SGR-Bench: Benchmarking Search Agents on State-Gated Retrieval

2026-05-21 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

SGR-Bench is a new benchmark designed to evaluate search agents on "state-gated retrieval" (SGR) tasks, where answer-bearing evidence requires establishing a correct site-specific retrieval state through filters or hierarchies. This benchmark comprises 100 expert-curated tasks across six source families and 12 public data ecosystems, each demanding agents discover appropriate websites and configure their retrieval state to yield structured answers. SGR-Bench facilitates controlled comparisons by pairing constraint-guided and goal-oriented problem formulations. Evaluations of eight CLI-based agentic LLM systems and three commercial search-agent products revealed the strongest system achieved only 66.18% item-level F1, with row-level F1 significantly lower. A manual audit of 156 failed CLI trajectories identified retrieval-scope drift (37.2%) and criterion mismatch (27.6%) as dominant failure modes, far outweighing final answer composition errors (10.3%). The dataset is available at https://huggingface.co/datasets/PKUAIWeb/SGR-BENCH.

Key takeaway

For AI Engineers developing search agents for specialized data retrieval, recognize that current systems significantly underperform on state-gated retrieval tasks, achieving only 66.18% item-level F1. Your development efforts should prioritize robust mechanisms for establishing correct site-specific retrieval states, specifically addressing retrieval-scope drift and criterion mismatch, which are primary failure modes. Utilize SGR-Bench to rigorously evaluate and refine your agent's ability to navigate complex data ecosystems.

Key insights

Search agents significantly underperform on state-gated retrieval due to failures in establishing correct site-specific states.

Principles

• Establishing correct retrieval state is paramount for specialized data access.
• Agent failures often stem from scope drift or criterion mismatch.
• Benchmarking reveals specific weaknesses in agentic LLM systems.

Method

SGR-Bench evaluates search agents on state-gated retrieval using 100 expert-curated tasks, pairing constraint-guided and goal-oriented problem formulations for controlled comparison.

In practice

• Prioritize agent development on site-specific state configuration.
• Implement robust mechanisms to prevent retrieval-scope drift.
• Enhance agent capabilities for precise criterion matching.

Topics

• Search Agents
• State-Gated Retrieval
• LLM Benchmarking
• Retrieval-Scope Drift
• Criterion Mismatch
• Data Ecosystems

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.