arXiv:2606.08529v1 Announce Type: cross Abstract: Published agent capability scores conflate what a model can do with what its scaffold lets it do, and the magnitude of this elicitation gap is not well characterized under controlled conditions. This study executes a pre-registered controlled comparison of three scaffolds (ReAct, a Planner-Actor-Rater multi-agent design, and planner-then-executor) across five models from three providers (Claude Opus 4.7, Sonnet 4.6, Haiku 4.5; Gemini 3.1 Pro Preview; GPT-5.5) on GAIA validation Levels 1 and 2, holding tasks and conditions fixed, with three atte
The rapid advancement and proliferation of large language models necessitate a more rigorous understanding of their true capabilities versus the influence of prompt engineering and scaffolding.
This study directly addresses the 'elicitation gap' in AI agent performance, which is crucial for objectively evaluating and comparing AI models and designing effective agentic systems.
A clearer, quantitatively established understanding of how different scaffold designs impact AI model performance will emerge, leading to more data-driven agentic system development.
- · AI platform providers
- · AI researchers
- · AI agent developers
- · Enterprises deploying AI
- · Poorly designed agentic systems
- · Developers relying solely on model scores
Improved methodologies for evaluating and comparing AI model capabilities become standard.
Accelerated development of more robust and reliable AI agent architectures.
Enhanced trust and broader adoption of AI agents in critical applications due to more predictable performance.
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Read at arXiv cs.LG