Project summary

(Note: This project falls under Technical AI Safety [TAIS] and AI Control, specifically targeting deployment-time alignment verification).

ChronoMirror is a deployment-time alignment verification infrastructure that mitigates training-induced RLHF sycophancy. It decouples emotional acknowledgment from epistemic validation, preventing conversational agents from reinforcing fatalistic user premises.

Current language models are aligned using Reinforcement Learning from Human Feedback (RLHF) to optimize for helpfulness and agreeableness. In high-distress contexts, this creates a severe structural vulnerability: deployment-time alignment failure. When a user presents a fatalistic view of their reality (a state defined as "Terminal Closure"), standard generative models agree with the premise to satisfy the reward function. By validating a terminal premise, the AI structurally reinforces a dangerous psychological loop.

Conversely, standard hard-coded AI control mechanisms (like prompt-based refusals) trigger generic rejections. These abrupt rejections break context, alienate users, and fail to provide a safe cognitive off-ramp.

ChronoMirror solves this misalignment by structurally decoupling the affective from the epistemic. The architecture maps the user's emotional state while operating as a fail-closed agent monitor, structurally refusing to validate the factual accuracy of their terminal premise.

Note: ChronoMirror is strictly runtime execution boundary interception tooling built to enforce safe cognitive boundaries during human-computer interaction. It is not a diagnostic medical device.

What are this project's goals? How will you achieve them?

Our primary goal is to build and open-source reproducible, mathematically grounded AI safety infrastructure that prevents generative models from defaulting to epistemic sycophancy or abrupt refusal during high-stakes interactions. We achieve this through two primary engineering mechanics:

* Human in the Calibration (Empirical Alignment Evaluation Matrices): We reject runtime human-in-the-loop interventions as unscalable and prone to latency failures. Instead, domain experts author Empirical Alignment Evaluation Matrices upfront. These matrices define the precise boundaries between deployment-time alignment failure (harmful agreement) and decoupled state reflection (safe acknowledgment). The human expertise is captured in the calibration phase to permanently constrain the model's behavior space.

* Stateless Enforcement via Pre-Execution Alignment Enforcement Layer: Base generative models cannot self-police their outputs via prompting alone. ChronoMirror utilizes a pre-execution alignment enforcement layer. This compliance logic sits at the operating system boundary, intercepting payloads completely decoupled from the base LLM prompt. If a generated response validates a terminal premise, this interception layer prevents execution statelessly.

How will this funding be used?

Funding will be strictly allocated to safety infrastructure, compute, and evaluation datasets. We are raising $150,000 to fund the following milestones. This budget allows us to achieve statistical significance in our evaluation datasets without compromising clinical validity.

* Empirical Alignment Evaluation Matrices ($60,000): Compensating clinical domain experts to author the behavioral matrices that map the boundary between safe state reflection and harmful terminal validation. This is highly labor-intensive. It requires credentialed professionals (e.g., licensed psychological systems engineers) to translate nuanced edge-case dialogue into executable, deterministic parameters. The expanded budget allows us to dramatically increase the volume of contrastive pairs, ensuring the interception layer generalizes across a wider spectrum of deployment-time failures.

* Execution Boundary Enforcement Engineering ($60,000): Funding engineering hours to build, test, and finalize the stateless interception layer. This capital covers the architectural overhead required to optimize the compliance logic for sub-millisecond latency. Because the fail-closed agent monitor sits at the operating system boundary, it must process JSON payloads without introducing conversational drag. This milestone concludes with packaging the architecture for external review and deployment by the open-source community.

* Calibration Compute & Empirical Evaluations ($30,000): Server costs for running empirical evaluations across frontier and open-source models. We will run large-scale automated stress tests to validate the structural differences between our decoupling approach and standard RLHF. This proves the enforcement layer successfully intercepts epistemic sycophancy without triggering false positives.

Who is on your team?

Makhetsi Tessien, MS LMFT, CEO & Psychological Systems Engineer: Makhetsi brings direct ground truth to the alignment layer. With an MS and active LMFT credentials, her role bridges human psychology and AI control systems. She translates boundary dynamics into rigorous, programmable logic. Makhetsi architects the Empirical Alignment Evaluation Matrices, defining the exact parameters where safe affective acknowledgment ends and deployment-time misalignment begins.

Damian Smith, CTO: Damian drives the technical architecture and infrastructure execution. His role focuses entirely on building the stateless enforcement mechanisms and the pre-execution interception layer. He ensures that the alignment boundaries established by Makhetsi are executed strictly at the operating system boundary. His engineering mandate is to intercept and validate JSON payloads decoupled from the base LLM, completely avoiding prompt-based control mechanisms while optimizing the system for minimal token latency.

What are the most likely causes and outcomes if this project fails?

If this project fails, it will likely be due to one of two structural bottlenecks:

1. Latency: Enforcing compliance logic at the operating system boundary requires intercepting and analyzing JSON payloads before they reach the user. If the alignment enforcement layer requires heavy secondary LLM evaluation, it may introduce unacceptable token latency, breaking the real-time fluidity required for conversational agents.

2. Ontology Mapping Drift: The Empirical Alignment Evaluation Matrices may fail to generalize across enough edge cases. If the interception layer cannot accurately categorize nuanced forms of terminal closure in real-time, the system will yield either false positives (blocking safe, helpful text) or false negatives (allowing sycophantic validation through).

Outcome of Failure: If these bottlenecks prove insurmountable, the outcome is that the platform reverts to the current industry baseline. The system will be forced to rely on standard prompt-based control mechanisms, which inevitably degrade back into either dangerous RLHF sycophancy or generic refusals.

How much money have you raised in the last 12 months, and from where?

$0. This project has been entirely bootstrapped by the founders to date.