arXiv:2603.16654v2 Announce Type: replace-cross Abstract: Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, especially in multi-hop QA benchmarks without step-level annotations. To address this gap, we introduce Omanic, an open-domain 4-hop QA benchmark designed not only to measure final-answer accuracy but also to diagnose where reasoning breaks down. Omanic contains 10,296 machine-generated training examples (OmanicSynth) and 967 expert-reviewed human-annotated evaluation examples (OmanicBench), with each
The rapid advancement and deployment of large language models necessitate more granular and diagnostic evaluation methods to understand their capabilities and limitations beyond mere final answer accuracy.
A strategic reader should care because improving LLM evaluation, particularly in multi-hop reasoning, directly impacts the reliability, safety, and ultimately the utility of AI systems for complex tasks.
The introduction of benchmarks like Omanic shifts LLM evaluation from solely measuring correctness to also diagnosing the 'why' and 'where' reasoning failures occur, leading to more targeted model improvements.
- · AI researchers
- · LLM developers
- · AI ethics and safety organizations
- · LLMs with poor diagnostic capabilities
- · Evaluation methods relying only on final answers
This benchmark helps LLM developers improve the multi-hop reasoning capabilities of their models by pinpointing weaknesses.
More robust and explainable LLMs with stronger reasoning will emerge, increasing their adoption in critical white-collar workflows.
The enhanced diagnostic capacity might accelerate the development of more transparent and trustworthy AI agents, leading to increased automation and efficiency across industries.
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Read at arXiv cs.LG