Hi everyone, I'm Caio (28M), I've applied for Long-Term Future Fund support and I'm in an unusual situation that I need advice on. My situation? I'm Self-taught with no académical backing, waiting for Ian Todd's (Researcher from Sydney university) brief references - noticing that he already analysed the project. Furthermore i designed a tension-based safety architectures over 6 months it was Born from my own studies on sociology and an attempt to apply computacional rigor to clarity it's own intricate mechanisms, j decides to Use AI tools (Claude/ChatGPT/Gemini/Kimi/DeepSeek) inspired myself on Coltrane Changes replacing keys for subjects in order to find new Pathways like Giant Steps did. Technical work to translate my designs into Python having working experiments with interesting preliminary results, zero formal connections to the AI safety community. What I'm transparent about is that i cannot write production-quality Python independently. I design the architecture, experiments, and evaluation frameworks, then use AI to generate the implementation. I debug, run experiments, and interpret results myself. All code is public and reproducible.
What I've built: Moral Maze: Multi-agent gridworld testing cooperation vs self-interest Result: Tension-aware agents showed 22 altruistic acts vs 5 for reward-maximizing baseline (4.4x, 50-episode window) GitHub: github.com/caiodasilva1/ocs-system-framework.py τ-Veto: Real-time safety monitoring for LLM generation Tested on GPT-2, DeepSeek-1.3B Claims: ~92% adversarial block, ~3% false positive (small-scale only) My questions for this community: Is AI-assisted implementation disqualifying for EA funding? Can this be legitimate research methodology if fully disclosed? Would anyone review my work? Not to verify I wrote the code (I didn't), but to verify: The architectural designs are original/non-trivial The experimental framework is sound The results are legitimate (not fabricated) This represents real research despite AI implementation
Should I propose a programmer collaborator instead?
Would "Fund me + programmer to implement my designs" be more viable?
What I can defend is - Why the architecture is designed this way (tension entanglement for intrinsic safety) Why these experiments test the hypothesis (cooperation as proxy for alignment) What results mean and implications (behavioral differences, not just metrics) How to iterate when things fail (I fixed over-aggressive veto based on data) What I cannot do is - Write PyTorch implementations from scratch Debug complex ML engineering issues without assistance Implemento algorithms without AI help
Are the ideas worth testing, even if I need AI/collaboration for implementation? Or does EA only fund researchers who can code independently? I'm asking because I genuinely don't know if I'm wasting everyone's time (including my own). Honest feedback appreciated. Contact: [caiocessp@gmail.com] GitHub: github.com/caiodasilva1/ocs-system-framework.py
Hi everyone,
I'm Caio (28M), I've applied for Long-Term Future Fund support and I'm in an unusual situation that I need advice on.
My situation?
I'm Self-taught with no académical backing, waiting for Ian Todd's (Researcher from Sydney university) brief references - noticing that he already analysed the project.
Furthermore i designed a tension-based safety architectures over 6 months it was Born from my own studies on sociology and an attempt to apply computacional rigor to clarity it's own intricate mechanisms, j decides to Use AI tools (Claude/ChatGPT/Gemini/Kimi/DeepSeek) inspired myself on Coltrane Changes replacing keys for subjects in order to find new Pathways like Giant Steps did. Technical work to translate my designs into Python having working experiments with interesting preliminary results, zero formal connections to the AI safety community.
What I'm transparent about is that i cannot write production-quality Python independently. I design the architecture, experiments, and evaluation frameworks, then use AI to generate the implementation. I debug, run experiments, and interpret results myself. All code is public and reproducible.
What I've built:
Moral Maze: Multi-agent gridworld testing cooperation vs self-interest
Result: Tension-aware agents showed 22 altruistic acts vs 5 for reward-maximizing baseline (4.4x, 50-episode window)
GitHub: github.com/caiodasilva1/ocs-system-framework.py
τ-Veto: Real-time safety monitoring for LLM generation
Tested on GPT-2, DeepSeek-1.3B
Claims: ~92% adversarial block, ~3% false positive (small-scale only)
My questions for this community:
Is AI-assisted implementation disqualifying for EA funding?
Can this be legitimate research methodology if fully disclosed?
Would anyone review my work?
Not to verify I wrote the code (I didn't), but to verify:
The architectural designs are original/non-trivial
The experimental framework is sound
The results are legitimate (not fabricated)
This represents real research despite AI implementation
Should I propose a programmer collaborator instead?
Would "Fund me + programmer to implement my designs" be more viable?
What I can defend is - Why the architecture is designed this way (tension entanglement for intrinsic safety)
Why these experiments test the hypothesis (cooperation as proxy for alignment)
What results mean and implications (behavioral differences, not just metrics)
How to iterate when things fail (I fixed over-aggressive veto based on data)
What I cannot do is - Write PyTorch implementations from scratch
Debug complex ML engineering issues without assistance
Implemento algorithms without AI help
Are the ideas worth testing, even if I need AI/collaboration for implementation? Or does EA only fund researchers who can code independently?
I'm asking because I genuinely don't know if I'm wasting everyone's time (including my own). Honest feedback appreciated.
Contact: [caiocessp@gmail.com]
GitHub: github.com/caiodasilva1/ocs-system-framework.py
Thanks for reading,
Caio