MemHub: Zero-Shot Latent Memory Transfer Across Heterogeneous LLMs

Direct latent communication between language models currently takes two forms: pairwise projections, whose training cost grows quadratically with the number of models, and shared representation spaces, which admit new members solely through existing ones and have been shown only within one model family. We introduce MEMHUB, a memory hub in which each frozen model independently trains one Writer–Reader adapter pair against a small frozen anchor that fixes a shared latent coordinate system. Members never observe one another’s parameters, activations, or training, yet the persistent artifacts they write are mutually readable zero-shot. Across nine members from five heterogeneous families (0.5B–8B), MEMHUB recovers a median 96.1% of pair-specific readout gains over all 72 foreign directions, retains 97% of self-transfer under dataset shift, and onboards two held-out families with one adapter pair each and no incumbent retraining. Two hubs retrained from scratch read the first hub’s artifacts, and each other’s, at parity over 432 cross-hub directions: the interface belongs to the anchor, not to any training run. Zero-shot reads outperform a pair-trained C2C channel overall. The artifact is a fixed 64 KB object read at prefill cost: 21–33× faster to first token than a live text handoff and higher quality than a text channel trained at matched budget. Controlled factorials isolate the mechanism: the geometric constraint alone fixes the shared coordinate system, and anchoring two of 32 slots aligns the rest.