A Tree-based RAG Framework for Evidence-Intensive QA via Adaptive Planning and Topology-Aware Evidence Gathering
APT-RAG framework excels in evidence-intensive QA, achieving a 40.69% F1 score improvement.
Key Findings
Methodology
APT-RAG addresses evidence-intensive QA through adaptive planning and topology-aware evidence gathering. It dynamically expands the reasoning structure, leveraging sibling evidence reuse, direct retrieval, and child node evidence aggregation to enhance coverage. The framework also introduces evidence-guided batched answer generation to reduce overhead.
Key Results
- APT-RAG achieved a 40.69% F1 score on the MoNaCo benchmark, significantly outperforming existing methods.
- On the QAMPARI benchmark, APT-RAG achieved a 23.28% F1 score, outperforming other structured RAG methods.
- Experiments show APT-RAG can adaptively expand its reasoning tree and retrieve more supporting evidence in evidence-intensive QA.
Significance
The APT-RAG framework holds significant value for academia and industry. It addresses the structural rigidity and topology-ignorant issues of existing structured RAG methods in evidence-intensive QA, enhancing complex problem-solving and answer generation efficiency.
Technical Contribution
APT-RAG overcomes limitations of existing methods by introducing adaptive planning and topology-aware evidence gathering. It provides new theoretical guarantees and engineering possibilities, especially in handling large-scale evidence sets.
Novelty
APT-RAG is the first to combine adaptive planning with topology-aware evidence gathering for evidence-intensive QA. Compared to existing methods, it dynamically adjusts reasoning depth and breadth, significantly improving evidence coverage.
Limitations
- APT-RAG may face computational overhead when handling extremely large document sets.
- The complexity of the framework may increase the difficulty of implementation and debugging.
Future Work
Future research could explore APT-RAG's application in other domains, such as legal and medical complex problem-solving. Additionally, further optimization of its computational efficiency is possible.
AI Executive Summary
In recent years, structured RAG methods have improved multi-hop QA performance through tree or graph structures. However, these methods face challenges in evidence-intensive QA due to structural rigidity and topology ignorance. To address these issues, researchers propose the APT-RAG framework. APT-RAG dynamically expands the reasoning structure through adaptive planning and improves evidence coverage through topology-aware evidence gathering. Experimental results show that APT-RAG outperforms existing methods on MoNaCo and QAMPARI benchmarks, demonstrating its potential in complex problem-solving.
The core technologies of APT-RAG include adaptive planning and topology-aware evidence gathering. Adaptive planning allows the framework to dynamically adjust the reasoning structure based on question dependencies and evidence requirements, while topology-aware evidence gathering improves evidence coverage through sibling evidence reuse, direct retrieval, and child node evidence aggregation. Additionally, evidence-guided batched answer generation significantly reduces overhead.
APT-RAG's success lies in its ability to adaptively expand the reasoning tree and retrieve more supporting evidence in evidence-intensive QA. This framework not only holds significant value for academia but also provides new solutions for industry, especially in handling large-scale evidence sets. Future research could explore its application in other domains and further optimize its computational efficiency.
Deep Analysis
Background
In recent years, the multi-hop QA field has made significant progress, especially driven by structured RAG methods. Representative works include methods like Plan*RAG and ToQ, which effectively organize the reasoning process through tree or graph structures. However, as problem complexity increases, existing methods face challenges in handling evidence-intensive QA, particularly when integrating evidence scattered across multiple documents.
Core Problem
The core problem of evidence-intensive QA is how to effectively integrate evidence scattered across numerous documents. The structural rigidity of existing methods limits the dynamic expansion of reasoning structures, while topology-ignorant evidence gathering hinders effective integration of evidence across different reasoning nodes. These bottlenecks severely impact the ability to solve complex problems.
Innovation
The core innovations of APT-RAG lie in its adaptive planning and topology-aware evidence gathering. Adaptive planning allows the framework to dynamically adjust the reasoning structure based on question dependencies, while topology-aware evidence gathering improves evidence coverage through sibling evidence reuse, direct retrieval, and child node evidence aggregation. These innovations significantly enhance the performance of evidence-intensive QA.
