By name: pbkv-agent-workflow description: Prediction-based KV-Cache management for efficient serving of dynamic agent workflows. Predicts future agent invocations to optimize cache eviction and prefetching. category: deep-learning tags: [LLM, KV-cache, agent-workflow, serving, inference-optimization, prediction] trigger: pbkv, kv-cache management, agent workflow serving, dynamic workflow, cache prediction, KVFlow
PBKV: Prediction-Based KV-Cache Management for Agent Workflows
Overview
PBKV optimizes KV-Cache management for dynamic LLM agent workflows by predicting future agent invocations and using these predictions to guide cache eviction and prefetching decisions.
Core Technique
- Workflow Prediction Model: Fuses historical workflow patterns with current task context to predict agent invocation sequence for next several steps
- Reuse Potential Estimation: Based on predictions, estimates which cache entries will be reused and prioritizes keeping them in GPU memory
- Conservative Policy: Uses predictions conservatively during both cache eviction and prefetching to be robust to prediction errors
- Dynamic Adaptation: Handles workflows where agent sequence depends on task context (unlike static workflow assumptions in prior work)
Key Benefits
- 1.85x speedup over LRU on dynamic workflows
- 1.26x speedup over KVFlow (SOTA) even on static workflows
- Robust to prediction errors via conservative cache management
Implementation Steps
- Collect historical workflow execution traces (agent sequences, contexts)
- Train lightweight prediction model: input = (current context + history), output = next N agent invocations
- At each step, predict future agents and estimate cache reuse potential
- Evict low-potential entries first; prefetch high-potential entries conservatively
- Fall back to safe eviction when prediction confidence is low
Pitfalls
- Prediction model must be lightweight — heavy models negate cache management savings
- Conservative policy may leave suboptimal entries in cache — tune confidence threshold
- Historical data distribution shift degrades prediction accuracy over time
Activation Keywords
pbkv, kv-cache management, agent workflow serving, dynamic workflow, cache prediction, KVFlow, LRU replacement, inference optimization