aiqbench analysis
This research introduces SkillDelta, a framework that predicts whether specific agent skills will improve task performance before execution, addressing the common issue where skills incur costs without delivering benefits. By leveraging paired historical executions to estimate task-conditional gains, the method achieves an average 4.3% improvement in success rates across multiple benchmarks and agents. The work provides a practical approach for optimizing resource allocation in autonomous agents by avoiding unnecessary skill activation that could degrade performance or waste tokens.
When Does a Skill Add Value? Task-Conditional Gain Prediction for Selective Skill Use
arXiv:2609.32274v1 Announce Type: new Abstract: Agent skills are expected to improve task performance. Yet we find that they often provide no benefit, and can even hurt performance while incurring additional token costs. Can we predict whether a skill will help before the agent acts? We introduce SkillDelta, a framework for predicting task-conditional skill gains from paired executions of the same agent with and without the skill. A local predictor transfers these historical gains to new tasks without retraining the agent. Under explicit transfer assumptions, our analysis links support coverage, representation mismatch, and execution noise to prediction error and decision regret. Across five benchmarks and three target agents, paired history improves observed-gain ranking over skill-assisted outcomes alone in 12 of 15 settings. At matched expected skill-use rates, SkillDelta improves success over random activation in all 15 settings, with an average absolute gain of 4.3%. Most of this advantage comes from allocation across task groups. Evidence for additional within-group selection value is strongest on ToolQA and weaker elsewhere. Code is available at https://github.com/TankTechnology/skilldelta.
Title:When Does a Skill Add Value? Task-Conditional Gain Prediction for Selective Skill Use
Abstract:Agent skills are expected to improve task performance. Yet we find that they often provide no benefit, and can even hurt performance while incurring additional token costs. Can we predict whether a skill will help before the agent acts? We introduce SkillDelta, a framework for predicting task-conditional skill gains from paired executions of the same agent with and without the skill. A local predictor transfers these historical gains to new tasks without retraining the agent. Under explicit transfer assumptions, our analysis links support coverage, representation mismatch, and execution noise to prediction error and decision regret. Across five benchmarks and three target agents, paired history improves observed-gain ranking over skill-assisted outcomes alone in 12 of 15 settings. At matched expected skill-use rates, SkillDelta improves success over random activation in all 15 settings, with an average absolute gain of 4.3%. Most of this advantage comes from allocation across task groups. Evidence for additional within-group selection value is strongest on ToolQA and weaker elsewhere. Code is available at this https URL.
22 pages, 7 figures. Code: this https URL
Artificial Intelligence (cs.AI)
arXiv:2609.32274 [cs.AI]
(or arXiv:2609.32274v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.32274 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 283 words
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