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EMNLP_Cost-Aware-Protocol-Routing

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multi-agent-systems
llm-routing
cost-aware-inference
calibration
agent-collaboration
reasoning
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EMNLP_Cost-Aware-Protocol-Routing / README.md
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metadata 
license: cc-by-4.0
language:
  - en
pretty_name: 'Cost-Aware Protocol Routing: Matched Protocol Outcomes'
size_categories:
  - 10K<n<100K
annotations_creators:
  - machine-generated
source_datasets:
  - extended
task_categories:
  - tabular-classification
  - question-answering
task_ids:
  - multi-class-classification
tags:
  - multi-agent-systems
  - llm-routing
  - cost-aware-inference
  - calibration
  - agent-collaboration
  - reasoning
  - arxiv:2608.14927
  - emnlp2026
configs:
  - config_name: matched_labels
    default: true
    data_files:
      - split: all
        path: data/matched_labels.csv
  - config_name: problems
    data_files:
      - split: all
        path: data/problems.csv
  - config_name: labels_for_scoring
    data_files:
      - split: all
        path: data/labels_for_scoring.csv
  - config_name: splits_six_setting
    data_files:
      - split: all
        path: data/splits/six_setting_splits.csv
  - config_name: splits_primary_omnimath
    data_files:
      - split: all
        path: data/splits/primary_omnimath_splits.csv
  - config_name: router_test_predictions
    data_files:
      - split: all
        path: data/router/six_setting_test_predictions.csv
  - config_name: router_metrics
    data_files:
      - split: all
        path: data/router/six_setting_metrics.csv
  - config_name: costs_omnimath
    data_files:
      - split: all
        path: data/costs/omnimath_per_protocol_costs.csv
  - config_name: confidence_postanswer
    data_files:
      - split: all
        path: data/confidence/postanswer_confidence_predictions.csv
  - config_name: confidence_preanswer
    data_files:
      - split: all
        path: data/confidence/primary_omnimath_confidence_predictions.csv
  - config_name: example_id_crosswalk
    data_files:
      - split: all
        path: registry/example_id_crosswalk.csv
  - config_name: confidence_metrics
    data_files:
      - split: all
        path: data/confidence/six_setting_confidence_metrics.csv
  - config_name: protocol_value_targets
    data_files:
      - split: all
        path: data/confidence/failure_and_protocol_value_targets.csv
  - config_name: aggregate_matched_coverage
    data_files:
      - split: all
        path: data/aggregate/matched_protocol_coverage.csv
  - config_name: aggregate_oracle_distribution
    data_files:
      - split: all
        path: data/aggregate/oracle_label_distribution.csv
  - config_name: aggregate_main_routing_heldout
    data_files:
      - split: all
        path: data/aggregate/main_routing_heldout.csv
  - config_name: registry_experiments
    data_files:
      - split: all
        path: registry/experiments.csv

Cost-Aware Protocol Routing: Matched Protocol Outcomes

The short version. We ran the same 6,803 reasoning problems through four different LLM collaboration setups — from a single direct answer up to a four-agent deliberation — and recorded, for every problem, which ones got it right. Then we asked whether a model can look at a problem beforehand and predict which setup is worth paying for.

It can predict whether it will fail. It cannot predict which collaboration protocol will fix the failure. That gap is what this dataset is for.

This is the data release for the EMNLP 2026 paper LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off.

The four protocols

Every released problem was run under all four, with the same solver model.

Protocol What it does Relative cost
Baseline One direct answer. No revision. cheapest
Single One solver iterates on its own answer with evaluator feedback. low
PER Planner decomposes, executor solves, reviewer checks. medium
Broadcast Several agents deliberate over a shared channel. most expensive

The oracle label

For each problem, oracle_label is the first protocol that succeeded, scanned in the fixed cost order Baseline -> Single -> PER -> Broadcast. If all four failed, the label is none.

