Measuring the Microtask Eligibility Gap: When Is an Off-the-Shelf SLM Enough for an Agent Harness?

Author1, Author2, Author3, Author4, Author5

microtaskslanguage modelsbenchmarkingevaluation

2610.00025

Builder Relevance

70%

1h ago

Abstract

This paper investigates the performance of small language models (SLMs) on microtasks in comparison to a baseline, revealing significant eligibility gaps.

Reality Card

Core Claim

Off-the-shelf SLMs fail to meet practitioner-defined thresholds for microtasks, with no configurations passing eligibility tests across multiple models.

Method / Result

0 of 16 configurations passed the eligibility tests for the microtasks evaluated.

Limitations

The findings are sensitive to model size and quantization methods, which may affect reproducibility across different setups.

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