Fractional Decay KV-Cache: Ownership-Aware Memory Management for Improved Inference Relevancy in Dialog Systems

2026-07-20 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Fractional Decay KV-Cache (FD-KVC) is a novel algorithm designed to enhance inference relevancy in transformer-based dialog systems by improving key-value (KV) cache management. Unlike uniform or coarse eviction strategies, FD-KVC employs a dual-channel scoring mechanism for each KV pair, combining cumulative attention with a recency-weighted relevance channel that uses temporal decay and reinforcement-inspired updates. This allows it to preserve important historical tokens while rapidly adapting to topic shifts. An adaptive learning rate, driven by an ownership loss function, ensures stable convergence. Operating entirely on CPU with negligible overhead, FD-KVC significantly outperforms the H2O baseline, achieving +6.7% on composite late-turn alignment, +127% on topic-shift, +87% on gradual evolution, and +30% on mixed-topic dialogs across five multi-turn scenarios. It adapts 3.6×faster than H2O and boasts 80.6% topic diversity.

Key takeaway

For Machine Learning Engineers or NLP Engineers building transformer-based dialog systems, FD-KVC offers a compelling solution to improve inference relevancy and topic adaptation. If your current KV-cache strategies struggle with evolving dialogs or topic shifts, you should consider implementing FD-KVC's dual-channel scoring and ownership-aware memory management. This approach provides substantial performance gains, including 3.6×faster topic adaptation and higher topic diversity, without requiring GPU resources.

Key insights

FD-KVC uses dual-channel scoring and adaptive learning to manage KV-cache, improving dialog relevancy and topic adaptation.

Principles

• Dual-channel scoring balances historical importance and recency for KV-cache entries.
• Ownership loss functions can drive adaptive learning rates for stable cache management.
• KV-cache strategies must dynamically adapt to evolving dialog topics.

Method

FD-KVC employs a dual-channel scoring mechanism for KV pairs, combining cumulative attention with recency-weighted relevance, updated via temporal decay and reinforcement-inspired logic, guided by an adaptive learning rate and ownership loss.

In practice

• Implement dual-channel KV-cache scoring to balance long-term and short-term relevance.
• Integrate ownership loss functions for adaptive learning rate control in cache algorithms.
• Prioritize CPU-efficient KV-cache solutions for real-time dialog inference.

Topics

• KV-Cache Management
• Dialog Systems
• Transformer Inference
• Memory Management
• Topic Adaptation
• CPU Optimization

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.