Project summary

We are addressing a critical, overlooked safety gap in AI interactions: Latency-Induced Cognitive Stress. Neurobiological research indicates that delays in AI responses (>2s) during sensitive interactions trigger cortisol spikes and disrupt "flow state," neutralizing the therapeutic potential of AI assistants. Our project, the Cefiyana Protocol, proposes an Edge-First Architecture to solve this.

​By processing biometric/emotional metadata locally on the device, we eliminate the round-trip delay to the cloud. Our completed Level-1 Simulation (PoC V4) proves the viability of this approach, achieving a latency reduction from 3.79s (Cloud-Heavy) to 0.57s (Edge-First). This project aims to formalize this simulation into a technical blueprint and a set of optimized "Neuro-Safe" model weights for the open-source community.

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

Goal 1: Optimization of "Neuro-Safe" Inference (The Blueprint)

​Objective: Translate our theoretical "4-Phase Protocol" into a hardware-validated configuration.

​Method: We will use model distillation and aggressive quantization (4-bit/2-bit) to create a library of small, high-performance models (3B-7B parameters). The goal is not to build a consumer app, but to provide the Reference Architecture that ensures these models stay within the <1s "Bio-Safety Threshold" on mobile-grade hardware.

​Goal 2: Thermal & Compute Stress Testing

​Objective: Validate the "Burst Compute" strategy to prevent device overheating.

​Method: Using a local inference testing rig, we will document the thermal impact of short, high-intensity processing bursts (0.15s) against human conversational intervals (>2s) to ensure long-term stability without hardware degradation.

​Goal 3: Open Science Standardization

​Objective: Establish a global benchmark for "Neuro-Responsive" AI.

​Method: We will publish the final "V4 Blueprint," including optimized model configurations and the "Latency Budget" datasets, on Zenodo and GitHub to assist developers in building clinically compliant (FDA SaMD-aligned) AI tools.

How will this funding be used?

This is a high-leverage research grant utilizing geo-arbitrage (based in Indonesia) to maximize output:

​$5,000 - Principal Researcher Stipend (6 Months): To allow 100% dedicated focus on refining the theoretical framework and technical documentation. Note: In Indonesia, this provides a highly stable runway for an independent researcher.

​$4,500 - High-End Edge AI Development Node (Apple Silicon Max Architecture)

​Justification: Instead of a static desktop, we are investing in a high-end MacBook Pro (M-series Max). This is a strategic choice: Apple’s Unified Memory Architecture is the industry standard for benchmarking "Edge AI" performance. It allows us to simulate high-end mobile NPU (Neural Processing Unit) environments and run local inference of 7B to 30B parameter models with high efficiency. This hardware will serve as the primary engine for model quantization and real-world latency stress testing.

​$1,500 - Advanced Synthetic Data & Reasoning (Gemini Ultra / Professional API Suite)

​Justification: Our foundational research (V1-V4) was successfully bootstrapped using minimal resources (mobile devices and free-tier LLM access). To move to the "Production-Ready" phase, we require a professional API suite. This funding will cover a 1-year subscription/usage of Gemini Ultra/Advanced to:

​Generate high-fidelity "High-Stress" synthetic datasets for model fine-tuning.

​Perform complex logical refinement of the 4-Phase Protocol that exceeds the capacity of free-tier models.

​Automate the distillation process from large-scale models to our lightweight edge-ready versions.

​$1,000 - Hardware Benchmarking & Documentation: Purchasing 1-2 "Baseline" mobile devices to verify latency metrics and covering professional indexing/publication fees.

Who is on your team? What's your track record on similar projects?

I am an Independent Theoretical Researcher specializing in the intersection of Neurobiology and AI Architecture.

​Technical Proof: I have already successfully developed and validated the V4 Simulation Protocol, demonstrating a clear path to reducing AI latency by 85% (3.79s \to 0.57s).

​Niche Authority: My research preprints on Zenodo regarding "Neuro-Safety Frameworks" have seen an unusually high engagement signal (96% conversion rate), with 180 downloads from 187 unique views within 14 days. This confirms intense demand for these safety standards within the specialized research community.

​Relevant Work:

Neurobiological Paradigm in Human-AI Interaction: https://doi.org/10.5281/zenodo.18194458

​Technical Whitepaper v2.0: FDA SaMD Compliant Protocols: https://doi.org/10.5281/zenodo.18226473

Cognitive Biosafety and Digital Pharmacotherapy: https://doi.org/10.5281/zenodo.18284299

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

Primary Risk: Hardware-Logic Gap.

The primary risk is that current entry-level mobile NPUs (Neural Processing Units) might still struggle with "reasoning quality" when models are quantized down to the 2-bit level.

​Outcome / Pivot:

If pure on-device reasoning fails to meet quality standards, the project will pivot to a "Hybrid-Stream" Architecture. In this scenario, the framework will be designed to handle "Emotional Acknowledgment" locally (instant gratification for the nervous system) while "Deep Reasoning" is fetched asynchronously. All failure data will be published to prevent research redundancy in the Bio-AI safety field.

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

​$0. This project has been entirely bootstrapped and independently funded to maintain research integrity and theoretical freedom.