MVSS: A Unified Framework for Multi-View Structured Survey Generation

arXiv 2026 54.2 method

TLDR

MVSS generates multi-view structured surveys with hierarchical trees, comparison tables, and text, outperforming existing methods on 76 CS topics.

Reasoning

The paper introduces a novel structure-first paradigm for survey generation, addressing limitations of linear text methods. Strengths include explicit modeling of hierarchical relations and multi-view alignment; weaknesses include lack of comparison to human-written surveys and potential scalability issues not discussed.

Read-first score

Read-first score 54.2, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 53.

Recency 8%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=evaluation,experiment,metric,result

Topical relevance 42%

44.2

Uses existing LLM keyword relevance scores normalized to 0-100. AI scientist,automated scientific discovery,autonomous research agent,automated research,literature review agent,survey generation,automated experimentation,experiment design agent,AI for scientific research,paper writing agent,research automation,scientific discovery agent

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 29.

Keyword Scores

survey generation

literature review agent

paper writing agent

AI for scientific research

automated research

research automation

automated scientific discovery

AI scientist

scientific discovery agent

autonomous research agent

automated experimentation

experiment design agent

Deep Analysis

Innovations

Multi-view structured survey generation jointly producing citation-grounded hierarchical trees, structured comparison tables, and survey text with alignment.
Structure-first paradigm: hierarchical tree construction, then tree-constrained comparison tables, then joint tree and table constraints for outline and text generation.
Dedicated multi-dimensional evaluation framework assessing structural quality, comparative completeness, and citation fidelity.

Methodology

MVSS framework constructs a hierarchical tree of research topics, generates comparison tables constrained by the tree, and uses both as structural constraints to guide outline and survey text generation. Evaluation is performed on 76 computer science topics using a new framework that measures structural quality, comparative completeness, and citation fidelity.

Key Results

MVSS significantly outperforms existing methods in survey organization and evidence grounding, and achieves performance comparable to expert-written surveys across multiple evaluation metrics.

Resources

Paper