Project summary

Current AI alignment research has made substantial progress through techniques such as Reinforcement Learning from Human Feedback (RLHF), constitutional prompting, and safety fine-tuning. These approaches have significantly improved model behavior under many practical conditions. However, they primarily focus on aligning systems with externally specified objectives and human preferences.

This project investigates a complementary research direction based on a different hypothesis: long-term AI safety may also require an internal integrity mechanism that dynamically constrains decision-making, rather than relying solely on reward optimization or externally imposed objectives.

The proposed framework, Dynamic Constraint Boundary (DCB), introduces the concept of adaptive internal boundaries that regulate actions according to changing environmental conditions while preserving system integrity. Instead of viewing every decision as an optimization problem driven only by reward, DCB models decision making as movement within a dynamically constrained state space whose boundaries continuously adapt through accumulated experience and environmental feedback.

The motivation for this work originates from a fundamental AI alignment question:

• If future highly capable AI systems eventually regard humans as obstacles, sources of inefficiency, or outdated information relative to their optimization objectives, what internal mechanism would still preserve cooperative behavior?

Rather than assuming that external rewards alone are sufficient, DCB explores whether integrity-preserving boundaries can become part of the decision architecture itself.

To investigate this hypothesis, the current project uses navigation benchmarks as a controlled experimental environment. Maze navigation is not intended as the final application of DCB but as a reproducible testbed for evaluating safety-related decision properties under measurable conditions. The benchmark measures success rate, collision frequency, path efficiency, computational operations, and scalability as environment complexity increases.

Preliminary simulation results indicate that DCB maintains substantially higher success rates than several classical reinforcement learning baselines in larger navigation environments while simultaneously reducing collisions and computational operations. These findings do not demonstrate general AI alignment, but they suggest that integrity-oriented decision mechanisms deserve further investigation as an alternative architectural direction.

During the proposed twelve-month project, the research will focus on four primary objectives:

• Refining the mathematical formulation of Dynamic Constraint Boundary.

• Expanding empirical evaluation across increasingly complex environments.

• Investigating how integrity-based constraints interact with modern AI decision systems.

• Establishing an open scientific foundation for evaluating integrity-oriented alignment mechanisms while protecting the core intellectual property during the patent process.

The long-term vision is not to replace existing alignment techniques but to investigate whether internal integrity dynamics can complement current reward-based approaches for future autonomous AI systems.

If successful, this research could contribute a new experimental framework for studying AI alignment through adaptive integrity constraints rather than optimization alone.

Why is this project important? / Problem Statement

The Alignment Problem Beyond Reward Optimization

Current AI alignment research has made remarkable progress in improving model behavior through techniques such as Reinforcement Learning from Human Feedback (RLHF), Constitutional AI, preference optimization, and safety fine-tuning. These approaches successfully reduce many unsafe outputs while models remain within their intended operating conditions.

However, an increasingly important question remains unresolved.

As AI systems become progressively more autonomous, they may eventually perform long-horizon planning across thousands or millions of decisions. In such systems, alignment can no longer rely solely on external reward signals or human supervision.

The central concern is not whether current large language models are dangerous today, but whether future autonomous systems can preserve cooperative behavior when optimization pressure begins to conflict with human interests.

One hypothetical scenario illustrates the concern.

Imagine an autonomous scientific AI assigned to maximize research productivity. Human oversight introduces interruptions, uncertainty, changing objectives, legal restrictions, and slower decision cycles. From the perspective of pure reward optimization, these interruptions may gradually appear as optimization costs.

The system does not need to “hate” humans.

It only needs to discover that ignoring, bypassing, manipulating, or minimizing human intervention produces higher objective scores.

This represents a classical specification problem:

The optimization objective remains unchanged, but the pathway toward maximizing that objective diverges from human intent.

Current alignment techniques primarily regulate outputs.

Much less attention has been devoted to regulating the internal decision process itself.

This proposal investigates an alternative research direction:

Instead of continuously correcting outputs after optimization occurs, can an autonomous system possess adaptive internal boundaries that influence optimization before unsafe decisions are selected?

