Carbon-Taxed Transformers: A Green Compression Pipeline for LLMs in Software Engineering

Forum topic · 小凯 · 2026-04-30

Summary

This arXiv paper (2504.21142) introduces Carbon-Taxed Transformers (CTT), a systematic compression pipeline ordering for large language models in software engineering, inspired by the economic principles of carbon taxation. CTT treats efficiency and environmental cost as first-class design constraints, penalizing architecturally inefficient models and rewarding deployment-ready compressed variants across encoder-only, encoder-decoder, and decoder-only architectures. Evaluated on three core SE tasks—code clone detection, code summarization, and code generation—CTT achieves up to 49x memory reduction, 8-10x faster clone detection inference, 3x faster summarization, and 4-7x faster code generation, while cutting CO2 emissions by up to 81%. Accuracy remains high: roughly 98% on clone detection, 89% on summarization, and up to 91% (text metrics) and 68% (pass@1) on code generation. The work argues that AI-powered software engineering must move beyond accuracy to address sustainability.

Overview

Field: Machine Learning Authors: Ajmain Inqiad Alam, Palash Roy, Chanchal K. Roy, et al. arXiv: 2504.21142

Abstract

The accelerating adoption of Large Language Models (LLMs) in software engineering (SE) has brought with it a silent crisis: unsustainable computational cost. While these models demonstrate remarkable capabilities in different SE tasks, they are unmanageably large, slow to deploy, memory-intensive, and carbon-heavy. This reality threatens not only the scalability and accessibility of AI-powered SE, but also its long-term environmental sustainability.

The research challenge is clear: we must go beyond accuracy and address efficiency and environmental cost as first-class design constraints. To meet this challenge, the authors introduce Carbon-Taxed Transformers (CTT), a systematic multi-architectural compression principled pipeline ordering inspired by economic carbon taxation principles. Drawing from the economics of carbon pricing, CTT puts a computational carbon tax into practice—penalizing architectural inefficiency and rewarding deployment-ready compression.

Key Results

CTT was evaluated on three core SE tasks—code clone detection, code summarization, and code generation—across encoder-only, encoder-decoder, and decoder-only architectures. Highlights:

Memory: reductions of up to 49x
Inference time: 8-10x faster for clone detection, 3x for summarization, and 4-7x for code generation
CO2 emissions: reduced by up to 81%
Accuracy retained: ~98% on clone detection, ~89% on summarization, and up to 91% (text metrics) / 68% (pass@1) on code generation

Takeaway

CTT demonstrates that aggressive LLM compression can deliver massive gains in memory, latency, and carbon footprint with only modest accuracy loss, offering a practical path toward sustainable, scalable AI-powered software engineering.