# Structure-Guided Memory Consolidation for Mitigating Compounding Errors in Literature Review Generation

arXiv: https://arxiv.org/abs/2508.04306 · HTML: https://mlsanity.com/paper/2508.04306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety](https://mlsanity.com/paper/2510.10994/) (2025)

## Abstract (arXiv)

Compounding errors pose a significant challenge in automatic literature review generation, as inaccuracies can cascade across multi-stage retrieval and generation workflows. Existing self-correction strategies often lack mechanisms to effectively track and consolidate verified information throughout the process, making it difficult to prevent error accumulation and propagation. In this paper, we propose Structure-Guided Memory Consolidation (SGMC), a novel framework that incrementally consolidates and verifies information using structured representations at each stage of the literature review pipeline. SGMC consists of three key modules: Tree-Guided Memory for hierarchical literature retrieval and outline generation, Hub-Guided Memory for evidence extraction and iterative content refinement, and Self-Loop Memory for proactive error correction via historical feedback. Extensive experiments on public benchmarks and a newly constructed large-scale dataset demonstrate that SGMC achieves state-of-the-art performance in citation accuracy and content quality, significantly mitigating compounding errors in long-form literature review generation.