@inproceedings{dong-etal-2026-benchcompass,
title = "{B}ench{C}ompass: From Scores to Signals for Training and Harness Decisions in Payment-Domain {LLM}s",
author = "Dong, Sijie and
Ren, Wei and
Hu, Xuanwei and
Luo, Jiawei and
Wang, Zifan and
Feng, Xiaoyun and
Cai, Hui and
Xue, Lyuxin and
Lu, Peng and
Wu, Wei and
Zhang, Xin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1170/",
pages = "24762--24781",
ISBN = "979-8-89176-462-0",
abstract = "Payment operations are a critical financial infrastructure, but the value of large language models in this domain remains unclear because payment rules change quickly, evidence is fragmented, and decisions depend on transaction state, participant role, region, and payment rail. Existing benchmarks do not isolate whether failures come from missing payment-rule knowledge, poor use of supplied evidence, or brittleness under imperfect harness inputs. We introduce BenchCompass, a payment-domain benchmark whose construction pipeline builds scenario-grounded tasks from typed evidence packs, applies LLM-based quality checks, creates task-input attack variants, and reserves final item admission for domain experts. The release contains an expert-reviewed Pro benchmark covering payment knowledge, context-grounded scenario reasoning, and Attacked Open robustness, plus a lower-assurance Normal pool for inspection and future curation. Across 16 model variants, BenchCompass shows qualitatively different failure modes: missing parametric payment knowledge, incomplete reasoning over supplied rules, and failure to reject plausible but invalid workflows. The benchmark remains unsaturated: the best frontier model reaches 89.6{\%} on Open Context-Grounded Reasoning and 81.7{\%} under attacked inputs, while a representative 32B open-weight model reaches 69.8{\%} and 42.6{\%}. The benchmark, data, and code are publicly available at https://github.com/ant-intl/BenchCompass."
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<abstract>Payment operations are a critical financial infrastructure, but the value of large language models in this domain remains unclear because payment rules change quickly, evidence is fragmented, and decisions depend on transaction state, participant role, region, and payment rail. Existing benchmarks do not isolate whether failures come from missing payment-rule knowledge, poor use of supplied evidence, or brittleness under imperfect harness inputs. We introduce BenchCompass, a payment-domain benchmark whose construction pipeline builds scenario-grounded tasks from typed evidence packs, applies LLM-based quality checks, creates task-input attack variants, and reserves final item admission for domain experts. The release contains an expert-reviewed Pro benchmark covering payment knowledge, context-grounded scenario reasoning, and Attacked Open robustness, plus a lower-assurance Normal pool for inspection and future curation. Across 16 model variants, BenchCompass shows qualitatively different failure modes: missing parametric payment knowledge, incomplete reasoning over supplied rules, and failure to reject plausible but invalid workflows. The benchmark remains unsaturated: the best frontier model reaches 89.6% on Open Context-Grounded Reasoning and 81.7% under attacked inputs, while a representative 32B open-weight model reaches 69.8% and 42.6%. The benchmark, data, and code are publicly available at https://github.com/ant-intl/BenchCompass.</abstract>
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%0 Conference Proceedings
%T BenchCompass: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLMs
%A Dong, Sijie
%A Ren, Wei
%A Hu, Xuanwei
%A Luo, Jiawei
%A Wang, Zifan
%A Feng, Xiaoyun
%A Cai, Hui
%A Xue, Lyuxin
%A Lu, Peng
%A Wu, Wei
%A Zhang, Xin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F dong-etal-2026-benchcompass
%X Payment operations are a critical financial infrastructure, but the value of large language models in this domain remains unclear because payment rules change quickly, evidence is fragmented, and decisions depend on transaction state, participant role, region, and payment rail. Existing benchmarks do not isolate whether failures come from missing payment-rule knowledge, poor use of supplied evidence, or brittleness under imperfect harness inputs. We introduce BenchCompass, a payment-domain benchmark whose construction pipeline builds scenario-grounded tasks from typed evidence packs, applies LLM-based quality checks, creates task-input attack variants, and reserves final item admission for domain experts. The release contains an expert-reviewed Pro benchmark covering payment knowledge, context-grounded scenario reasoning, and Attacked Open robustness, plus a lower-assurance Normal pool for inspection and future curation. Across 16 model variants, BenchCompass shows qualitatively different failure modes: missing parametric payment knowledge, incomplete reasoning over supplied rules, and failure to reject plausible but invalid workflows. The benchmark remains unsaturated: the best frontier model reaches 89.6% on Open Context-Grounded Reasoning and 81.7% under attacked inputs, while a representative 32B open-weight model reaches 69.8% and 42.6%. The benchmark, data, and code are publicly available at https://github.com/ant-intl/BenchCompass.
%U https://aclanthology.org/2026.findings-emnlp.1170/
%P 24762-24781
Markdown (Informal)
[BenchCompass: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLMs](https://aclanthology.org/2026.findings-emnlp.1170/) (Dong et al., Findings 2026)
ACL
- Sijie Dong, Wei Ren, Xuanwei Hu, Jiawei Luo, Zifan Wang, Xiaoyun Feng, Hui Cai, Lyuxin Xue, Peng Lu, Wei Wu, and Xin Zhang. 2026. BenchCompass: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 24762–24781, Budapest, Hungary. Association for Computational Linguistics.