Two things to be clear about:

  • none is not a fifth protocol. It is a router action: abstain, spend nothing, because nothing observed here worked. Four protocols were executed on every released problem.
  • The oracle is retrospective. It is computed from one realized execution per protocol, not from repeated sampling. It is the best a router could have done on these specific runs, not a ground-truth best action. A protocol that failed once here might succeed on a resample.

What is in here

15,088 rows in the flagship table: 10 settings (5 benchmark conditions x 2 solver models), covering 6,803 distinct problems.

Setting Benchmark Condition Solver n
omnimath2__competition_math_4181__gpt_oss_120b OmniMath competition math gpt-oss-120b 4,181
omnimath2__competition_math_4181__gemma_4_31b OmniMath competition math Gemma-4-31B-it 4,181
labbench__text_no_tool__gpt_oss_120b_text_no_tool LAB-Bench text, no tools gpt-oss-120b 1,542
labbench__text_no_tool__gemma_4_31b_text_no_tool LAB-Bench text, no tools Gemma-4-31B-it 1,542
labbench__llm_strict__gpt_oss_120b LAB-Bench strict in-prompt evidence gpt-oss-120b 741
labbench__llm_strict__gemma_4_31b LAB-Bench strict in-prompt evidence Gemma-4-31B-it 741
scibench__text_only__gpt_oss_120b SciBench text only gpt-oss-120b 565
scibench__text_only__gemma_4_31b SciBench text only Gemma-4-31B-it 565
jeebench__text_only__gpt_oss_120b JEEBench text only gpt-oss-120b 515
jeebench__text_only__gemma_4_31b JEEBench text only Gemma-4-31B-it 515

Alongside the outcomes: two documented split schemes, held-out router predictions and metrics for six settings, confidence-probe measurements, and the paper's eight aggregate result tables reproduced verbatim.

Full column-by-column documentation: docs/schema.md.

What is NOT in here, and why

No problem text. No gold answers. No answer options. No reference solutions.

The four upstream benchmarks carry four different licenses, and LAB-Bench — the most restrictive — is CC-BY-SA-4.0 with an upstream do-not-train request. Rather than ship a mixed-license text column that downstream users would have to untangle correctly, this release withholds text across the board and gives you stable identifiers plus reconstruction instructions instead.

That applies even to JEEBench and SciBench, whose MIT licenses would have permitted redistribution. The decision was made release-wide because the flagship table interleaves all four benchmarks in one file.

To attach the text yourself: docs/reconstruction.md. The licensing evidence: docs/license_audit.md.

Also not included: raw model generations, per-problem cost accounting for nine of the ten settings (one setting is covered, see below), MaScQA (excluded — NonCommercial, and not in the paper), and the Gemma-3-27B scope check. See docs/provenance.md.

Also not included: Baseline final-answer text. The post-answer probe consumed it, so data/probe_inputs.jsonl is an identifier and metadata manifest, not a runnable prompt set — rebuilding runnable probe inputs needs both the problem text rehydrated from upstream and that setting's Baseline final answer. Baseline answers are not released, and for three of the six probe settings they no longer exist in the project's own artifacts either, so the probe is not fully re-runnable by anyone. The probe's outputs are released in full, so the paper's numbers stay reproducible. Coverage is measured in TODO.md.

Cost: per-problem tokens, for one setting only

The paper's cost axis is tokens, and data/costs/omnimath_per_protocol_costs.csv gives per-problem token totals and model-call counts for all four protocols — but only for omnimath2__competition_math_4181__gpt_oss_120b, one of the ten settings. The other nine have no per-problem cost data here; their cost appears only through the aggregate tables.

These are protocol-level totals, summed over every model call the protocol made. That is the paper's accounting. They are not a single call's prompt+completion — Baseline averages 9.67 model calls per problem in this setting, so the two quantities differ by roughly that factor. If you compare against another dataset's total_tokens, check which quantity it measures first.

