RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context

TL;DR

RhinoInsight enhances deep research with control mechanisms that improve robustness, traceability, and quality in large language models without requiring parameter updates.

Contribution

It introduces two novel control modules, a Verifiable Checklist and an Evidence Audit, to improve model behavior and context management in deep research tasks.

Findings

Achieves state-of-the-art performance on deep research tasks.

Improves robustness and traceability in language model research.

Reduces hallucinations and context rot.

Abstract

Large language models are evolving from single-turn responders into tool-using agents capable of sustained reasoning and decision-making for deep research. Prevailing systems adopt a linear pipeline of plan to search to write to a report, which suffers from error accumulation and context rot due to the lack of explicit control over both model behavior and context. We introduce RhinoInsight, a deep research framework that adds two control mechanisms to enhance robustness, traceability, and overall quality without parameter updates. First, a Verifiable Checklist module transforms user requirements into traceable and verifiable sub-goals, incorporates human or LLM critics for refinement, and compiles a hierarchical outline to anchor subsequent actions and prevent non-executable planning. Second, an Evidence Audit module structures search content, iteratively updates the outline, and prunes noisy context, while a critic ranks and binds high-quality evidence to drafted content to ensure verifiability and reduce hallucinations. Our experiments demonstrate that RhinoInsight achieves state-of-the-art performance on deep research tasks while remaining competitive on deep search tasks.