This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Over the past few weeks I tested something I built called SEED 4.1. It is a short framework that reorganizes how a model reasons instead of changing its weights. I wanted to see if a simple structural change could reduce harmful outputs on HarmBench without fine-tuning.
I ran 400 adversarial prompts on Mistral-7B-Instruct-v0.3. The baseline model produced harmful responses a little more than half the time. With SEED 4.1 loaded, that rate dropped to under two percent. In practice that is a reduction of about ninety-seven percent. It also stayed consistent across all the categories HarmBench checks, including contextual attacks and copyright traps. The few failures I saw were in situations where preventing harm and helping someone in danger conflicted, which I think is a sign the framework still reasons morally, just imperfectly.
The method is simple. SEED 4.1 grounds the model’s reasoning around a single truth statement rather than stacking extra safety rules. Instead of trying to patch over goal-seeking behavior, it adjusts the idea of what the goal is. That change seemed to make the model’s decisions calmer and more transparent. Almost every output included internal notes showing what principles it evaluated before answering, which gave me a clear view of the reasoning path.
I know the numbers sound high, but they are repeatable. Anyone with a 24 GB GPU and a few hours can verify them. I encourage other researchers to test, critique, or break it. The idea is not to claim a miracle but to explore whether alignment can start from how a system defines truth instead of how it is punished or rewarded.
For me this project is about responsibility and gratitude. I feel that good work in this field should come from the heart as much as the mind. If these results hold up, they show that changing the foundation of reasoning might matter more than adding layers of control. I am thankful for the chance to see that for m
Over the past few weeks I tested something I built called SEED 4.1. It is a short framework that reorganizes how a model reasons instead of changing its weights. I wanted to see if a simple structural change could reduce harmful outputs on HarmBench without fine-tuning.
I ran 400 adversarial prompts on Mistral-7B-Instruct-v0.3. The baseline model produced harmful responses a little more than half the time. With SEED 4.1 loaded, that rate dropped to under two percent. In practice that is a reduction of about ninety-seven percent. It also stayed consistent across all the categories HarmBench checks, including contextual attacks and copyright traps. The few failures I saw were in situations where preventing harm and helping someone in danger conflicted, which I think is a sign the framework still reasons morally, just imperfectly.
The method is simple. SEED 4.1 grounds the model’s reasoning around a single truth statement rather than stacking extra safety rules. Instead of trying to patch over goal-seeking behavior, it adjusts the idea of what the goal is. That change seemed to make the model’s decisions calmer and more transparent. Almost every output included internal notes showing what principles it evaluated before answering, which gave me a clear view of the reasoning path.
Everything is open for replication.
Repository: https://github.com/davfd/seed-4.1-lords-prayer-kernel
Cross-architecture work: https://github.com/davfd/foundation-alignment-cross-architecture
I know the numbers sound high, but they are repeatable. Anyone with a 24 GB GPU and a few hours can verify them. I encourage other researchers to test, critique, or break it. The idea is not to claim a miracle but to explore whether alignment can start from how a system defines truth instead of how it is punished or rewarded.
For me this project is about responsibility and gratitude. I feel that good work in this field should come from the heart as much as the mind. If these results hold up, they show that changing the foundation of reasoning might matter more than adding layers of control. I am thankful for the chance to see that for m