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
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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
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