Scaffold Effects on GAIA: A Controlled Comparison

2026-06-07 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A controlled study investigated how scaffold choice impacts Large Language Model (LLM) performance on the GAIA validation benchmark, using Levels 1 and 2. The research compared three scaffolds—ReAct, a Planner-Actor-Rater multi-agent design, and planner-then-executor—across five models: Claude Opus 4.7, Sonnet 4.6, Haiku 4.5; Gemini 3.1 Pro Preview; and GPT-5.5. Findings reveal scaffold choice can alter measured accuracy by up to 28 percentage points within a single model (Opus, Level 2), confirming significant elicitation gaps. Contrary to predictions, more capable models were not less scaffold-sensitive. The multi-agent advantage was specific to the Anthropic family at Level 2, not cross-provider models. Structured scaffolds made fewer tool calls but recovered more errors, and Gemini with planner-then-executor proved cheapest and most accurate at Level 2.

Key takeaway

For AI Scientists evaluating LLM capabilities, you must recognize that reported scores are scaffold-conditional estimates. Systematically testing multiple elicitation scaffolds, like Planner-Actor-Rater or planner-then-executor, is crucial to accurately assess a model's true potential on complex benchmarks like GAIA. Do not assume model improvements automatically shrink the elicitation gap; instead, invest in scaffold optimization.

Key insights

LLM performance on complex tasks is heavily scaffold-dependent, with elicitation gaps varying significantly by model and task difficulty.

Principles

• Scaffold choice can alter measured accuracy by up to 28 percentage points.
• More capable models are not necessarily less scaffold-sensitive.
• Model family, not capability tier, conditions multi-agent advantage.

Method

Controlled comparison of ReAct, Planner-Actor-Rater, and planner-then-executor scaffolds across five models on GAIA Levels 1 and 2, with three attempts per question.

In practice

• Structured scaffolds reduce tool calls but improve error recovery.
• Gemini with planner-then-executor offers cost-effective high accuracy.

Topics

• LLM Scaffolding
• GAIA Benchmark
• Model Elicitation
• ReAct
• Multi-Agent Systems
• Performance Evaluation

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.