Pith. sign in

REVIEW 1 cited by

Towards Sustainable Web Agents: A Plea for Transparency and Dedicated Metrics for Energy Consumption

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2502.17903 v1 pith:Y7BJVNFL submitted 2025-02-25 cs.AI cs.HC

classification cs.AIcs.HC
keywords agentsenergyconsumptionagentassociateddailydedicatedmetrics
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Improvements in the area of large language models have shifted towards the construction of models capable of using external tools and interpreting their outputs. These so-called web agents have the ability to interact autonomously with the internet. This allows them to become powerful daily assistants handling time-consuming, repetitive tasks while supporting users in their daily activities. While web agent research is thriving, the sustainability aspect of this research direction remains largely unexplored. We provide an initial exploration of the energy and CO2 cost associated with web agents. Our results show how different philosophies in web agent creation can severely impact the associated expended energy. We highlight lacking transparency regarding the disclosure of model parameters and processes used for some web agents as a limiting factor when estimating energy consumption. As such, our work advocates a change in thinking when evaluating web agents, warranting dedicated metrics for energy consumption and sustainability.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A three-pass LLM framework (extractor, detector, false-positive verifier) more than doubled PPV and halved estimated review costs for radiology report error detection, while the absolute number of confirmed errors sta...

Pith tools