Super Research: Answering Highly Complex Questions with Large Language Models through Super Deep and Super Wide Research
arXiv:2603.00582v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated proficiency in Deep Research or Wide Search, their capacity to solve highly complex questions-those requiring long-horizon planning, massive evidence gathering, and synthesis across heterogeneous sources-remains largely unexplored. We introduce Super Research, a task for complex autonomous research tasks that integrates (i) structured decomposition into a research plan, (ii) super wide retrieval for diverse perspectives, and (iii) super deep investigation to resolve uncertainties through iterative queries. To evaluate this capability, we curated a benchmark of 300 expert-written questions across diverse domains, each requiring up to 100+ retrieval steps and 1,000+ web pages to reconcile conflicting evidence. Super Research produces verifiable reports with fine-grained citations and intermediate artifacts (e.g., outlines and tables) to ensure traceable reasoning. Furthermore, we present
arXiv:2603.00582v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated proficiency in Deep Research or Wide Search, their capacity to solve highly complex questions-those requiring long-horizon planning, massive evidence gathering, and synthesis across heterogeneous sources-remains largely unexplored. We introduce Super Research, a task for complex autonomous research tasks that integrates (i) structured decomposition into a research plan, (ii) super wide retrieval for diverse perspectives, and (iii) super deep investigation to resolve uncertainties through iterative queries. To evaluate this capability, we curated a benchmark of 300 expert-written questions across diverse domains, each requiring up to 100+ retrieval steps and 1,000+ web pages to reconcile conflicting evidence. Super Research produces verifiable reports with fine-grained citations and intermediate artifacts (e.g., outlines and tables) to ensure traceable reasoning. Furthermore, we present a graph-anchored auditing protocol that evaluates Super Research along five dimensions: Coverage, Logical Consistency, Report Utility, Objectivity and Citation Health. While super-complex questions may be infrequent in standard applications, Super Research serves as a critical ceiling evaluation and stress test for LLM capabilities. A model's proficiency within Super Research acts as a powerful proxy for its general research competence; success here suggests the robustness necessary to navigate nearly any subordinate research task. Leaderboard is available at: https://cnsdqd-dyb.github.io/Super-Research-Benchmark/
Executive Summary
The article introduces Super Research, a novel task for complex autonomous research that leverages Large Language Models (LLMs) to tackle highly complex questions. It integrates structured decomposition, super wide retrieval, and super deep investigation to produce verifiable reports with fine-grained citations. The authors evaluate Super Research using a benchmark of 300 expert-written questions and propose a graph-anchored auditing protocol to assess its performance. This work serves as a critical ceiling evaluation and stress test for LLM capabilities, providing a powerful proxy for general research competence.
Key Points
- ▸ Introduction of Super Research task for complex autonomous research
- ▸ Integration of structured decomposition, super wide retrieval, and super deep investigation
- ▸ Evaluation using a benchmark of 300 expert-written questions and a graph-anchored auditing protocol
Merits
Comprehensive Evaluation Framework
The proposed graph-anchored auditing protocol provides a comprehensive framework for evaluating Super Research, covering five dimensions: Coverage, Logical Consistency, Report Utility, Objectivity, and Citation Health.
Demerits
Limited Scope of Application
Super-complex questions may be infrequent in standard applications, which may limit the immediate practical impact of Super Research.
Expert Commentary
The introduction of Super Research marks a significant milestone in the development of LLMs, as it pushes the boundaries of their capabilities in tackling complex research tasks. The proposed evaluation framework and benchmark provide a rigorous testing ground for LLMs, enabling researchers to assess their performance and identify areas for improvement. As LLMs continue to evolve, Super Research can serve as a powerful tool for evaluating their research competence and informing their development.
Recommendations
- ✓ Further research is needed to explore the applications of Super Research in various domains and to develop more advanced evaluation frameworks.
- ✓ The development of Super Research should be accompanied by efforts to address potential biases and limitations in LLMs, ensuring that they are fair, transparent, and reliable.