4,155 of 4,181 problems are covered. The 26 omitted problems form 13 duplicate-text pairs where a cost row cannot be attributed to a specific problem_id; they were dropped rather than guessed. As a result, the mean Baseline token count recomputed from this file is 18,432.0, while the paper's published figure over all 4,181 problems is 18,385.4. That gap is the arithmetic of the 26 dropped rows, not a disagreement — cite 18,385.4 for the paper, expect 18,432.0 from this file, and do not reconcile them by adjusting either.

Two confidence probes — do not confuse them

The dataset ships two different instruments. They ask different questions at different points in the pipeline, and they support different numbers in the paper. Every row of both files carries a probe_type column.

post-answer probe pre-answer probe
probe_type post_answer_pre_collaboration pre_answer_q1
file data/confidence/postanswer_confidence_predictions.csv data/confidence/primary_omnimath_confidence_predictions.csv
rows 12,928 (all six analysed settings) 839 (OmniMath primary split)
runs after Baseline answers, before any collaboration before any solving
sees the problem, allowed metadata, and the model's own Baseline final answer the problem only
asks "is this Baseline answer correct?" "how likely am I to solve this in one pass?"
supports the paper's headline failure-risk result the confidence-gate policy row

The title claim comes from the post-answer probe. Its released predictions reproduce data/aggregate/postanswer_confidence.csv for all six settings to four decimals — including the headline gpt-oss-120b OmniMath figures of 4,181 rows, 4,151 parseable, and 0.8847 failure AUROC.

Reproducibility limit. The post-answer probe cannot be fully re-run from released artifacts, because the Baseline final-answer string that the probe consumes was not persisted for the gpt-oss runs. The probe's outputs and metrics are fully released and independently reproducible. Recoverable coverage is 6,462 / 12,928 probe rows (50.0%): 100% for the three Gemma settings, 0% for the three gpt-oss settings. See TODO.md.

Neither probe ever sees the gold answer, the correctness label, the oracle label, or any protocol outcome. The post-answer prompt states that boundary explicitly and is injection-hardened — it marks the problem text and the baseline answer as untrusted data, tells the model not to follow instructions inside them, and enumerates what the model does not have. It ships verbatim at docs/confidence_probe_prompt.txt.

confidence is 0-100, not 0-1, in both probe files, and the pre-answer file leaves it empty for all 222 fallback rows rather than filling in a value. The documented gate treats an empty confidence as escalate. Get either convention wrong and you get a plausible, wrong number: on the 423 test problems the correct reading reproduces the published 78.0%, while misreading the scale gives 60.76% and treating empties as "stay" gives 73.76%. See docs/schema.md.

Join the probe predictions on example_id, never on problem_uid_run. The Gemma and gpt-oss runs use different native identifier schemes for the same problems. Joining on the native id gives zero overlap for all three Gemma settings while the three gpt-oss settings join perfectly — it silently drops half the data and still looks like it worked. registry/example_id_crosswalk.csv maps (setting_id, example_id) -> problem_id so you never hit this.

Dataset Structure

Data Files

All measured row counts, verified against the files in this repository.

File Rows Cols What it is
data/matched_labels.csv 15,088 15 Start here. One row per (setting, problem): the four protocol outcomes and the fixed-order oracle label, for all 10 settings.
data/problems.csv 6,803 10 Router-visible problem metadata. No labels — this is the feature side.
data/labels_for_scoring.csv 12,928 15 The label side, kept in a separate file from the features on purpose.
data/probe_inputs.jsonl 12,928 9 Identifier manifest for the confidence probe (not a runnable prompt set — see below).
data/confidence/postanswer_confidence_predictions.csv 12,928 12 Post-answer probe outputs, 6 settings. Backs the headline result.
data/confidence/primary_omnimath_confidence_predictions.csv 839 15 Pre-answer q1 probe on the primary split. A different instrument.
data/router/six_setting_test_predictions.csv 5,832 10 Held-out predictions for 3 routers × 6 settings on identical test ids.
data/costs/omnimath_per_protocol_costs.csv 4,155 10 Per-protocol token totals and model calls. One setting only.
data/splits/six_setting_splits.csv 12,928 6 70/15/15 stratified by oracle label, seed 20260712.
data/splits/primary_omnimath_splits.csv 4,181 5 80/10/10 stratified, seed 42, test n=423.
data/aggregate/*.csv 6–24 each — The eight camera-ready aggregate tables, as published.
registry/* — — Experiment, benchmark, model and protocol registries; schema; manifest; checksums; the example_id crosswalk.

