Hello,
I am reaching out not with a proposal or a request for collaboration,
but to ask a single technical question.
In many recent LLM-based services, issues related to safety and coherence appear to be addressed primarily through post-generation mechanisms such as policies, guardrails, and output filtering.
While this approach is effective in many cases, it also seems to involve a structural limitation:
• Inputs that are structurally invalid, or
• Situations that should not be subject to judgment or reasoning in the first place
are often processed internally by the model at least once before being blocked.
With this in mind, I would like to ask the following question:
In your system, at what stage do you explicitly distinguish between domains where an AI is permitted to perform judgment or reasoning, and domains that should not enter the generation or evaluation process at all?
Rather than focusing on improving the quality of generated outputs, we have been examining architectures that reduce the permissible judgment space itself prior to language generation or reasoning.
We are not seeking to share proprietary details, as relevant protections are already in place.
Our intention is simply to ask how this class of problem is viewed from a Safety / Alignment or system architecture perspective.
If this issue is considered meaningful within real-world deployment or model design contexts, we would be grateful for the opportunity to hear even a brief perspective.
Thank you very much for your time and consideration.
Sincerely,
Hello,
I am reaching out not with a proposal or a request for collaboration,
but to ask a single technical question.
In many recent LLM-based services, issues related to safety and coherence appear to be addressed primarily through post-generation mechanisms such as policies, guardrails, and output filtering.
While this approach is effective in many cases, it also seems to involve a structural limitation:
• Inputs that are structurally invalid, or
• Situations that should not be subject to judgment or reasoning in the first place
are often processed internally by the model at least once before being blocked.
With this in mind, I would like to ask the following question:
In your system, at what stage do you explicitly distinguish between domains where an AI is permitted to perform judgment or reasoning, and domains that should not enter the generation or evaluation process at all?
Rather than focusing on improving the quality of generated outputs, we have been examining architectures that reduce the permissible judgment space itself prior to language generation or reasoning.
We are not seeking to share proprietary details, as relevant protections are already in place.
Our intention is simply to ask how this class of problem is viewed from a Safety / Alignment or system architecture perspective.
If this issue is considered meaningful within real-world deployment or model design contexts, we would be grateful for the opportunity to hear even a brief perspective.
Thank you very much for your time and consideration.
Sincerely,