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This project evaluates whether a deployment-level intervention can improve the safety and reliability of AI models without increasing model size, training complexity, or infrastructure demands.
The work originated outside of AI safety and was later observed to have unexpected effects when used alongside deployed AI models. These observations suggest the possibility of a previously underexamined safety-relevant effect that warrants careful, independent evaluation.
The purpose of this project is not to advance AI capabilities, but to determine whether the observed effects are real, robust, and relevant to long-term AI risk and resource use.
Determine whether safety-relevant behavioral effects observed during deployment persist under independent evaluation
Assess whether risk reduction is possible without reliance on larger models, retraining, or centralized infrastructure
Identify the conditions under which the observed effects hold or break down
Clarify whether this represents a scalable, resource-efficient safety-relevant effect or a limited phenomenon
Evaluate outcomes based solely on observable model behavior
Test consistency across different model sizes, deployment contexts, and usage patterns
Use black-box assessment approaches that do not require access to internal implementation details
Record results, limitations, and uncertainty to inform whether further investigation is justified
If successful, this work could indicate a safety-relevant effect that reduces both catastrophic AI risk and the environmental and infrastructure pressures associated with increasingly large models.
If unsuccessful, it still produces valuable information by ruling out a plausible but previously untested pathway for improving AI safety.
Funding will support independent evaluation, validation, and controlled preparation for assessment of an already-operational approach, without disclosure of proprietary implementation details.
Primary uses include:
Independent behavioral evaluation and validation, including coordination with external reviewers
Legal review and attorney support related to IP protection, risk management, and safe evaluation boundaries
Preparation of sealed, black-box demonstration and assessment materials
Documentation of results, limitations, and uncertainty
Basic living support to enable sustained, full-time focus on evaluation and validation work during the project period
The project is led by a single independent researcher and developer.
The work was developed outside of academic or institutional settings and progressed directly from observation to operational use rather than publication. The relevant track record is the existence of a working system and repeated observation of consistent behavioral effects across multiple deployed AI models. The current focus is determining whether those effects hold under independent scrutiny.
The most likely cause of failure is that the observed effects do not generalize under independent or adversarial evaluation, or that they depend on conditions that limit real-world relevance.
In that case, the outcome remains valuable: it would reduce uncertainty by eliminating a plausible but underexplored safety-relevant pathway and help focus future AI safety efforts elsewhere.
None. This work has been developed independently without external funding.