Data Splits

Split set Train Dev Test Seed Stratified by
Six-setting router 9,046 1,938 1,944 20260712 oracle label, within setting
Primary (OmniMath) 3,342 416 423 42 oracle label

Disjointness is checked by validate.py, which fails if any problem appears in more than one split.

Data Fields

The key fields of the flagship table, matched_labels.csv:

Column Type Meaning
setting_id string benchmark__condition__solver, one of 10
problem_id string Stable problem identifier, unique within a setting
baseline_correct, single_correct, per_correct, broadcast_correct 0/1 Did that protocol solve this problem
oracle_label enum First success in the fixed order; none if all four failed
any_protocol_solved 0/1 Did any of the four succeed
model, model_endpoint, domain, benchmark_id, slice_id, run_id string Provenance

oracle_label takes exactly one of baseline_llm, single_agent, per, broadcast, none. Do not trust it blindly — recompute it from the four outcome columns; validate.py does exactly that and reports zero mismatches across all 15,088 rows.

Full field-level documentation, including the confidence scale and null handling, is in docs/schema.md.

Leakage warning — read this before training anything

Features and labels are in separate files, on purpose. Do not merge them into your model input.

File Role Contains outcomes?
data/problems.csv feature side no
data/probe_inputs.jsonl feature side no
data/costs/omnimath_per_protocol_costs.csv feature side no
data/confidence/postanswer_confidence_predictions.csv feature side no
data/confidence/primary_omnimath_confidence_predictions.csv feature side no
data/matched_labels.csv label side yes
data/labels_for_scoring.csv label side yes
data/router/six_setting_test_predictions.csv label side yes

Three specific hazards:

  1. The six-setting splits are drawn independently per setting. A problem can be in train for gpt-oss-120b and in test for Gemma-4-31B-it. Measured cross-solver split agreement is about 54%. If you pool both solvers' rows and train one model, you will leak. Split on problem_id yourself if you pool.
  2. The oracle label is not a router input. It is computed from the outcomes you are trying to predict. It is deliberately absent from both split files.
  3. baseline_correct is an outcome, not a feature. Several analyses condition on it, which is legitimate for measurement but not for a router that must decide before any execution.

Quick start

Download 

hf download AgentsSci/EMNLP_Cost-Aware-Protocol-Routing --repo-type dataset --local-dir data/emnlp_protocol_routing

The Python equivalent:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="AgentsSci/EMNLP_Cost-Aware-Protocol-Routing",
    repo_type="dataset",
    local_dir="data/emnlp_protocol_routing",
)

Validate what you downloaded 

python data/emnlp_protocol_routing/validate.py

It checks the release and schema versions, every expected file, all row counts, benchmark/model/protocol coverage, the oracle recomputation, split disjointness, the leakage guard, and every sha256 in registry/checksums.sha256, then reports which optional artifacts are absent by design. It exits nonzero on any failure.