Methodology
- �� Adaptive Planning: Dynamically expands the reasoning structure based on question dependencies.
- �� Topology-aware Evidence Gathering: Improves evidence coverage through sibling evidence reuse, direct retrieval, and child node evidence aggregation.
- �� Evidence-guided Batched Answer Generation: Reduces overhead and improves efficiency.
Experiments
Experiments were conducted on MoNaCo and QAMPARI benchmarks, using Qwen3-Embedding-0.6B as the embedding model to retrieve the top 20 documents. Comparisons were made with existing structured RAG methods, with evaluation metrics including answer precision, recall, and F1 score.
Results
APT-RAG achieved a 40.69% F1 score on the MoNaCo benchmark, significantly outperforming existing methods. On the QAMPARI benchmark, APT-RAG achieved a 23.28% F1 score, demonstrating its advantages in evidence-intensive QA.
Applications
APT-RAG can be applied in fields requiring the integration of large amounts of dispersed evidence, such as complex problem-solving in legal and medical domains. Its adaptive planning and topology-aware evidence gathering capabilities make it widely applicable in these areas.
Limitations & Outlook
APT-RAG may face computational overhead when handling extremely large document sets. Additionally, the complexity of the framework may increase the difficulty of implementation and debugging. Future research could explore further optimization of its computational efficiency.
Plain Language Accessible to non-experts
Imagine a librarian needing to answer a complex question about a topic. Traditional methods are like the librarian only searching one shelf, while APT-RAG is like the librarian searching the entire library and dynamically adjusting the search strategy based on the topic. This approach not only increases the probability of finding the correct answer but also reduces unnecessary repeated searches.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to find clues. Traditional methods are like you're only allowed to search one room, while APT-RAG is like you can search the entire house and adjust your search strategy based on the game's progress. This makes it easier to find the right clues and win the game!
Glossary
RAG (Retrieval-Augmented Generation)
A QA method combining information retrieval and generation.
Used for multi-hop reasoning and evidence integration.
Adaptive Planning
A strategy to dynamically adjust the reasoning structure based on questions.
Used in APT-RAG to expand the reasoning tree.
Topology-aware Evidence Gathering
Improves evidence coverage through sibling and child node evidence reuse.
Used in APT-RAG for evidence integration.
Evidence-intensive QA
A QA task requiring the integration of large amounts of dispersed evidence.
Main application scenario for APT-RAG.
Batched Answer Generation
A method to reduce computational overhead by generating multiple answers simultaneously.
Used in APT-RAG to improve generation efficiency.
Open Questions Unanswered questions from this research
- 1 How can APT-RAG's computational efficiency be further optimized to handle larger document sets?
- 2 What is the potential application of APT-RAG in other fields, such as finance and education?
Applications
Immediate Applications
Legal Problem Solving
APT-RAG can be used in the legal field for complex problem-solving, integrating large legal documents to provide accurate answers.
Long-term Vision
Medical Diagnosis Support
APT-RAG has the potential to be used in the medical field, helping doctors integrate large medical literature to support diagnostic decisions.
Abstract
Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. However, they face key limitations in evidence-intensive QA, where answering a question requires synthesizing information scattered across dozens or even hundreds of documents: structural rigidity, which limits adaptive reasoning expansion, and topology-ignorant evidence gathering, which prevents effective integration of evidence across different reasoning nodes. To address these issues, we propose APT-RAG, an Adaptive Planning and Topology-aware evidence gathering RAG framework. Adaptive planning dynamically expands the reasoning structure based on question dependencies and evidence requirements, while topology-aware evidence gathering improves evidence coverage through sibling evidence reuse, direct retrieval, and evidence aggregation from child nodes. We further introduce evidence-guided batched answer generation to reduce significant generation overhead in evidence-intensive QA. In the experiments on evidence-intensive QA benchmarks, APT-RAG outperforms existing structured RAG methods. Our code is available at https://github.com/hyudsl/APT-RAG.