A water-filling token allocation policy optimally distributes output tokens across sequential LLM agents to maximize reliability under latency and cost constraints.
In these systems, agents collaborate to solve complex tasks by exchang- ing intermediate information and progressively refining their out- puts [1, 6]
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Toward Reliable Design of LLM-Enabled Agentic Workflows: Optimizing Latency-Reliability-Cost Tradeoffs
A water-filling token allocation policy optimally distributes output tokens across sequential LLM agents to maximize reliability under latency and cost constraints.