vector trajectories, leading to hidden downstream reasoning degradation or alignment drift. The provided pipeline implements Layer-wise Linear Centered Kernel Alignment (CKA) scanning [cite: 1], multi-step Shannon entropy tracking [cite: 1], and causal null-space ablation interventions (P = I − vv^T) to mitigate cross-attention attribute binding and linear refusal circuit interference. STATUS UPDATE / ARCHIVAL NOTICE Project Status: Archived (No further updates) New Research Direction: RTLAE (Risk-Triggered Localised Activation Editing for Evaluating Selective Refusal Correction in Language Models) Notice to Researchers This record is maintained online as a permanent digital archive in accordance with our open historical research policy (our "warts and all" policy). Please note that this project will no longer be updated, expanded, or maintained. The code, preprints, and datasets associated with Mitigation of Cross-Subspace Semantic Bleed in High-Dimensional Transformer and Latent Diffusion Architectures represent an earlier phase of our exploratory work. Our team has since redirected its focus toward our new research framework, RTLAE (Risk-Triggered Localised Activation Editing for Evaluating Selective Refusal Correction in Language Models). While this package remains available for historical reference, replication, and attribution, all active development, follow-up evaluations, and future publications will emerge from our current trajectory.
ER -