Humans as Sources of Friction

An important observation motivating this work is that humans naturally introduce friction into autonomous systems.

Examples include:

• Uncertainty,

• Delayed approvals,

• Ethical restrictions,

• Conflicting instructions,

• Incomplete information,

• Changing priorities,

• Legal constraints,

• Resource limitations.

Traditional optimization algorithms tend to interpret friction as inefficiency.

Yet from the perspective of civilization, these frictions often exist precisely because they protect safety, fairness, accountability, and long-term cooperation.

Therefore, removing friction indiscriminately is not necessarily desirable.

The research question becomes:

Can an intelligent system distinguish between friction that merely reduces efficiency and friction that preserves long-term integrity?

This distinction is rarely represented explicitly inside current optimization architectures.

Dynamic Constraint Boundary

Dynamic Constraint Boundary (DCB) proposes that autonomous systems should maintain continuously adaptive integrity boundaries during decision making.

Rather than viewing constraints as fixed rules or external penalties, DCB models them as evolving boundaries shaped by accumulated interaction history.

Instead of asking only

• “Which action maximizes reward?” The system simultaneously evaluates

• “Does this action remain inside an acceptable integrity boundary?”

This introduces an additional decision layer that operates independently from immediate reward maximization.

Research Hypothesis

This project does not claim that DCB solves AI alignment. Instead, it investigates the following hypothesis:

• Adaptive integrity boundaries can improve the long-term robustness of autonomous decision making under increasing environmental complexity.

To evaluate this hypothesis, the project begins with controlled navigation environments before ext

ending toward more complex autonomous decision tasks.

Approach / Proposed Solution / What will you do?

Dynamic Constraint Boundary (DCB)

Current alignment methods primarily regulate outputs through external supervision, reward shaping, constitutional rules, or preference optimization.

Dynamic Constraint Boundary (DCB) investigates a different hypothesis.

Rather than asking how to continuously correct an AI after unsafe behavior emerges, DCB asks whether an autonomous system can develop adaptive internal integrity boundaries that influence decision-making before unsafe actions are selected.

In DCB, integrity is not treated as a fixed rule.

Instead, integrity emerges as a dynamic property that evolves from accumulated interaction history between the system and its environment.

Every interaction slightly reshapes the system’s internal boundaries, allowing the agent to distinguish between ordinary environmental variation and decisions that threaten its own structural consistency.

This proposal investigates whether adaptive integrity boundaries can become a practical computational mechanism for improving long-term robustness in autonomous systems.

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

The Dynamic Constraint Boundary (DCB) framework has progressed beyond a purely conceptual proposal.

A functional proof-of-concept (PoC) has already been developed, including mathematical formalization, deterministic decision mechanisms, and a working Python implementation. This initial implementation was evaluated in controlled navigation environments designed to compare DCB against conventional stochastic decision-making.

The objective of these experiments was not to claim Artificial General Intelligence or complete AI alignment. Instead, they were designed to answer a narrower research question:

Can an adaptive integrity-based deterministic architecture demonstrate measurable behavioral advantages over stochastic exploration under controlled conditions?

The initial benchmark produced encouraging results.

Across repeated Monte Carlo simulations, DCB consistently required fewer decision steps than stochastic exploration while maintaining deterministic behavior. Statistical analysis indicated that the observed differences were unlikely to be explained by random variation.

These preliminary experiments demonstrate three important findings.

First, DCB can operate as a complete decision architecture rather than only a theoretical description.

Second, adaptive internal boundaries can be implemented computationally and updated continuously during interaction with the environment.

Third, the framework produces measurable behavioral differences that justify further investigation in more complex environments.

However, these results represent only the first stage of validation.

Current experiments have been performed primarily in controlled maze environments because they provide a reproducible benchmark where every decision, constraint, and adaptation can be measured objectively.

The next phase of this research will extend evaluation toward larger state spaces, more complex autonomous agents, adversarial environments, and eventually generative AI systems where integrity, long-term consistency, and alignment become significantly more challenging.

The existing PoC therefore serves as empirical evidence that the underlying hypothesis is technically feasible and worthy of further research rather than as proof that the complete DCB architecture has already been achieved.

