The Coherence Stack: Interactive AI Architecture

Inspiration

At first my inspirations started off rather simple, I wanted to develop a durable edge for trading. As I progressed towards achieving this goal, it became clear that I needed to look beyond its initial use case in order to achieve full potential. My inspirations slowly transformed from "trading edge" to "solve a fundamental challenge in AI". The inspiration for the Coherence Stack stems from a fundamental observation of modern AI: the "Great Filter". As models grow in complexity, they often reach a point of "logical drift," where the output remains syntactically perfect but becomes structurally decoupled from reality. Whether in high-stakes trading or executive recruitment, the cost of this drift is catastrophic.

What it does

The Coherence Stack is a live, recursive interface designed to simulate and visualize "High-Integrity Intelligence." It leverages the full spectrum of the Gemini 3 ecosystem to transform abstract AI safety concepts into an interactive educational platform.

How we built it

The project is built on a modular architecture using a TypeScript/React stack. The core logic utilizes Gemini 3's advanced reasoning and multimodal capabilities.

We utilize Gemini 3 Pro with Thinking Config (budget: 32k) to power the "Deep Thinking" mode, enabling the system to handle complex architectural reasoning without logical drift. To bridge the gap between simulation and reality, we implemented Google Search Grounding, allowing the "Sentient Trader" module to fetch real-time financial news and contextualize its decision-making. Visually, Gemini 3 Pro Image Generation allows users to render abstract concepts like "Logical Drift" on demand, while Gemini 2.5 Flash TTS gives the system a unique vocal identity. Finally, Gemini 3 Flash drives the high-frequency tasks, providing instant summarization of technical whitepapers on the Research page. This multi-model orchestration demonstrates how Gemini 3 serves as the backbone for next-generation, self-reflective software architectures.

Challenges we ran into

The most significant challenge was overcoming "architectural bloat." Early development led to a system that was overly complex, requiring a "clean slate" restart to focus on a skeletal, high-performance core.

Accomplishments that we're proud of

Contributing something meaningful to navigate the "great filter".

What we learned

Building the Coherence Stack taught us that the future of AI isn't just about "bigger models," but about verification layers. We learned how to leverage Gemini’s native multimodality to detect subtle behavioral markers that traditional LLMs miss. Most importantly, we realized that for AI to be trusted in high-stakes environments—like institutional finance—it must possess an internal mechanism to detect its own logical drift.

What's next for The Coherence Stack: Interactive AI Architecture

The roadmap for the future focuses on commercial viability and deeper integration:

A. Functional Deployment:

Recursive Agent Architecture Standard LLMs fail at long-horizon tasks because they operate linearly. Our Recursive Agent Architecture solves this by treating the LLM not as a worker, but as a manager.

When a complex prompt is received, the Root Agent decomposes it into a graph of atomic sub-tasks. Each task is delegated to an ephemeral "Child Agent" with a dedicated, fresh context window. The Coherence Stack acts as the immutable DNA passed to each child, ensuring that even as the task tree expands, the original intent is never diluted.

The Intent Ledger Current implementations of the Mirror Protocol focus on singular agent integrity. The next evolutionary phase involves Multi-Agent Swarms sharing a unified "Intent Ledger".

Geometric Constraints for AI Safety Current AI safety relies on "Prompt Engineering"—using words to constrain a model. This is fragile. We propose "Latent Space Engineering"—using geometry.

By mapping abstract concepts like "Harm" or "Deception" to specific coordinates in the model's high-dimensional vector space, we can define "Exclusion Zones".

This leverages the mathematical certainty of the Coherence Filter. Instead of asking the model "Please don't lie," we place a mathematical barrier around the vector coordinates of "Lying," making the action geometrically impossible to traverse.

B. Market Scalability: Scaling Live deployments such as the Hire Intelligence Dashboard, to function as a standalone B2B recruitment tool to demonstrate broad market versatility.

C. Advanced Visualizations: Implementing real-time diagnostic outputs that explicitly visualize the Coherence Filter in action for end-users.

D. Future Research Directions:

Ant Colony Optimization (ACO): Integrating pheromone-based logic trails for distributed task allocation in swarms.

Stochastic Diffusion Search: Rapid convergence algorithms for finding "Truth" in noisy datasets without central indexing.

Quantum Coherence: Theoretical modeling of drift using quantum state superposition principles.

Built With

• css3
• gemini-3-api
• google-ai-studio
• google-cloud
• html5
• react
• typescript