Datasets:
Formats:
csv
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
multi-agent-systems
llm-routing
cost-aware-inference
calibration
agent-collaboration
reasoning
License:
| NOTICE | |
| Cost-Aware Protocol Routing: Matched Protocol Outcomes | |
| This dataset accompanies: | |
| "LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration | |
| Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks" | |
| Chih-Hsuan Yang, Jingyan Jiang, Cheng-Hau Yang, Vikram Vasudevan, | |
| Huihuo Zheng, Venkatram Vishwanath, Rajeev Thakur. | |
| EMNLP 2026. arXiv:2608.14927 | |
| ATTRIBUTION FOR UPSTREAM BENCHMARKS | |
| The derived outcomes in this release were measured on problems from the | |
| following benchmarks. Their problem text is NOT redistributed here; only stable | |
| identifiers are. Anyone who reconstructs the problems from upstream must comply | |
| with the upstream license and cite the upstream work. | |
| Omni-MATH-2 (Apache-2.0) | |
| https://huggingface.co/datasets/martheballon/Omni-MATH-2 | |
| Derived from Omni-MATH, https://arxiv.org/abs/2410.07985 | |
| JEEBench (MIT), Copyright (c) 2023 Data Analytics and Intelligence Research | |
| (DAIR) Group, IIT Delhi | |
| https://github.com/dair-iitd/jeebench | |
| Arora, Singh, Mausam. "Have LLMs Advanced Enough? A Challenging Problem | |
| Solving Benchmark For Large Language Models." EMNLP 2023. | |
| SciBench (MIT), Copyright (c) 2023 Xiaoxuan Wang | |
| https://github.com/mandyyyyii/scibench | |
| Wang et al. "SciBench." ICML 2024. | |
| LAB-Bench (CC-BY-SA-4.0), FutureHouse | |
| https://huggingface.co/datasets/futurehouse/lab-bench | |
| Upstream revision 5c77cec648430f30611808808861eb86f81d5eaa. | |
| LAB-Bench ships a canary string and an accompanying do-not-train request. | |
| This release contains no LAB-Bench problem text, options, or answers, so it | |
| carries no canary; do not use the identifiers here to assemble a training | |
| corpus that would violate that request. | |
| MaScQA (CC-BY-NC-SA-4.0) is NOT part of this release. It has no matched Gemma | |
| run and is excluded from the paper. Its NonCommercial terms are also | |
| incompatible with this release's licensing. | |
| MODEL TERMS | |
| openai/gpt-oss-120b Apache-2.0 weights | |
| google/gemma-4-31B-it Gemma Terms of Use, https://ai.google.dev/gemma/terms | |
| ACKNOWLEDGMENT | |
| This research used resources of the Argonne Leadership Computing Facility, a | |
| U.S. Department of Energy (DOE) Office of Science user facility at Argonne | |
| National Laboratory (ANL) operated under Contract No. DE-AC02-06CH11357. | |
| CONTACT | |
| bellayang@anl.gov | |