VisibilityOS
notableReviewed Sun Nov 01

Glasseek research introduces auditable deep-search agent evaluation

Pang and colleagues describe Glasseek, an open framework for training and evaluating deep-search agents, including synthetic search trajectories, context-compaction-aware optimization, and benchmark contamination safeguards. Their 9B model reportedly scores 61.6% on BrowseComp in the specified evaluation.

Context

Pang and colleagues describe Glasseek, an open framework for training and evaluating deep-search agents, including synthetic search trajectories, context-compaction-aware optimization, and benchmark contamination safeguards. Their 9B model reportedly scores 61.6% on BrowseComp in the specified evaluation.

What changed

The paper provides an attributable open deep-search training/evaluation pipeline and describes URL-level benchmark-source filtering and execution sandboxing to reduce evaluation leakage.

Why it matters

Agentic search benchmarks can overstate retrieval capability if an agent encounters hidden answer keys or benchmark data while searching. The work highlights why evidence provenance, search trajectories and benchmark leakage must be audited before drawing conclusions about AI discovery.

Visibility OS interpretation

Visibility OS interpretation: evaluation leakage and context-compaction differences can change apparent search-agent performance. These controlled results do not establish commercial AI search ranking, publisher visibility, or citation behavior.

What remains uncertain

How do the reported results reproduce with independent evaluators and changing live web corpora? How sensitive are the gains to tool access, budgets, judging methods and benchmark filters?

Evidence claims

introduces

The October 12, 2026 Glasseek preprint describes open deep-search agent training and evaluation with domain-level URL filtering and sandboxing to reduce benchmark contamination; the authors report 61.6% BrowseComp accuracy for their 9B agent under the stated harness.