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

2026-05-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

SGR-Bench is a new benchmark designed to evaluate search agents on "state-gated retrieval" (SGR) tasks, where answer-bearing evidence on specialized data-retrieval websites is accessible only after configuring site-specific states like filters or views. The benchmark comprises 100 expert-curated tasks across six source families and 12 public data ecosystems. It includes paired constraint-guided and goal-oriented problem formulations. Evaluation of eight CLI-based LLM systems and three commercial search agents on SGR-Bench shows the strongest system achieves only 66.18% item-level F1, with much lower row-level F1. Manual audits of 156 failed CLI trajectories reveal that agents often reach relevant sources but fail to establish the correct retrieval state, with retrieval-scope drift (37.2%) and criterion mismatch (27.6%) being dominant error types.

Key takeaway

For AI Scientists and Machine Learning Engineers developing search agents, this research highlights a critical need to improve "state-gated retrieval" capabilities. Your models must prioritize preserving active filters, scopes, and row identities across dependent retrieval steps, rather than just source discovery or final answer plausibility. Implement training and evaluation setups that explicitly stress retrieval-state preservation, especially for specialized data-retrieval websites with interacting controls, to bridge the significant gap between partial evidence access and correct structured completion.

Key insights

Search agents struggle with state-gated retrieval, failing to maintain site-specific contexts on specialized data platforms.

Principles

• Answer evidence often requires specific site-state configuration.
• Retrieval-state preservation is a key bottleneck for agents.
• Multiple aligned web controls increase task difficulty.

Method

SGR-Bench uses a four-stage data curation pipeline: candidate website curation, task design protocol specification (six requirements), task construction with LLM assistance, and multi-round expert validation.

In practice

• Focus agent training on maintaining retrieval state across steps.
• Design evaluation to verify row anchoring to correct site slices.
• Couple navigation, active filters, and structured extraction.

Topics

• State-Gated Retrieval
• Search Agents
• LLM Benchmarking
• Web Retrieval
• Retrieval-Scope Drift
• Criterion Mismatch
• Information Seeking

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

Related on AIssential

• SGR-Bench: Benchmarking Search Agents on State-Gated Retrieval — Search agents significantly underperform on state-gated retrieval due to failures in establishing correct site-specific states.
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• AgentIR: Reasoning-Aware Retrival for Deep Research Agents — Deep Research agents, unlike human users, generate explicit natural language reasoning before search calls, a rich intent signal that existing retrieval systems overlook.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.