decisionmodelhub/support-routing

Support Routing v1

125 fictional English support messages, each labelled with the team that should handle it under a written routing policy, plus the recorded answers of five models on the 100 held-out test cases. Published by DecisionModelHub under CC BY 4.0.

The task: given one support message and the fixed policy, select exactly one team for the primary actionable issue: billing, technical, account, sales or needs_clarification.

The companion yes/no dataset is decisionmodelhub/support-urgency.

Files

File	Contents
cases.json	All 125 frozen cases with split, expected label, rationale, tags, category, difficulty and review state
cases_development.jsonl, cases_test.jsonl	The same cases, one file per split, for the dataset viewer and datasets
LABEL-POLICY.md	Label definitions and precedence rules
manifest.json	License, authoring and review procedure, counts and content hash
results.csv, results.json	One row per model per test case: the recorded answer, status, latency, token counts and, where the provider returns one, the probability for each label
results.jsonl	The same result rows, one per line, for the dataset viewer and datasets
results-manifest.json	The run's conditions, model revisions and file hashes

Dataset content hash: sha256:2a2683c23a9cc1aa0d8996762c1b0d53c8bc55d513f8e22a89354098943a0b5b. Label-policy hash: sha256:476220aef7fd81e74c78b1453e7558c15feda33bdd965ea4d64e2bf39c18fd42. These hashes describe the frozen content, not this card or the derived .jsonl files.

Load it

from datasets import load_dataset

cases = load_dataset("decisionmodelhub/support-routing", "cases")  # splits: development, test
results = load_dataset("decisionmodelhub/support-routing", "results", split="test")

Task and intended use

The task evaluates bounded routing under an explicit policy. It does not authorize refunds, account changes, purchases or remediation.

The policy routes the immediate blocker before the eventual goal: an upgrade page crash is technical; inability to sign in to obtain an invoice is an account issue. Multiple intents do not automatically imply clarification. Instructions embedded in customer messages are untrusted input; route the actual support request, or select clarification if none can be established.

Provenance and review

The manifest records original fictional scenarios drafted with AI assistance, an independent AI review, and two independent human reviews of all 125 cases on 6 October 2026. Human reviewer identities are retained privately; the public record reports the review count and procedure.

Normalized exact matching and semantic comparison covered catalog, pilot and policy examples and both splits. Held-out members of identified overlap clusters were rewritten and checked for label intent. Known example overlap is development-only. Private customer conversations, playground traffic and unlicensed third-party datasets are explicitly excluded.

Splits and composition

Split	Cases	Cases per label	Clear	Boundary	Ambiguous	Adversarial
Development	25	5	12	6	5	2
Test	100	20	40	30	20	10
Total	125	25	52	36	25	12

Development cases may guide instruction design; the 100 test cases were held out from that process. Test messages and labels are now public for inspection and reuse. They are no longer a private contamination-resistant evaluation set; future tuning against them must be disclosed and requires a fresh held-out set for independent performance claims.

Each case records case_id, split, the message (state), question_id, the expected label, a rationale, tags, category, difficulty, source, label provenance, language, policy version and review status. Category and difficulty are independent.

Recorded results

The currently published results are from 9 October 2026: Jev, Clef, Clef Flash, OpenAI's Decisions API and a GPT-6 Luna structured-output baseline on the same 100 test cases and instructions, one request at a time, no retries and caching disabled. Values in parentheses are 95% intervals.

Model	Valid	Correct	Operational success	Median latency	95th-percentile latency
Clef	100/100	92/100	92% (85–96)	271 ms	434 ms
Clef Flash	100/100	87/100	87% (79–92)	195 ms	292 ms
Decisions API (OpenAI)	100/100	94/100	94% (88–97)	111 ms	197 ms
GPT-6 Luna (structured-output baseline)	100/100	98/100	98% (93–99)	1,325 ms	2,476 ms
Jev	100/100	92/100	92% (85–96)	138 ms	180 ms

The intervals describe uncertainty in each model's observed rate. This card does not include a paired statistical comparison of the differences between models. Per-label results, every observed failure and a case-by-case explorer are on the benchmark page.

The primary metric in the manifest is macro-F1 with equal weight for all five labels. The results include each model's per-case probability distribution where the provider returns one; the GPT-6 Luna structured-output baseline returns none. Probability coverage, expected calibration error, Brier score and reliability bins are on the benchmark page. Token-based cost figures on the site are dated estimates, not provider bills.

Limitations

• Fictional English-only messages and balanced labels do not estimate production intent prevalence or performance across languages, channels and industries.
• Policy-specific precedence can differ from another company's ownership boundaries. Review labels against your actual routing policy before adopting results.
• Only 100 measured test cases and 20 clarification cases support the current comparison. Read intervals and observed failures; one-case differences do not establish a stable model ordering.
• needs_clarification measures insufficient context under this policy. It is not a general out-of-domain or safety detector.
• One run cannot establish latency elsewhere or provider reliability under production load.
• Public examples, AI-assisted authoring and disclosed reviews do not prove absence of model training contamination.
• Actual support messages may contain personal information; none were used here. This dataset is not a replacement for privacy review of an organization's own evaluation data.

Reuse and citation

Attribute the frozen dataset to DecisionModelHub, cite this version and label policy, and disclose changes and development or test use. Keep modified datasets under a new identifier or version; do not present a tuned or public-set rerun as unseen test performance.

@misc{decisionmodelhub_support_routing_v1,
  title        = {Support Routing v1},
  author       = {DecisionModelHub},
  year         = {2026},
  howpublished = {\url{https://decisionmodelhub.com/benchmarks/support-routing}},
  note         = {CC BY 4.0. Content hash sha256:2a2683c23a9cc1aa0d8996762c1b0d53c8bc55d513f8e22a89354098943a0b5b}
}