# Tokenomics：量化模型token在智能体软件工程中的应用

- 来源：Hacker News 热门（buzzing.cc 中文翻译）
- 作者：Anon84
- 发布时间：2026-06-07 17:31
- AIHOT 分数：48
- AIHOT 链接：https://aihot.virxact.com/items/cmq3lykdm00gkslsg19nefn7r
- 原文链接：https://arxiv.org/abs/2601.14470

## AI 摘要

一篇arXiv论文提出Tokenomics框架，量化分析模型token在智能体软件工程各环节的使用分布，揭示不同任务对token消耗的差异。

## 正文

Computer Science > Software Engineering

Title:Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineering

Abstract:LLM-based Multi-Agent (LLM-MA) systems are increasingly applied to automate complex software engineering tasks such as requirements engineering, code generation, and testing. However, their operational efficiency and resource consumption remain poorly understood, hindering practical adoption due to unpredictable costs and environmental impact. To address this, we conduct an analysis of token consumption patterns in an LLM-MA system within the Software Development Life Cycle (SDLC), aiming to understand where tokens are consumed across distinct software engineering activities. We analyze execution traces from 30 software development tasks performed by the ChatDev framework using a GPT-5 reasoning model, mapping its internal phases to distinct development stages (Design, Coding, Code Completion, Code Review, Testing, and Documentation) to create a standardized evaluation framework. We then quantify and compare token distribution (input, output, reasoning) across these stages. Our preliminary findings show that the iterative Code Review stage accounts for the majority of token consumption for an average of 59.4% of tokens. Furthermore, we observe that input tokens consistently constitute the largest share of consumption for an average of 53.9%, providing empirical evidence for potentially significant inefficiencies in agentic collaboration. Our results suggest that the primary cost of agentic software engineering lies not in initial code generation but in automated refinement and verification. Our novel methodology can help practitioners predict expenses and optimize workflows, and it directs future research toward developing more token-efficient agent collaboration protocols.

Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2601.14470 [cs.SE] (or arXiv:2601.14470v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2601.14470 Focus to learn more arXiv-issued DOI via DataCite

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