EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents

arXiv cs.AI / 3/27/2026

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Key Points

The paper argues that widely used LLM reasoning strategies like Chain-of-Thought can waste computation at web scale, increasing energy use and carbon emissions while limiting access in resource-constrained regions.
It introduces EcoThink, an energy-aware adaptive inference framework that uses a lightweight, distillation-based router to decide when to skip deep reasoning and when to apply it for complex logic.
EcoThink targets both sustainability (supporting UN SDG 13) and inclusivity (supporting SDG 10) by reducing algorithmic waste during LLM agent inference.
Experiments on nine benchmarks show an average 40.4% reduction in inference energy, with up to 81.9% savings for web knowledge retrieval, while maintaining no statistically significant performance degradation.
The authors position EcoThink as a scalable approach to building more sustainable and accessible generative AI agents without sacrificing quality.

Abstract

As the Web transitions from static retrieval to generative interaction, the escalating environmental footprint of Large Language Models (LLMs) presents a critical sustainability challenge. Current paradigms indiscriminately apply computation-intensive strategies like Chain-of-Thought (CoT) to billions of daily queries, causing LLM overthinking, a redundancy that amplifies carbon emissions and operational barriers. This inefficiency directly undermines UN Sustainable Development Goals 13 (Climate Action) and 10 (Reduced Inequalities) by hindering equitable AI access in resource-constrained regions. To address this, we introduce EcoThink, an energy-aware adaptive inference framework designed to reconcile high-performance AI intelligence with environmental responsibility. EcoThink employs a lightweight, distillation-based router to dynamically assess query complexity, skipping unnecessary reasoning for factoid retrieval while reserving deep computation for complex logic. Extensive evaluations across 9 diverse benchmarks demonstrate that EcoThink reduces inference energy by 40.4% on average (up to 81.9% for web knowledge retrieval) without statistically significant performance loss. By mitigating algorithmic waste, EcoThink offers a scalable path toward a sustainable, inclusive, and energy-efficient generative AI Agent.