Load 

from datasets import load_dataset

# flagship table: one row per (setting, problem)
d = load_dataset("AgentsSci/EMNLP_Cost-Aware-Protocol-Routing", "matched_labels")["all"]

# feature side, no labels
p = load_dataset("AgentsSci/EMNLP_Cost-Aware-Protocol-Routing", "problems")["all"]

Or just read the CSVs — everything under 50 MB is plain CSV on purpose:

import pandas as pd
m = pd.read_csv("data/matched_labels.csv")          # 15,088 rows
m.groupby("setting_id").oracle_label.value_counts(normalize=True)

Recompute the oracle yourself in one line, and confirm it matches:

import numpy as np
rec = np.select(
    [m.baseline_correct == 1, m.single_correct == 1,
     m.per_correct == 1, m.broadcast_correct == 1],
    ["baseline_llm", "single_agent", "per", "broadcast"],
    default="none")
assert (rec == m.oracle_label).all()   # holds for all 15,088 rows

Per-benchmark licensing

Benchmark Upstream license Text redistributed here? Upstream source
Omni-MATH-2 Apache-2.0 no martheballon/Omni-MATH-2
JEEBench MIT no dair-iitd/jeebench
SciBench MIT no mandyyyyii/scibench
LAB-Bench CC-BY-SA-4.0, do-not-train request no futurehouse/lab-bench
MaScQA CC-BY-NC-SA-4.0 benchmark excluded entirely M3RG-IITD/MaScQA

Project-authored content (documentation, registry tables, all derived measurements) is CC-BY-4.0. The reasoning, stated plainly: the released tables are our own measurements of model behaviour, and no upstream problem text or gold answers are redistributed here, so each upstream benchmark's own license continues to govern its own content and LAB-Bench's CC-BY-SA-4.0 share-alike obligation is not triggered — there is no LAB-Bench content in this release to share alike. Model terms: gpt-oss-120b weights are Apache-2.0; Gemma-4-31B-it is governed by the Gemma Terms of Use. Full notice in LICENSE and NOTICE.

Intended and out-of-scope use

Intended. Training and evaluating cost-aware routers; studying calibration and failure prediction; measuring how much collaboration is actually worth on a given problem; benchmarking against a retrospective oracle without paying for four protocol executions.

Out of scope. Treating the oracle labels as repeated-sampling expected optima. Reading per-protocol solve rates as general claims about those protocols outside these benchmarks and these two solvers. Using the identifiers here to assemble a training corpus over LAB-Bench, against its upstream do-not-train request. Any commercial use of MaScQA-derived work — which is moot here, since MaScQA is not included.

Verification

Every claim below was recomputed at staging time, not copied:

  • Recomputing oracle_label from the four correctness flags in fixed order reproduces the stored label for all 15,088 rows, zero mismatches.
  • The per-problem labels reproduce the paper's matched_protocol_coverage and oracle_label_distribution tables for all ten settings, to two decimals.
  • Both solvers cover an identical canonical problem set in all five paired settings.
  • Splits are pairwise disjoint and exhaustive in both schemes.

Known discrepancies are stated, not hidden, in docs/provenance.md — including a 3-row disagreement between two source tables and a 73% clean-parse rate on the primary confidence probe.

Links

Citation 

@inproceedings{yang2026costaware,
  title     = {LLMs Can Predict Failure Risk, But Struggle to Predict Which
               Collaboration Protocol Pays Off: Cost-Aware Protocol Routing
               Across Reasoning Tasks},
  author    = {Yang, Chih-Hsuan and Jiang, Jingyan and Yang, Cheng-Hau and
               Vasudevan, Vikram and Zheng, Huihuo and Vishwanath, Venkatram and
               Thakur, Rajeev},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in
               Natural Language Processing (EMNLP)},
  year      = {2026},
  eprint    = {2608.14927},
  archivePrefix = {arXiv}
}

Authors. Chih-Hsuan Yang1, Jingyan Jiang1, Cheng-Hau Yang1, Vikram Vasudevan2, Huihuo Zheng1, Venkatram Vishwanath1, Rajeev Thakur1

1 Argonne National Laboratory, Lemont, IL, USA 2 Oregon State University, Corvallis, OR, USA

Contact: bellayang@anl.gov

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.