Abstract
Modern distributed inference architectures increasingly separate computing topologies into disaggregated Prefill (compute-bound) and Decode (memory-bandwidth-bound) nodes to maximize hardware efficiency. However, this decoupling introduces a severe operational bottleneck: the physical transmission of high-dimensional Key-Value (KV) cache tensors across the interconnect fabric layer during context handoffs or dynamic tool-calling forks. This paper introduces a zero-bandwidth state synchronization method that mitigates physical I/O serialization delays. Instead of transmitting raw multi-gigabyte float/int KV matrices over hardware interconnects, we serialize a compressed, deterministic symbolic vector—an Anamorphic Semantic Seed (\(\Psi \)). The target Decode node then utilizes a localized coordinate generation template to reconstruct the tensor boundaries in situ. Our empirical validation demonstrates that replacing physical network transfers with bounded local matrix arithmetic effectively neutralizes the inter-chip interconnect wall.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "ANAMORPHIC STATE RECONSTRUCTION VIA DETERMINISTIC SYMBOLIC SEEDING IN DISAGGREGATED INFERENCE RUNTIMES", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11725