# TokenCast: Forecasting Token Consumption During LLM Agent Execution

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/tokencast-forecasting-token-consumption-during-llm-agent-execution
> **Original Source**: [arXiv cs.AI](https://arxiv.org/abs/2609.35760v1)
> **Author**: Chaoqian Ouyang, Ling Yue (undefined)
> **Published Date**: Monday, September 28, 2026
> **Category**: RESEARCH
> **Tags**: Research, arXiv, Machine Learning

## Executive Summary
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 ev



## Full Coverage
Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chaoqian Ouyang [ view email ] [v1] Mon, 28 Sep 2026 17:59:09 UTC (1,159 KB) [v2] Tue, 29 Sep 2026 10:45:52 UTC (1,159 KB) Full-text links: Access Paper: View a PDF of the paper titled TokenCast: Forecasting Token Consumption During LLM Agent Execution, by Chaoqian Ouyang and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-09 Change to browse by: cs cs.AI cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website

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