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This study evaluates the ability of frontier LLMs to predict the success rate of AI agents on real-world freelance tasks, revealing that performance hinges critically on how the agent's capabilities are described to the model. The research demonstrates a significant drop in predictive accuracy when models assume a generic agent configuration compared to those with specific constraints, offering practical insights for deploying autonomous agents in economic markets.
PAYWORTHY: Can LLMs Predict Which Freelance Jobs an AI Agent Will Complete?
arXiv:2611.05759v1 Announce Type: cross Abstract: Before an AI agent takes on a paid job, someone has to judge whether it is likely to succeed. We test whether frontier language models can make this call before any attempt. We propose PAYWORTHY which contains 1{,}494 real freelance job postings, sampled from 18.8M postings made between 2020 and 2026 on a freelancing platform. An agent attempts each job from the posting alone in a single run, and an LLM judge acting as the client decides whether to accept the work. We ask 18 models to forecast how likely the agent is to succeed and measure how well each model's forecasts rank the accepted jobs above the rejected ones (AUC, where 0.5 is a random ranking). The answer depends on how the agent is described. Told what the agent cannot do, every model ranks the jobs well (AUC 0.66--0.86). Told instead that it is a generic agent with a browser and code execution, the same models rank the same jobs about as well as a random ordering (0.45--0.56). These results hold under judges from four other model families (Claude, GLM, Kimi and Qwen), and the informed forecasts also rank the verdicts of two human raters. Code is available at https://github.com/upwork/Payworthy
Title:PAYWORTHY: Can LLMs Predict Which Freelance Jobs an AI Agent Will Complete?
Abstract:Before an AI agent takes on a paid job, someone has to judge whether it is likely to succeed. We test whether frontier language models can make this call before any attempt. We propose PAYWORTHY which contains 1{,}494 real freelance job postings, sampled from 18.8M postings made between 2020 and 2026 on a freelancing platform. An agent attempts each job from the posting alone in a single run, and an LLM judge acting as the client decides whether to accept the work. We ask 18 models to forecast how likely the agent is to succeed and measure how well each model's forecasts rank the accepted jobs above the rejected ones (AUC, where 0.5 is a random ranking). The answer depends on how the agent is described. Told what the agent cannot do, every model ranks the jobs well (AUC 0.66--0.86). Told instead that it is a generic agent with a browser and code execution, the same models rank the same jobs about as well as a random ordering (0.45--0.56). These results hold under judges from four other model families (Claude, GLM, Kimi and Qwen), and the informed forecasts also rank the verdicts of two human raters. Code is available at this https URL
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2611.05759 [cs.AI]
(or arXiv:2611.05759v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.05759 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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