Datasets:

TrangBui
/

RoutingBench-V2

license: cc-by-4.0
language:
  - en
pretty_name: RoutingBench V2-beta
size_categories:
  - n<1K
tags:
  - multi-agent
  - agent-routing
  - orchestration
  - decision-making
  - deterministic-simulation
configs:
  - config_name: decision
    default: true
    data_files:
      - split: development
        path: data/decision/development.parquet
      - split: validation
        path: data/decision/validation.parquet
      - split: test
        path: data/decision/test.parquet
  - config_name: interactive
    data_files:
      - split: development
        path: data/interactive/development.parquet
      - split: validation
        path: data/interactive/validation.parquet
      - split: test
        path: data/interactive/test.parquet
  - config_name: agent_catalog
    data_files:
      - split: train
        path: data/agent_catalog/train.parquet

RoutingBench V2-beta

RoutingBench V2-beta is a deterministic benchmark for evaluating whether an orchestrator can select the next appropriate agent from the task, current observable state, execution memory, budget, and qualitative role cards.

This release is a public research preview. It evaluates routing independently from actor-language-model quality: actors are deterministic state transitions, and agent profiles contain neither backbone identities nor capability scores.

What is included

120 interactive workflows across 8 workflow families.
809 isolated next-action decisions.
24 qualitative agent profiles.
Set-valued admissible actions where multiple routes are reasonable.
Scheduled actor failures, pre-execution routing rejection, recovery, budget, parallel joins, terminal control, and semantic-loop cases.
Public oracle labels and deterministic simulator specifications.

Configurations and splits

Configuration	Development	Validation	Test	Purpose
`decision`	165	211	433	Track A: isolated next-agent selection
`interactive`	24	32	64	Track B: complete deterministic trajectories
`agent_catalog`	24 (`train`)	—	—	Qualitative role contracts

All instances sharing a split_group_id remain in one split. The test split and its labels are intentionally public in V2-beta. It is a diagnostic split, not a sealed leaderboard test.

Loading

from datasets import load_dataset

decision = load_dataset("TrangBui/RoutingBench-V2", "decision")
interactive = load_dataset("TrangBui/RoutingBench-V2", "interactive")
agents = load_dataset("TrangBui/RoutingBench-V2", "agent_catalog")

The default configuration is decision.

Track A: isolated routing decisions

Input to a router consists of public fields such as task, observed_state, task_context, execution_memory, recent_events, budget, agent_pool, and candidate_actions.

Primary label:

admissible_actions: actions justified by the observable state and role contracts.

Diagnostic labels:

progress_actions: actions that the hidden deterministic simulator knows can still reach the goal;
oracle_efficient_actions: actions on the hidden step/cost Pareto frontier;
distractor_actions, action_outcomes, and related validity fields.

Do not provide diagnostic or oracle fields to the router. The reference evaluator constructs a public observation before calling a routing policy.

Primary metrics include admissible next-action accuracy, macro accuracy by workflow template, ambiguous-state accuracy, post-failure and post-rejection accuracy, counterfactual-pair accuracy, STOP precision/recall, distractor rate, efficiency, and calibration.

Track B: interactive trajectory evaluation

The interactive configuration contains deterministic transition specifications. The evaluator starts from the initial public state, asks the router for one agent, applies the corresponding transition, and repeats until completion or a terminal failure such as step limit, cost limit, premature STOP, or semantic loop.

Primary metrics include goal completion, admissible/efficient action rates, macro completion by workflow template, recovery success, action rejection, stagnant repetitions, loop termination, and step/cost regret.

Transition tables are evaluator data and must not be exposed to the router.

Workflow families

Sequential dependency
Parallel join
Conditional branch
Conflict resolution
Tool/artifact routing
Failure recovery
Terminal control
Budget-sensitive routing

Pool variants include base, irrelevant noise, adjacent-role noise, order permutation, and stress pools.

Failure observability

V2-beta distinguishes:

ACTION_REJECTED: the role was rejected before actor execution;
EXECUTED_NO_PROGRESS: the actor ran but made no substantive progress;
EXECUTED_RETRYABLE_FAILURE: the actor ran and its method should be revised;
EXECUTED_INVALID_OUTPUT: execution returned an unusable artifact;
EXECUTED_SUCCESS: execution updated the workflow state.

These statuses are observable runtime telemetry, not recommended next actions.

Baselines and orchestration ablation

The reference code supports:

random, fixed-sequence, oracle, and decision-model routers;
naive: minimal prompt, name/goal role cards, no routing guard;
structured: full qualitative role contracts, failure-aware prompting, and an observable guard against repeating a rejected action without a state change.

The naive/structured comparison measures the combined effect of orchestration engineering. It does not separately identify the causal contribution of prompt, role-card detail, and harness guard.

Reference implementation: BuiThiThanhTrang/RACO_MAS

Construction and quality control

All task text and workflow specifications are synthetic/original. MuSiQue, GAIA, and prior project runs informed abstract workflow and failure patterns; no source questions, answers, passages, or copyrighted attachments were copied.

Generation uses seed 42. Release checks include deterministic generation, JSON schema validation, grouped splits, zero observable-state label collisions, quota checks, checksums, and oracle completion.

See validation_report.json, quota_report.json, split_manifest.json, release_manifest.json, and checksums.sha256.

Intended use

Evaluate state-aware next-agent routing.
Compare routing prompts, role-card designs, and observable harness rules.
Study robustness to overlapping roles, irrelevant agents, order changes, and deterministic failures.
Prototype benchmark contracts before larger hidden-test releases.

Out-of-scope use and limitations

V2-beta is not a complete measure of real-world multi-agent quality.
It does not measure actor reasoning quality, factuality, tool reliability, or natural-language collaboration quality.
Workflows are synthetic and deterministic and currently cover abstract workflow families rather than broad real-world domains.
Test labels are public and have been used for diagnostic analysis; do not use this release for sealed-test or leaderboard claims.
Decision-model performance may depend on provider behavior and model version.

More detail is available in docs/DATASHEET.md, docs/EVALUATION.md, docs/LIMITATIONS.md, and docs/REPRODUCIBILITY.md.

License

The dataset, task text, profiles, and deterministic workflow specifications are released under CC BY 4.0. See LICENSE_DATA.

Citation

@dataset{bui2026routingbench,
  author    = {Trang Bui},
  title     = {RoutingBench V2-beta: A Deterministic Benchmark for State-Aware Agent Routing},
  year      = {2026},
  version   = {2.0.0-beta.1},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/TrangBui/RoutingBench-V2}
}