# ARISE: Agentic Rubric-Guided Iterative Survey Engine for Automated Scholarly Paper Generation

arXiv: https://arxiv.org/abs/2511.17689 · HTML: https://mlsanity.com/paper/2511.17689/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 39 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 25 of 25)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Evaluating Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2310.19736/) (2023)
- [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [AutoSurvey: Large Language Models Can Automatically Write Surveys](https://mlsanity.com/paper/2406.10252/) (2024)
- [CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists](https://mlsanity.com/paper/2403.18771/) (2024)
- [A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://mlsanity.com/paper/2407.04069/) (2024)
- [SurveyForge: On the Outline Heuristics, Memory-Driven Generation, and Multi-dimensional Evaluation for Automated Survey Writing](https://mlsanity.com/paper/2503.04629/) (2025)
- [X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs](https://mlsanity.com/paper/2505.16997/) (2025)
- [100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models](https://mlsanity.com/paper/2505.00551/) (2025)
- [SurveyX: Academic Survey Automation via Large Language Models](https://mlsanity.com/paper/2502.14776/) (2025)
- [MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem](https://mlsanity.com/paper/2505.14148/) (2025)
- [Automated Grading and Feedback Tools for Programming Education: A Systematic Review](https://mlsanity.com/paper/2306.11722/) (2023)
- [Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications](https://mlsanity.com/paper/2505.00049/) (2025)
- [Automated Review Generation Method Based on Large Language Models](https://mlsanity.com/paper/2407.20906/) (2024)
- [A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities](https://mlsanity.com/paper/2505.00525/) (2025)
- [Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review](https://mlsanity.com/paper/2505.00210/) (2025)
- [Understanding the Impact of Artificial Intelligence in Academic Writing: Metadata to the Rescue](https://mlsanity.com/paper/2502.16713/) (2025)

## Built on by (most cited first, 3 of 3)

- [How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling](https://mlsanity.com/paper/2604.04791/) (2026)
- [Deep Academic Survey: Stateful Agentic Closed-Loop Paradigm for Academic Survey Automation](https://mlsanity.com/paper/2608.18034/) (2026)
- [Paper Pilot: A Human-in-the-Loop Expert System for Evidence-Traceable Scientific Manuscript Generation in Applied Sciences](https://mlsanity.com/paper/2608.28596/) (2026)

## Abstract (arXiv)

The rapid expansion of scholarly literature presents significant challenges in synthesizing comprehensive, high-quality academic surveys. Recent advancements in agentic systems offer considerable promise for automating tasks that traditionally require human expertise, including literature review, synthesis, and iterative refinement. However, existing automated survey-generation solutions often suffer from inadequate quality control, poor formatting, and limited adaptability to iterative feedback, which are core elements intrinsic to scholarly writing. To address these limitations, we introduce ARISE, an Agentic Rubric-guided Iterative Survey Engine designed for automated generation and continuous refinement of academic survey papers. ARISE employs a modular architecture composed of specialized large language model agents, each mirroring distinct scholarly roles such as topic expansion, citation curation, literature summarization, manuscript drafting, and peer-review-based evaluation. Central to ARISE is a rubric-guided iterative refinement loop in which multiple reviewer agents independently assess manuscript drafts using a structured, behaviorally anchored rubric, systematically enhancing the content through synthesized feedback. Evaluating ARISE against state-of-the-art automated systems and recent human-written surveys, our experimental results demonstrate superior performance, achieving an average rubric-aligned quality score of 92.48. ARISE consistently surpasses baseline methods across metrics of comprehensiveness, accuracy, formatting, and overall scholarly rigor. All code, evaluation rubrics, and generated outputs are provided openly at https://github.com/ziwang11112/ARISE