Fractional Decay KV-Cache: Ownership-Aware Memory Management for Improved Inference Relevancy in Dialog Systems
FD-KVC introduces a dual-channel scoring mechanism for KV-cache eviction, combining cumulative attention importance with recency-weighted relevance via temporal decay and reinforcement-inspired updates An adaptive learning rate driven by an ownership loss function ensures stable convergence without oscillation during cache management FD-KVC operates entirely on CPU with negligible overhead, making it practical for real-world deployment Outperforms H2O by +6.7% on composite late-turn alignment, w
Analysis
TL;DR
- FD-KVC introduces a dual-channel scoring mechanism for KV-cache eviction, combining cumulative attention importance with recency-weighted relevance via temporal decay and reinforcement-inspired updates
- An adaptive learning rate driven by an ownership loss function ensures stable convergence without oscillation during cache management
- FD-KVC operates entirely on CPU with negligible overhead, making it practical for real-world deployment
- Outperforms H2O by +6.7% on composite late-turn alignment, with dramatic gains of +127% on topic-shift, +87% on gradual evolution, and +30% on mixed-topic dialogs
- Adapts to new topics 3.6X faster than H2O and achieves the highest topic diversity (80.6%) across all evaluated methods
Why It Matters
This work addresses a critical bottleneck in transformer-based dialog systems: KV-cache management during long, multi-turn conversations where topics naturally shift. By enabling faster adaptation to topic changes while preserving historically important context, FD-KVC directly improves the relevance and coherence of generated responses in real-world conversational AI applications.
Technical Details
- Dual-channel scoring: Each cached KV pair maintains two scores — a cumulative attention channel tracking aggregate importance (similar to H2O's heavy-hitter approach) and a recency-weighted relevance channel using temporal decay and reinforcement-inspired updates
- Ownership loss function: Drives an adaptive learning rate that ensures convergence without oscillation during cache eviction decisions
- CPU-only execution: The algorithm runs entirely on CPU with negligible overhead, avoiding the need for specialized GPU-based cache management
- Evaluation: Tested across five diverse multi-turn dialog scenarios with 600 dialogs each, comparing against H2O as the state-of-the-art heavy-hitter baseline
- Ablation studies: Confirmed the individual contribution of each component (cumulative attention channel, recency-weighted channel, adaptive learning rate) to overall performance
Industry Insight
- Dialog systems and chatbots that handle long conversations will benefit significantly from adaptive cache management, especially in domains where topic shifts are frequent (customer support, tutoring, therapy bots)
- The CPU-only design makes this approach accessible for edge deployment and cost-sensitive applications that cannot afford GPU-based cache optimization
- The 3.6X faster adaptation to new topics suggests that conversational AI systems could maintain higher relevance over extended interactions, potentially reducing user frustration and improving task completion rates
Disclaimer: The above content is generated by AI and is for reference only.