So like- I considered this idea of using a fast decision model to prune before compaction, but I scrapped it because A; im not bottlenecked by speed, and B; cache invalidation dark magic
From my understanding, if we have turns [A, B, C, D, E] and we decide that turn C is useless context, then we lose our cache for the [D, E] segment. (plz correct me if im wrong, I havent tested this)
Personally, I pay no mind to session state nowadays. I exclusively use provider native hosted compaction, which if you believe the providers, is the way to go. For context management- I spend most of my effort on the things which dump output back to the model.
If anyone has any good reads on benchmarking OpenAI and Anthropic hosted/native compaction against custom stuff, I would love to read!
From my understanding, if we have turns [A, B, C, D, E] and we decide that turn C is useless context, then we lose our cache for the [D, E] segment. (plz correct me if im wrong, I havent tested this)
Personally, I pay no mind to session state nowadays. I exclusively use provider native hosted compaction, which if you believe the providers, is the way to go. For context management- I spend most of my effort on the things which dump output back to the model.
If anyone has any good reads on benchmarking OpenAI and Anthropic hosted/native compaction against custom stuff, I would love to read!