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ARXIV:2603.16654 · NLP EVALUATION · SUBMITTED 19 MAR · 20:22 UTC · FRESHNESS STALE
ARXIV:2603.16654NLP EVALUATIONSUBMITTED 19 MAR · 20:22 UTCFRESHNESS STALEarXiv
Omanic provides a structured approach to evaluate multi-hop reasoning in large language models through detailed annotations and a challenging benchmark.
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Pain Omanic provides a structured approach to evaluate multi-hop reasoning in large language models through detailed annotations and a challenging benchmark.
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Omanic provides a structured approach to evaluate multi-hop reasoning in large language models through detailed annotations and a challenging benchmark. To address this gap, we propose Omanic, an open-domain multi-hop QA resource that provides…
Reasoning-focused large language models (LLMs) have advanced in many NLP tasks, yet their evaluation remains challenging: final answers alone do not expose the intermediate reasoning steps, making it difficult to determine whether a model…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Systematic evaluations show that state-of-the-art LLMs achieve only 73.11% multiple-choice accuracy on OmanicBench, confirming its high difficulty. A public repository is linked, so build…
NLP Evaluation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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Omanic provides a structured approach to evaluate multi-hop reasoning in large language models through detailed annotations and a challenging benchmark.
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10.48550/arXiv.2603.16654Omanic provides a structured approach to evaluate multi-hop reasoning in large language models through detailed annotations and a challenging benchmark.
Abstract
Reasoning-focused large language models (LLMs) have advanced in many NLP tasks, yet their evaluation remains challenging: final answers alone do not expose the intermediate reasoning steps, making it difficult to determine whether a model truly reasons correctly and where failures occur, while existing multi-hop QA benchmarks lack step-level annotations for diagnosing reasoning failures. To address this gap, we propose Omanic, an open-domain multi-hop QA resource that provides decomposed sub-questions and intermediate answers as structural annotations for analyzing reasoning processes. It contains 10,296 machine-generated training examples (OmanicSynth) and 967 expert-reviewed human-annotated evaluation examples (OmanicBench). Systematic evaluations show that state-of-the-art LLMs achieve only 73.11% multiple-choice accuracy on OmanicBench, confirming its high difficulty. Stepwise analysis reveals that CoT's performance hinges on factual completeness, with its gains diminishing under knowledge gaps and errors amplifying in later hops. Additionally, supervised fine-tuning on OmanicSynth brings substantial transfer gains (7.41 average points) across six reasoning and math benchmarks, validating the dataset's quality and further supporting the effectiveness of OmanicSynth as supervision for reasoning-capability transfer. We release the data at https://huggingface.co/datasets/li-lab/Omanic and the code at https://github.com/XiaojieGu/Omanic.
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PROBLEM
Omanic provides a structured approach to evaluate multi-hop reasoning in large language models through detailed annotations and a challenging benchmark. To address this gap, we propose Omanic, an open-domain multi-hop QA resource that provides decomposed sub-questions and interm...
METHOD
Reasoning-focused large language models (LLMs) have advanced in many NLP tasks, yet their evaluation remains challenging: final answers alone do not expose the intermediate reasoning steps, making it difficult to determine whether a model truly reasons correctly and where failur...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Systematic evaluations show that state-of-the-art LLMs achieve only 73.11% multiple-choice accuracy on OmanicBench, confirming its high difficulty. A public repository is linked, so build verification can...
WHY NOW
NLP Evaluation moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
To address this gap, we propose Omanic, an open-domain multi-hop QA resource that provides decomposed sub-questions and intermediate answers as structural annotations for analyzing reasoning processes.
Explicitly stated in the abstract as the core contribution of the paper
partial
Systematic evaluations show that state-of-the-art LLMs achieve only 73.11% multiple-choice accuracy on OmanicBench, confirming its high difficulty.
Direct numeric result stated in the abstract with specific percentage
partial
Additionally, supervised fine-tuning on OmanicSynth brings substantial transfer gains (7.41 average points) across six reasoning and math benchmarks.
Direct numeric result stated in the abstract with specific improvement metric
partial
Stepwise analysis reveals that CoT's performance hinges on factual completeness, with its gains diminishing under knowledge gaps and errors amplifying in later hops.
Directly stated in abstract as a key finding from stepwise analysis
partial
It contains 10,296 machine-generated training examples (OmanicSynth) and 967 expert-reviewed human-annotated evaluation examples (OmanicBench).
Explicit numeric counts provided in the abstract
partial
Risk 3: Human-annotated evaluation set is small (967 examples), which could lead to overfitting or biased benchmarks.
Explicitly stated as a caveat in the analysis section
partial
Risk 2: Step-wise evaluation adds computational overhead, potentially slowing down real-time applications.
Explicitly stated as a caveat in the analysis section
partial
Risk 1: The dataset's synthetic training examples may not generalize to all real-world domains, limiting effectiveness in niche industries.
Explicitly stated as a caveat in the analysis section
partial
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Omanic provides a structured approach to evaluate multi-hop reasoning in large language models through detailed annotations and a challenging benchmark.
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