ConRAG: Consensus-Driven Evidence Flow for Multi-Hop Question Answering

arXiv:2605.28093 · cs.CL · Submitted 2026-05-27 · Read on arXiv

Listen

Radio episode about this paper

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: Today's paper: "ConRAG: Consensus-Driven Evidence Flow for Multi-Hop Question Answering".

Jane: The gist The ConRAG framework proposes a consensus-driven multi-view retrieval system that jointly optimizes both query and corpus sides to enhance large language models for complex multi-hop question answering.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So we're looking at this paper called "ConRAG: Consensus-Driven Evidence Flow for Multi-Hop Question Answering". It sounds like they are trying to fix the problem where standard retrieval methods fail on multi-hop questions because they only look at one round of searching.

Jane: Right, so when you ask a really complex question that needs info from several different places in a document, the old systems just pull one chunk and maybe that's not enough for the whole answer.

Lu: Exactly. The authors are proposing this ConRAG framework which aims to optimize both how the model asks questions and how it searches through all the documents at once instead of doing them separately.

Meng: So, like they’re trying to make sure that when we look for evidence, we’re looking at all the right kinds of things—not just plain text chunks.

Lalam: I see the title suggests a consensus-driven approach which implies they are trying to find a common ground among different types of retrieval signals.

Tom: Yeah, and they focus on using these multiple views of evidence—relation, entity, and text signals—to get a better result for those complex multi-hop questions.

The paper's summary: Jane: So what’s the main idea here? Basically, ConRAG tries to combine the best parts of two existing research directions: reasoning-based methods on the query side and graph-based methods on the corpus side.

Tom: That's it. They acknowledge that reasoning-based methods can decompose a big question into smaller steps, but they get tripped up by poor retrieval quality over text chunks.

Lu: And graph-based methods are good at finding related evidence, but the signals they get from entities and relations are all different sizes and shapes, which makes it hard to compare them effectively.

Meng: So ConRAG is trying to solve both problems simultaneously by making a unified way to score all these different pieces of information.

Lalam: The paper introduces three main components: Connection, Constraint, and Consensus, which are the core ideas behind this consensus-driven multi-view RAG framework.

Tom: Let’s break down what those mean for us so we can see how it actually works in practice.

The paper's improvements: Jane: Okay, let's talk about the actual mechanics of ConRAG. They focus on Connection, Constraint, and Consensus as the core ideas to improve performance on multi-hop QA tasks.

Tom: Connection is about building this evidence-grounded knowledge graph where every object in that graph keeps a link back to its original source text unit.

Lu: That’s important because it means the structural signals they get from the graph actually trace back to verifiable text evidence, so you're not just getting abstract graph connections.

Meng: But then they have Consensus which is where they try to unify those different retrieval signals—the relation view, entity-anchor view, and text-evidence view—into one shared space.

Lalam: They project these heterogeneous signals into a unified evidence-unit space and use a scoring function that combines the normalized scores from all three views using fusion weights.

Tom: And they add a consensus bonus which boosts the ranking score when an evidence unit gets support from multiple views, which should help filter out weaker, less supported pieces of information.

Jane: Then there's Constraint, which is on the query side to make sure the model plans its steps better by generating dependency-aware sub-question plans and creating bound queries.

Conclusion: Tom: So, we’ve seen that ConRAG systematically optimizes both the query planning and the corpus evidence utilization by aligning those multi-view retrieval signals in a unified ranking space to get more accurate results.

Jane: The results on benchmarks like HotpotQA and 2WikiMultiHopQA show that this approach consistently beats all previous methods by a margin of up to twenty-six point nine percent average performance gain over vanilla RAG.

Lu: Especially on the MuSiQue benchmark, ConRAG allows Gemma-four-31B to achieve new state-of-the-art results, which is pretty significant for testing complex reasoning capabilities.

Meng: It’s good that they show efficiency too; they got an average query time of five point six four seconds on 2WikiMultiHopQA when using GPT-4o-mini, so it seems like it doesn't just get better accuracy but also stays reasonably fast to run at scale.

Lalam: The paper notes a limitation, though, which is that the evidence-grounded knowledge graph construction can be affected by errors made during the information extraction process itself.

Tom: And they also mention that the slot-bound execution strategy depends on how good those intermediate answers are; if you fill in a slot incorrectly, it can mess up the next retrieval step.

Jane: So, while this is a strong method for multi-hop QA, it’s important to remember that the quality of their initial knowledge graph and the success of those sequential steps really matter.

Lu: And I think we need to keep an eye on how they handle these dependencies when we try to apply this kind of framework to much longer, more complex reasoning chains.

Yikai Zhu, Kunfeng Chen, Qihuang Zhong, Juhua Liu, Bo Du

Wuhan University

cs.CL

Submitted: 2026-05-27

Updated: 2026-10-14

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 91/100

The gist: The gist The ConRAG framework proposes a consensus-driven multi-view retrieval system that jointly optimizes both query and corpus sides to enhance large language models for complex multi-hop

Key concepts

Consensus-Driven Multi-View Ranking
This technique creates a single ranking score for evidence by combining three different retrieval signals: one based on relations, one on entity anchors, and one directly from the text. A 'consensus bonus' further boosts the score if an evidence unit is supported by multiple views, ensuring that relevant information is selected even if it appears in only one view.
Slot-Based Execution Mechanism
This component manages how the model reasons step-by-step. It involves creating a dependency-aware plan for subtasks and using a 'slot binding map' to generate specific, bound queries for each step. This ensures that the information gathered in one reasoning stage is correctly passed as input (a slot) to the next dependent reasoning stage.
Evidence-Grounded Knowledge Graph
This is a structure where knowledge objects are linked directly to their source textual evidence. By building this graph, the system can trace any retrieved piece of information back to its original document, which helps in verifying the quality and relevance of the evidence used for complex reasoning tasks.

