So, the core is basically an audit loop on thinking tokens to re-steer the model?
Seems like an interesting direction to reduce token usage/agent error. This would be a lot easier to grok if you created a benchmark, that would both demonstrate your ideal use case and the potential improvement your method brings.
Also, I'd avoid directly using model output for HN/social media communications. I don't really care if the information is correct and concise, but some people get really triggered by it.
Seems like an interesting direction to reduce token usage/agent error. This would be a lot easier to grok if you created a benchmark, that would both demonstrate your ideal use case and the potential improvement your method brings.
Also, I'd avoid directly using model output for HN/social media communications. I don't really care if the information is correct and concise, but some people get really triggered by it.