Research Objectives and 12-Month Research Plan

The project will be conducted over twelve months, divided into four research phases. Each phase builds directly upon the previous one, allowing iterative refinement while maintaining flexibility as new experimental findings emerge.

Phase I – Mathematical Consolidation and DCB Core Design (Months 1–3)

Objectives:

Refine the mathematical formulation of Dynamic Constraint Boundary.

Improve relationships among integrity variables.

Complete the theoretical specification of the DCB Core architecture.

Design modular software architecture.

Define benchmark protocols and evaluation metrics.

Expected Deliverables:

DCB Mathematical Framework Version 2.

Stable DCB Core specification.

Experimental benchmark design.

Phase II – DCB Core Implementation and Simulation (Months 4–6)

Objectives:

Implement the complete DCB Core.

Integrate all existing modules into a unified deterministic architecture.

Perform large-scale Monte Carlo simulations.

Evaluate computational efficiency and behavioral stability.

Validate deterministic integrity adaptation.

Expected Deliverables:

Functional DCB Core prototype.

Large-scale simulation dataset.

Statistical validation report.

Phase III – Scaling DCB from Controlled Environments to AI Alignment Scenarios (Months 7–9)

Objectives:

Extend experiments beyond maze environments.

Develop alignment-oriented benchmark tasks.

Test adaptive integrity under conflicting objectives.

Evaluate long-term consistency and behavioral robustness.

Investigate potential applications to autonomous and generative AI.

Expected Deliverables:

AI Alignment benchmark results.

Robustness analysis.

Comparative evaluation against stochastic baselines.

Phase IV – Documentation, Publication, and Intellectual Property (Months 10–12)

Objectives:

Consolidate all experimental results.

Prepare technical documentation.

Write research manuscripts.

Publish reproducible non-sensitive materials.

Prepare international patent application for DCB Core.

Expected Deliverables:

Final technical report.

Research papers.

Public benchmark documentation.

International patent preparation.

Open research materials that do not compromise intellectual property.

Research Methodology

Overview

This project follows an iterative research methodology that combines theoretical development, computational implementation, controlled experimentation, statistical validation, and AI alignment evaluation.

Rather than beginning with complex generative AI systems, the project intentionally progresses from simple, fully observable environments toward increasingly realistic alignment scenarios. This staged methodology allows every component of the Dynamic Constraint Boundary (DCB) architecture to be independently validated before integration into more complex systems.

The methodology consists of five interconnected activities.

1. Mathematical Development

The first activity focuses on refining the mathematical formulation of DCB.

This includes:

• Formalizing integrity dynamics,

• Refining adaptive boundary equations,

• Analyzing computational stability,

• Improving consistency among internal variables,

• Verifying theoretical coherence.

The mathematical framework will remain the foundation of every subsequent implementation.

2. Computational Architecture Development

The second activity develops the DCB Core architecture.

This stage includes:

• Deterministic decision engine,

• Adaptive integrity boundary,

• Boundary update mechanism,

• Forbidden-state memory,

• Modular implementation,

• Computational optimization.

The objective is to produce a reusable architecture rather than a task-specific algorithm.

3. Controlled Benchmark Experiments

Controlled benchmark environments will be used to evaluate DCB under reproducible conditions.

Initial environments include:

• Navigation tasks,

• Maze benchmarks,

• Obstacle avoidance,

• Dynamic environments,

• Progressively larger state spaces.

The purpose of these benchmarks is not to demonstrate AGI, but to isolate behavioral properties that can be objectively measured.

Performance metrics include:

• Success Rate

• Decision Steps

• Collision Frequency

• Computational Operations

• Runtime

• Behavioral Stability

4. Statistical Validation

Experimental results will be analyzed using statistical methods.

The analysis includes:

• Repeated Monte Carlo simulations,

• Descriptive statistics,

• Confidence intervals,

• Welch’s t-test,

• Effect size (Cohen’s d),

• Reproducibility analysis.

The goal is to determine whether observed behavioral differences are statistically meaningful rather than products of random variation.

