Diego Bachman, co-founder of Manifest AI, explains the technical mechanism behind Manifest AI's Power Retention architecture.
Assertion Contradicted
Bachman: Models claiming 256k+ context use windowed transformers, discarding data
“Anybody who says they're using a transformer
With a context length of, you know, 256,000 or more, they're not using a true transformer.
What they're using is a windowed transformer that essentially throws out a huge amount of its information at various layers …”
Assertion Open · timeframe Sep 2026
Bachman: Power Retention Delivers 100x Inference Speedup at 64k Context
“And at 64 K tokens, We get something like a 10 X speed up at training, but at inference time, because you're not only saving flops at inference time, but also paging in and out of memory of the KV cache, you actually get a hundred X speed ups from power retent…”
Assertion Open · timeframe Sep 2028
Bachman: Power Retention models match original base model performance
“They'll come out with a nice shiny new, a power retention architecture that has the same performance on whatever data set they want as the original base model did.”
Assertion Open · timeframe Sep 2028
Bachman: StarCoder-3B converted to Power Retention matches baseline loss in two hours
“After just 10,000 steps of training, which this training one took about two hours, this orange curve, you see that it fully matches the original loss.”
Prediction Open · timeframe Sep 2026
Bachman: Big Foundation Models Will Train on Power Retention Within a Year
“After that, I think, you know, probably within six months to a year, we're going to start to see the really big foundation models being trained in this way.”
Insight
Bachman: Compute-optimal models on internet text don't need long context
“In general, most internet text has mostly short-term structure. There's just not that much value in capturing long-term structure, and so compute optimal models on internet text actually don't have that long context, and so, of course, you're perfectly fine us…”