AgenticRAGTracer: A Hop-Aware Benchmark for Diagnosing Multi-Step Retrieval Reasoning in Agentic RAG
arXiv:2602.19127v1 Announce Type: new Abstract: With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop reasoning, which requires models to engage in deliberate thinking and multi-step interaction, serves as a critical testbed for assessing such capabilities. However, existing benchmarks typically provide only final questions and answers, while lacking the intermediate hop-level questions that gradually connect atomic questions to the final multi-hop query. This limitation prevents researchers from analyzing at which step an agent fails and restricts more fine-grained evaluation of model capabilities. Moreover, most current benchmarks are manually constructed, which is both time-consuming and labor-intensive, while also limiting scalability and generalization. To address these challenges, we introduce AgenticRAGTracer, the first Agentic RAG benchmark that is primarily constructed automatically by
arXiv:2602.19127v1 Announce Type: new Abstract: With the rapid advancement of agent-based methods in recent years, Agentic RAG has undoubtedly become an important research direction. Multi-hop reasoning, which requires models to engage in deliberate thinking and multi-step interaction, serves as a critical testbed for assessing such capabilities. However, existing benchmarks typically provide only final questions and answers, while lacking the intermediate hop-level questions that gradually connect atomic questions to the final multi-hop query. This limitation prevents researchers from analyzing at which step an agent fails and restricts more fine-grained evaluation of model capabilities. Moreover, most current benchmarks are manually constructed, which is both time-consuming and labor-intensive, while also limiting scalability and generalization. To address these challenges, we introduce AgenticRAGTracer, the first Agentic RAG benchmark that is primarily constructed automatically by large language models and designed to support step-by-step validation. Our benchmark spans multiple domains, contains 1,305 data points, and has no overlap with existing mainstream benchmarks. Extensive experiments demonstrate that even the best large language models perform poorly on our dataset. For instance, GPT-5 attains merely 22.6\% EM accuracy on the hardest portion of our dataset. Hop-aware diagnosis reveals that failures are primarily driven by distorted reasoning chains -- either collapsing prematurely or wandering into over-extension. This highlights a critical inability to allocate steps consistent with the task's logical structure, providing a diagnostic dimension missing in traditional evaluations. We believe our work will facilitate research in Agentic RAG and inspire further meaningful progress in this area. Our code and data are available at https://github.com/YqjMartin/AgenticRAGTracer.
Executive Summary
The article introduces AgenticRAGTracer, a novel benchmark designed to evaluate multi-step retrieval reasoning in Agentic RAG systems. Unlike existing benchmarks, AgenticRAGTracer provides intermediate hop-level questions, enabling detailed analysis of model performance at each step. Constructed primarily through large language models, it spans multiple domains and contains 1,305 data points, with no overlap with current benchmarks. The study reveals that even advanced models like GPT-5 perform poorly on the hardest portions of the dataset, highlighting issues with reasoning chain integrity. The benchmark aims to facilitate research in Agentic RAG and inspire further advancements in this field.
Key Points
- ▸ AgenticRAGTracer is the first benchmark to support step-by-step validation in Agentic RAG.
- ▸ The benchmark is primarily constructed automatically by large language models, enhancing scalability and generalization.
- ▸ Experiments show that even advanced models like GPT-5 perform poorly on the hardest portions of the dataset.
- ▸ Failures in multi-hop reasoning are primarily driven by distorted reasoning chains, either collapsing prematurely or wandering into over-extension.
- ▸ The benchmark spans multiple domains and contains 1,305 data points, with no overlap with existing benchmarks.
Merits
Innovative Benchmark Design
AgenticRAGTracer introduces a novel approach to evaluating multi-step retrieval reasoning by providing intermediate hop-level questions, which allows for a more granular analysis of model performance.
Automated Construction
The benchmark is primarily constructed using large language models, which enhances scalability and reduces the time and labor required for manual construction.
Comprehensive Coverage
The dataset spans multiple domains and contains 1,305 data points, ensuring a broad and diverse evaluation of model capabilities.
Demerits
Limited Generalizability
While the automated construction method enhances scalability, it may also introduce biases or limitations inherent in the large language models used for construction.
Performance Metrics
The study highlights the poor performance of advanced models on the hardest portions of the dataset, but it does not provide detailed insights into specific areas of failure beyond reasoning chain integrity.
Expert Commentary
The introduction of AgenticRAGTracer represents a significant advancement in the evaluation of multi-step retrieval reasoning in Agentic RAG systems. By providing intermediate hop-level questions, the benchmark enables a more detailed analysis of model performance, which is crucial for identifying specific areas of failure. The automated construction method enhances scalability and reduces the time and labor required for manual construction, making it a valuable tool for researchers and developers. However, the study also highlights the poor performance of advanced models like GPT-5 on the hardest portions of the dataset, indicating a critical inability to allocate steps consistent with the task's logical structure. This finding underscores the need for further research into multi-hop reasoning algorithms and the development of more robust evaluation benchmarks. The benchmark's comprehensive coverage across multiple domains and its lack of overlap with existing benchmarks ensure a broad and diverse evaluation of model capabilities. Overall, AgenticRAGTracer is poised to facilitate meaningful progress in the field of Agentic RAG and inspire further advancements in AI research.
Recommendations
- ✓ Researchers should leverage AgenticRAGTracer to evaluate and improve the performance of their Agentic RAG systems, focusing on enhancing multi-hop reasoning capabilities.
- ✓ Future studies should explore the biases and limitations inherent in the use of large language models for benchmark construction, ensuring the reliability and generalizability of the datasets.
