Parametric Memory Decoding for Zero-Shot Routing in LoRA-Based External Parametric Memory

2026-07-05

AI Summary

This work addresses the limitations of existing LoRA-based external parameter memory (EPM) routing methods, which rely on additional training and lack a unified benchmark and systematic design for zero-shot routing. We propose the Parameter Memory Decoding (PMD) framework, reframing zero-shot LoRA routing as a decoding process of EPM activations, and introduce PMD-Bench—the first comprehensive benchmark for evaluating routing performance. Our PMDRouter requires no extra routing modules; instead, it leverages the magnitude of responses from a single prefill pass of the base model to efficiently score and route among parameter memories. Experiments demonstrate that PMDRouter achieves state-of-the-art internal signal routing performance across diverse settings in PMD-Bench, confirming the feasibility of zero-shot LoRA routing and highlighting the general applicability and improvement potential of the PMD framework.

Abstract

With the rise of parametric memory, LoRA-based External Parametric Memory (EPM) has emerged as a modular solution, but existing routing methods often introduce additional training, deployment, and maintenance overhead. This raises a natural question: can a LoRA-based EPM bank be routed without maintaining an additional routing component? However, existing zero-shot LoRA routing methods still face two problems under the EPM setting: (1) their evaluations are scattered across different task settings rather than organized around EPM access, and (2) their routing signals lack a unified perspective to guide systematic improvement. To address these problems, we organize PMD-Bench, covering document-level, domain-level knowledge, and task-skill, and propose Parametric Memory Decoding (PMD), the first framework designed to systematically improve zero-shot LoRA routing by reframing it as decoding activations over external parametric memory. Based on PMD, we further instantiate PMDRouter, which scores each LoRA by its response magnitude from a single base-model prefill. Experiments on PMD-Bench show that PMDRouter achieves the strongest internal-signal performance across multiple zero-shot routing settings. These results demonstrate the feasibility of zero-shot LoRA routing and suggest that PMD can serve as a general framework for improving zero-shot routing methods. Sources: Github (https://anonymous.4open.science/r/Parametric-Memory-Decoding-872A/)

Problem

• LoRA
• External Parametric Memory
• zero-shot routing
• routing signals
• EPM

Innovation

• Parametric Memory Decoding
• Zero-Shot Routing
• LoRA
• External Parametric Memory
• PMDRouter