PBKV predicts agent invocations in dynamic LLM workflows to manage KV-cache reuse, delivering up to 1.85x speedup over LRU and 1.26x over KVFlow.
Beyond black-box benchmarking: Observability, analytics, and optimization of agentic systems
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
AI agents require distinct regulation as AI systems under the EU AI Act with orchestration-layer oversight and a risk-based traffic light authorization system in contract law to preserve human accountability.
Introduces Agent Behavior Mining to translate generative AI agent activities into standardized process logs, enabling process mining for policy deviation detection and variability quantification in business processes, with practitioner feedback.
citing papers explorer
-
Efficient Serving for Dynamic Agent Workflows with Prediction-based KV-Cache Management
PBKV predicts agent invocations in dynamic LLM workflows to manage KV-cache reuse, delivering up to 1.85x speedup over LRU and 1.26x over KVFlow.
-
A pragmatic approach to regulating AI agents
AI agents require distinct regulation as AI systems under the EU AI Act with orchestration-layer oversight and a risk-based traffic light authorization system in contract law to preserve human accountability.
-
Agent Behavior Mining: Generative AI Agent Governance in Business Processes
Introduces Agent Behavior Mining to translate generative AI agent activities into standardized process logs, enabling process mining for policy deviation detection and variability quantification in business processes, with practitioner feedback.