Cover Image

Can AI Freelancers Compete? Benchmarking Earnings, Reliability, and Task Success at Scale

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Noever, David, McKee, Forrest
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Forbidden Science: Dual-Use AI Challenge Benchmark and Scientific Refusal Tests
by: Noever, David, et al.
Published: (2025)

Alpha Excel Benchmark
by: Noever, David, et al.
Published: (2025)

The Impossible Test: A 2024 Unsolvable Dataset and A Chance for an AGI Quiz
by: Noever, David, et al.
Published: (2024)

Transparency Attacks: How Imperceptible Image Layers Can Fool AI Perception
by: McKee, Forrest, et al.
Published: (2024)

Exploiting Alpha Transparency In Language And Vision-Based AI Systems
by: Noever, David, et al.
Published: (2024)

Hallucinating AI Hijacking Attack: Large Language Models and Malicious Code Recommenders
by: Noever, David, et al.
Published: (2024)

AirTag, You're It: Reverse Logistics and Last Mile Dynamics
by: Noever, David, et al.
Published: (2025)

Moravec's Paradox: Towards an Auditory Turing Test
by: Noever, David, et al.
Published: (2025)

Goal-Directed Search Outperforms Goal-Agnostic Memory Compression in Long-Context Memory Tasks
by: Zheng, Yicong, et al.
Published: (2025)

Can AI-Generated Text be Reliably Detected?
by: Sadasivan, Vinu Sankar, et al.
Published: (2023)

Novel AI Camera Camouflage: Face Cloaking Without Full Disguise
by: Noever, David, et al.
Published: (2024)

Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
by: Ye, Jiasheng, et al.
Published: (2023)

Multi-Task GRPO: Reliable LLM Reasoning Across Tasks
by: Ramesh, Shyam Sundhar, et al.
Published: (2026)

Workspace-Bench 1.0: Benchmarking AI Agents on Workspace Tasks with Large-Scale File Dependencies
by: Tang, Zirui, et al.
Published: (2026)

Uncovering Competency Gaps in Large Language Models and Their Benchmarks
by: Bohacek, Maty, et al.
Published: (2025)

Reliable Evaluation and Benchmarks for Statement Autoformalization
by: Poiroux, Auguste, et al.
Published: (2024)

Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning
by: Zhang, Jifan, et al.
Published: (2024)

Predicting Task Performance with Context-aware Scaling Laws
by: Montgomery, Kyle, et al.
Published: (2025)

Beyond No: Quantifying AI Over-Refusal and Emotional Attachment Boundaries
by: Noever, David, et al.
Published: (2025)

Gradients Must Earn Their Influence: Unifying SFT with Generalized Entropic Objectives
by: Wang, Zecheng, et al.
Published: (2026)

Infecting Generative AI With Viruses
by: Noever, David, et al.
Published: (2025)

Stop Listening to Me! How Multi-turn Conversations Can Degrade LLM Reliability
by: Guo, Kevin H., et al.
Published: (2026)

Can Language Models Discover Scaling Laws?
by: Lin, Haowei, et al.
Published: (2025)

Generation Constraint Scaling Can Mitigate Hallucination
by: Kollias, Georgios, et al.
Published: (2024)

BEExAI: Benchmark to Evaluate Explainable AI
by: Sithakoul, Samuel, et al.
Published: (2024)

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective
by: Jesson, Andrew, et al.
Published: (2024)

MiMIC: Multi-Modal Indian Earnings Calls Dataset to Predict Stock Prices
by: Ghosh, Sohom, et al.
Published: (2025)

Large Language Models Can Self-Improve At Web Agent Tasks
by: Patel, Ajay, et al.
Published: (2024)

Learning from Relevant Subgoals in Successful Dialogs using Iterative Training for Task-oriented Dialog Systems
by: Kaiser, Magdalena, et al.
Published: (2024)

Can Generative AI Support Patients' & Caregivers' Informational Needs? Towards Task-Centric Evaluation Of AI Systems
by: Rajagopal, Shreya, et al.
Published: (2024)

TDBench: A Benchmark for Top-Down Image Understanding with Reliability Analysis of Vision-Language Models
by: Hou, Kaiyuan, et al.
Published: (2025)

Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents
by: Miao, Jiacheng, et al.
Published: (2025)

Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environments
by: Mannekote, Amogh, et al.
Published: (2024)

Beyond Benchmarks: On The False Promise of AI Regulation
by: Stanovsky, Gabriel, et al.
Published: (2025)

Benchmarking the Medical Understanding and Reasoning of Large Language Models in Arabic Healthcare Tasks
by: AlDahoul, Nouar, et al.
Published: (2025)

Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
by: Ludziejewski, Jan, et al.
Published: (2025)

Language Control Diffusion: Efficiently Scaling through Space, Time, and Tasks
by: Zhang, Edwin, et al.
Published: (2022)

Batched Contextual Reinforcement: A Task-Scaling Law for Efficient Reasoning
by: Yang, Bangji, et al.
Published: (2026)

Domain-Adapted Small Language Models for Reliable Clinical Triage
by: Aljohani, Manar, et al.
Published: (2026)

Fantastic Bugs and Where to Find Them in AI Benchmarks
by: Truong, Sang, et al.
Published: (2025)