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home / papers / 2026 / SCAN: Structured Capability Assessment and Navigation for LLMs

SCAN: Structured Capability Assessment and Navigation for LLMs

Paper · 2026 · Zongqi Wang, Tianle Gu, Chen Gong et al.

Summary

Most automatic LLM benchmarks try to reproduce leaderboard orderings such as Chatbot Arena. They reveal little about where a particular model is strong or weak. SCAN (Structured Capability Assessment and Navigation) treats evaluation as a way to understand a model rather than to rank it.

  • TaxBuilder mines capability tags from large pools of real user queries and arranges them into a hierarchical tree. It inserts tags recursively, so the LLM only compares a new tag with one tree level at a time.
  • RealMix blends material from several real queries to synthesize new ones, then filters them. This gives even rare long-tail tags enough test items.
  • PC² scores answers pointwise with an LLM judge. Its weighted criteria come from a preliminary comparison step. The authors report higher accuracy than classic judging, without the cost of full pairwise evaluation.
  • Visualization and analysis tools, such as a Failure Mode Explorer, help users browse results.

Applied to 21 LLMs, SCAN exposes large capability gaps that aggregate scores hide. This makes it useful for targeted model training and data planning.

Key points

  • The first released resources are the taxonomy SCAN-T-V0 and the dataset SCAN-D-V0. They span six areas: coding, math, reasoning, knowledge, roleplay and writing. Code is at github.com/liudan193/SCAN.
  • During node insertion, the judging LLM picks one of three outcomes: the tag already exists, it is a sibling, or it is a child. This keeps prompts short and lets the taxonomy grow.
  • Across the 21 evaluated LLMs, GPT-OSS-120B ranks above Deepseek-R1 overall but is weak in writing and roleplay. GPT-OSS-20B ranks below Qwen3-8B overall yet leads in coding.
  • Fine-grained views show uneven skills within a single domain:
    • GPT-OSS models do well in JavaScript but trail in C and Java.
    • In knowledge, one model ranks #1 in computer science and aerospace engineering but #11 in bioengineering. The Failure Mode Explorer flagged this automatically.
  • Limitations: the framework is text-only and does not yet assess safety, honesty, factuality, hallucination or fairness. A multimodal extension, SCAN-Anything, is planned.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Tsinghua UniversityBeijing · China333 works · 87,019 citations🎓Tsinghua Shenzhen International Graduate SchoolShenzhen · China28 works · 3,035 citations🎓Xidian UniversityXi'an · China14 works · 1,357 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Zongqi Wang, Tianle Gu, Chen Gong, Xin Tian, Siqi Bao and Yujiu Yang. 2026. SCAN: Structured Capability Assessment and Navigation for LLMs. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.126

@inproceedings{wang2026scan,
  title = {{SCAN: Structured Capability Assessment and Navigation for LLMs}},
  author = {Zongqi Wang, Tianle Gu, Chen Gong, Xin Tian, Siqi Bao and Yujiu Yang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.126},
  url = {https://doi.org/10.18653/v1/2026.acl-long.126}
}

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