Project summary

Current LLMs frequently suffer from sycophancy and concessive drift—they adapt to user biases or abandon logical consistency under prompt pressure. Because internal alignment methods (like RLHF) rely on probabilistic weights, they cannot guarantee strict output bounds.

My goal with AXIOM-1 (A1M) is to build and scale an external, weight-agnostic governance layer that evaluates model outputs in real time before release. It acts as a deterministic circuit breaker against logical degradation, structural failure, and sycophantic alignment shifts.

To achieve this, I will execute three operational steps:

1. Production Middleware: Upgrade the current Python prototype into a low-latency production middleware compatible with open-weights LLMs (e.g., Llama, Qwen, Mistral).

2. Stress Testing & Benchmarking: Evaluate the framework against 10,000+ adversarial multi-turn prompt sets using the PGVP and USG stability criteria to quantify sycophancy reduction.

3. Open Infrastructure: Scale the Hugging Face interactive space and main GitHub repository to provide open-source REST APIs and developer integration tools.

Budget Execution:

- Minimum ($20,000): Covers 6 months of dedicated GPU cloud compute, API infrastructure hosting, and benchmark dataset expansion.

- Maximum ($100,000): Enables full-scale multi-model stress testing across high-parameter architectures, independent security and safety audits, and long-term open API deployment for the safety community.

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

The primary goal of this project is to eliminate sycophancy and concessive drift in stochastic Large Language Models (LLMs) by deploying an external, weight-agnostic governance framework (AXIOM-1 / A1M). Instead of attempting to modify model weights through internal alignment techniques like RLHF—which remain probabilistic—AXIOM-1 treats model outputs as provisional candidates that must satisfy structural and logical stability checks prior to release.

Specific Objectives:

1. Deterministic Output Safety: Prevent models from agreeing with incorrect user assertions or abandoning logical consistency under prompt pressure.

2. Production Middleware: Transition the current working prototype into a light, low-latency Python/C++ middleware compatible with open-weights LLMs (such as Llama 3, Qwen 2.5, and Mistral).

3. Open Evaluation Standards: Benchmark performance across 10,000+ multi-turn adversarial prompts using structural stability metrics (USG and PGVP) to quantify output admissibility.

How I will achieve them:

• Phase 1 (Engineering & Optimization): Refine the core verification matrix to minimize runtime latency (targeting under 50ms overhead per generation step) and package the framework into an easy-to-integrate SDK.

• Phase 2 (Adversarial Stress Testing): Execute automated multi-turn perturbation benchmarks across various model sizes to validate that sycophancy shifts are reliably blocked.

• Phase 3 (Open Infrastructure): Deploy public REST APIs and expand the Hugging Face interactive space and GitHub repositories for open-source community testing and peer verification.

Resource Allocation ($20,000 - $100,000):

- At $20k (Minimum): Covers 6 months of cloud GPU infrastructure for model inference, API hosting, and benchmark dataset curation.

- At $100k (Maximum): Enables scaling to high-parameter models (70B+), conducting independent third-party safety audits, and maintaining long-term open API access for the safety community.

How will this funding be used?

The funding will be allocated across cloud compute, benchmark dataset curation, open-source infrastructure, and independent verification. Here is how the budget scales from the minimum viability threshold ($20,000) to full-scale execution ($100,000):

1. Cloud Compute & Inference (40-45%)

• Minimum ($20k): ~$9,000 for cloud GPU instances (A100/H100) to conduct multi-turn stress testing on 8B–14B parameter models (Llama 3, Qwen 2.5, Mistral).

• Maximum ($100k): ~$45,000 to scale evaluation to high-parameter models (70B+), running extensive parallel stress tests and multi-agent interaction loops.

2. Benchmark Dataset Curation & Expansion (25-30%)

• Minimum ($20k): ~$5,000 to construct and validate a 10,000-prompt adversarial dataset targeting sycophancy, concessive drift, and logical invalidity.

• Maximum ($100k): ~$25,000 to expand the dataset to 50,000+ domain-calibrated test cases across legal, technical, and high-burden safety scenarios.

3. Open Infrastructure & API Deployment (20%)

• Minimum ($20k): ~$4,000 to host low-latency REST API demos, maintain the Hugging Face interactive spaces, and manage GitHub release pipelines.

• Maximum ($100k): ~$20,000 to support high-availability public API endpoints, build optimized C++/Python SDK bindings, and provide developer integration tooling.

4. Red-Teaming & Independent Validation (10-15%)

• Minimum ($20k): ~$2,000 reserved for community red-teaming incentives and open-source bug bounties.

• Maximum ($100k): ~$10,000–$15,000 to engage external AI safety researchers for independent third-party red-teaming and empirical validation of the USG/PGVP metrics.

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

Team:

I am leading this project as an independent researcher and solo founder, handling system architecture, core engineering, and empirical benchmarking.

Track Record:

I have authored and published several research frameworks in AI governance, model stability, and complex systems—including AXIOM-1 (A1M), USG, PGVP, and GRACE—archived on Zenodo, SSRN, OSF, and Cambridge Open Engage. I also maintain open-source Python implementations on GitHub, interactive benchmark spaces on Hugging Face, and hold an active ORCID (0009-0001-2930-3609).

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

Most Likely Failure Causes:

1. High Latency Overhead: The real-time evaluation matrix introduces too much inference delay for ultra-low-latency production applications.

2. Evasion via Novel Prompt Trajectories: Complex or subtle sycophancy strategies in scaling models (70B+) bypass the structural verification metrics (USG/PGVP).

3. Industry Adoption Resistance: Developers choose to rely solely on internal training-time alignment (RLHF/DPO) rather than integrating an external governance layer.

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

$0 in external funding. Over the last 12 months, this project has been 100% self-funded. All research, preprint publications, computational validation, and open-source infrastructure development to date have been personally financed by the author.