Terminology

Summary

The gist The ConRAG framework proposes a consensus-driven multi-view retrieval system that jointly optimizes both query and corpus sides to enhance large language models for complex multi-hop question answering.

Problem and Motivation

Retrieval-augmented generation (RAG) methods currently struggle with complex multi-hop question answering because traditional methods typically adopt single-round retrieval, which is insufficient when a user query relies on evidence from multiple documents. Multi-hop RAG research has progressed along two lines of work: Reasoning-based RAG, which focuses on the query side by decomposing complex questions into simpler subtasks, and Graph-based RAG, which focuses on the corpus side by organizing knowledge structures to facilitate cross-passage evidence discovery. However, reasoning-based methods rely on semantic retrieval over document chunks, making them vulnerable to poor retrieval quality that can degrade overall performance. Conversely, graph-based methods often deal with heterogeneous objects with different granularities, such as entities and relations, making it difficult to compare different retrieval signals.

ConRAG Framework Components

ConRAG centers on three complementary designs: Connection, Constraint, and Consensus.

  1. Connection involves constructing an evidence-grounded knowledge graph where each graph object maintains links to its source evidence units. This allows graph-derived signals to be traced back to their source textual evidence.

  2. Consensus introduces a unified ranking space by projecting heterogeneous retrieval signals—relation, entity-anchor, and text-evidence—into a shared evidence-unit space. The system constructs three views: the relation view indexes textual representations of relation edges, the entity-anchor view indexes textual representations of entity nodes, and the text-evidence view directly builds a dense index over evidence units. These views are then mapped back to evidence units to form a unified set Cx. A compact evidence-unit scoring function s(c) is designed by combining the normalized scores from these three views using fusion weights αr, αa, and αt. The consensus bonus b(c) boosts the ranking score when an evidence unit receives support from multiple views.

  3. Constraint employs a lightweight slot-bound execution mechanism to strengthen inter-task dependencies on the query side. This strategy involves generating a dependency-aware sub-question plan and constructing a slot binding map to create bound queries q⋆i.

Evaluation and Results

ConRAG was evaluated on three multi-hop QA benchmarks: HotpotQA, 2WikiMultiHopQA, and MuSiQue. Extensive experiments show that ConRAG consistently outperforms all baselines by a clear margin, achieving up to +26.9% average performance gains over vanilla RAG. Specifically, on the challenging MuSiQue benchmark, ConRAG enables Gemma-4-31B to achieve new state-of-the-art results. The ablation study confirms that removing either consensus fusion or slot binding leads to consistent performance degradation. Furthermore, the sensitivity analysis of fusion weights indicates that the text-evidence view retains a relatively large weight while structural views serve as complementary support for best performance on MuSiQue.

Efficiency and Conclusion

The efficiency analysis shows that ConRAG achieves an average query time of 5.64 seconds on 2WikiMultiHopQA using GPT-4o-mini. This demonstrates that ConRAG not only brings better performance but also improves the inference efficiency. Overall, ConRAG systematically optimizes both the query and corpus sides by aligning multi-view retrieval signals within a unified evidence-unit ranking space to achieve more accurate retrieval. This framework consistently outperforms existing RAG baselines across all benchmarks.

Limitations

Potential limitations include the reliance on offline evidence-grounded knowledge graph construction, which may be affected by information extraction errors. Additionally, slot-bound execution depends on the quality of intermediate answers; incorrect slot filling can mislead subsequent retrieval steps. The work is also limited by evaluating only three English multi-hop QA benchmarks.

Ethics and Reproducibility

The paper adheres to the ACL Ethics Policy, stating that the constructed evidence-grounded knowledge graph is used only for retrieval and ranking. The authors commit to publicly releasing their code at https://github.com/yikai-zhu/ConRAG to facilitate reproducibility.

How it works

Improvements for AI systems

  1. Retrieval-augmented generation (RAG) can be enhanced by implementing ConRAG, which jointly optimizes both the query and corpus sides to enhance multi-hop QA performance of LLMs in RAG scenarios. This allows the system to move beyond single-round retrieval by systematically optimizing both query planning and corpus evidence utilization.

  2. The system can perform multi-round reasoning through a slot-bound execution strategy where the bound query is then obtained by: q⋆i = Bind(qi, Θi), ensuring that subsequent retrieval queries must be informed by intermediate answers obtained in earlier steps. This enables the model to manage complex dependencies and maintain coherence across multiple reasoning steps.

  3. The system can leverage heterogeneous evidence by projecting signals into a unified space for consensus, as described by Consensus fuses multi-view retrieval signals within a unified evidence-unit space and applying a multi-view consensus bonus strategy to jointly weight and fuse the heterogeneous evidence within this space. This mechanism ensures that retrieved context is selected based on which pieces of evidence achieve consensus across views.

  4. The system can construct an evidence-grounded knowledge graph where graph objects are not directly used as generation context. Instead, each graph object maintains links to its source evidence units, serving as a retrieval interface that bridges structural signals and evidence units. This grounds the corpus structure in verifiable text rather than using it as a raw context container.

  5. The system can generate structured reasoning plans through question decomposition where the output is a JSON object containing an acquired information set and a plan of steps, such as Plan only for missing facts or facts that need verification needed to answer question. This enables the LLM to break down complex questions into manageable, dependency-aware sub-tasks.

Sources

Related papers