arXiv Artificial Intelligence

TokenCast: Forecasting Token Consumption During LLM Agent Execution

2 hours ago 30.09.2026 07:00 👁 5 views
TokenCast: Forecasting Token Consumption During LLM Agent Execution

Quick summary

arXiv:2609.35760v2 Announce Type: replace-cross Abstract: When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses its next steps based on tool feedback and intermediate results, while the growing context steadily inflates the input size of every subsequent call. The total consumption of a task is therefore hard to predict before execution and the prediction must be revised as the run unfolds. In this paper, we propose TokenCast, which learns a composable cost representation for each execution segmen

Key takeaways

  • arXiv:2609.35760v2 Announce Type: replace-cross Abstract: When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs.
  • The agent chooses its next steps based on tool feedback and intermediate results, while the growing context steadily inflates the input size of every subsequent call.
  • The total consumption of a task is therefore hard to predict before execution and the prediction must be revised as the run unfolds.

Why it matters

“TokenCast: Forecasting Token Consumption During LLM Agent Execution” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