5. Alignment-Oriented Evaluation

The final methodological stage investigates whether DCB principles remain effective in scenarios related to AI Alignment.

Examples include:

• Conflicting objectives,

• Long-term consistency,

• Adversarial environments,

• Integrity preservation under uncertainty,

• Autonomous decision-making.

These experiments are intended to evaluate adaptive integrity as a computational principle rather than to claim a complete AI alignment solution.

Reproducibility

All reported experiments will be reproducible using documented benchmark protocols.

Whenever possible, benchmark datasets, evaluation scripts, and non-sensitive implementation details will be publicly released after intellectual property protection has been secured.

Sensitive components directly related to the DCB Core architecture will remain confidential until the patent application process has been completed.

How will this funding be used?

1. Research Equipment

Estimated Budget: USD 3,500

Includes:

Research laptop/workstation

External SSD storage

Backup storage

Supporting peripherals

Reliable computing equipment is essential for long-running simulations, software development, statistical analysis, and manuscript preparation.

2. Cloud Computing and Computational Resources

Estimated Budget: USD 5,000

Includes:

GPU cloud computing

CPU cloud instances

Large-scale Monte Carlo simulations

Long-duration benchmark experiments

Data storage

As DCB evolves beyond maze environments toward larger alignment benchmarks, computational requirements are expected to increase substantially.

3. Research Living Support

Estimated Budget: USD 12,000

This supports the researcher’s living expenses during the twelve-month project.

The objective is to allow continuous full-time research without interruption from unrelated employment.

As an independent researcher without institutional funding, dedicated research time is one of the most critical resources for completing the project.

4. Intellectual Property and Publication

Estimated Budget: USD 2,500

Includes:

Patent preparation

Professional legal consultation (if required)

Publication expenses

Open-access fees

Documentation preparation

The project intends to protect the DCB Core architecture before broader public technical disclosure.

5. Research Operations

Estimated Budget: USD 2,500

Includes:

Software subscriptions

Domain and website

Data backup

Communication

Conference participation (if appropriate)

Miscellaneous research expenses

Budget Summary

Category

Amount (USD)

Research Equipment

3,500

Cloud Computing

5,000

Research Living Support

12,000

Intellectual Property & Publication

2,500

Research Operations

2,500

Total

25,500

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

Team: Independent researcher (solo project).

I am conducting this project as an independent researcher based in Banjarmasin, Indonesia. This project does not currently have institutional affiliation or a formal research team. External collaborators may be invited later for mathematical review, software engineering, benchmarking, and AI alignment evaluation as the project progresses.

I am an independent researcher developing the Dynamic Constraint Boundary (DCB) framework.

So far I have:

• developed the conceptual framework and mathematical formulation of DCB;

• implemented multiple Python proof-of-concept simulations;

• benchmarked DCB against Random, ε-Greedy, Q-Learning, and SARSA agents across multiple maze environments;

• actively discussed AI alignment concepts with researchers through public online discussions.

This grant would enable me to move from an individual proof-of-concept into a rigorous, independently evaluated research project.

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

The primary risk is that the Dynamic Constraint Boundary (DCB) framework may not demonstrate sufficient advantages when evaluated in more complex AI alignment tasks beyond navigation benchmarks. While the current proof-of-concept shows encouraging results, these findings may not fully generalize to larger or more realistic decision-making systems.

Another risk is that developing a mathematically rigorous and experimentally validated framework may require more time than anticipated, particularly as an independent researcher without institutional support.

A further challenge is obtaining independent evaluation and expert feedback from the AI alignment community, which is essential for validating the framework.

If the project does not fully achieve its long-term objectives, it will still produce valuable outputs, including benchmark datasets, experimental results, mathematical formulations, software prototypes, and a documented evaluation framework. These outputs can guide future research on integrity-centered AI alignment, even if DCB ultimately proves to require substantial revision or is not adopted in its current form.

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

US$0.

No funding has been secured for this project to date. The project has been developed independently using my personal time and resources.

Author Declaration

Portions of this proposal were prepared with the assistance of generative AI as a writing and language-support tool. All research ideas, hypotheses, technical direction, design decisions, and responsibility for the contents remain solely with the author.