ARISE: Agentic Rubric-Guided Iterative Survey Engine for Automated Scholarly Paper Generation
TLDR
ARISE uses specialized LLM agents and rubric-guided iterative refinement to automatically generate high-quality academic survey papers.
Reasoning
The paper presents a novel modular architecture with role-specific agents and a structured feedback loop, achieving strong quantitative results. However, it focuses narrowly on survey generation rather than broader scientific discovery, and the abstract lacks details on limitations or generalizability.
Read-first score
Read-first score 73.2, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 67.
Field roles
Rank sensitivity
Stability: volatile; rank range: 47.
Keyword Scores
Deep Analysis
Innovations
- Agentic rubric-guided iterative refinement loop with multiple reviewer agents
- Modular architecture of specialized LLM agents mirroring distinct scholarly roles (topic expansion, citation curation, summarization, drafting, peer review)
- Behaviorally anchored rubric for structured multi-agent evaluation
Methodology
ARISE employs a modular agentic system where LLM agents perform distinct scholarly tasks, and a rubric-guided iterative refinement loop with multiple reviewer agents that assess drafts using a structured, behaviorally anchored rubric, synthesizing feedback to improve the manuscript. Evaluation compares ARISE against state-of-the-art automated systems and recent human-written surveys.
Key Results
ARISE achieved an average rubric-aligned quality score of 92.48, consistently outperforming baseline methods in comprehensiveness, accuracy, formatting, and scholarly rigor.