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WorkflowEvals dataset snapshot

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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - evaluation
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+ - workflows
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+ configs:
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+ - config_name: cases
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+ default: true
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+ data_files:
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+ - split: test
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+ path: data/cases.parquet
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+ - config_name: questions
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+ data_files:
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+ - split: test
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+ path: data/questions.parquet
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+ - config_name: run_results
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+ data_files:
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+ - split: test
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+ path: data/run_results.parquet
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+ ---
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+
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+ # Agent trace triage
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+
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+ Snapshot: 2026-09-28. 111 cases and 1,124 question instances.
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+ Default reference: **consensus**. Labels are model-generated references.
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+
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+ ## Data
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+
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+ Load configuration `cases`, `questions`, or `run_results`; all have a `test` split.
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+
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+ - **cases:** one row per `case_id`, with the complete input in `input_json`, descriptive
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+ `metadata_json`, and `openai`, `anthropic`, and `consensus` labelsets. Decisions are grouped
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+ by `policy_id` and contain `status`, `actions`, and `primary_action`.
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+ - **questions:** one row per distinct case/node/question/input combination, identified by
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+ `question_instance_id`. `question_json` and `state_json` describe
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+ the model input. Each labelset contains `answer_json`, `probabilities`, `confidence`,
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+ `confidence_method`, and `expected_score` for score questions. `answer_json` is the modal
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+ answer (null for ties); the expected score is a separate numeric value.
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+
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+ Independent labels include the actual model, reasoning effort, question mode, and
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+ `used_opus_fallback`. Missing labels have explicit status and null values; the case is retained.
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+ Question status `not_answered` means that reference did not answer that question instance.
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+
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+ ## Scoring
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+
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+ `dataset.json` lists policy IDs and the applicable comparison rules:
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+
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+ - `exact_actions`: compare complete action sets including arguments; ignore order and duplicates.
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+ - `primary_action`: compare the designated primary action including its arguments.
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+
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+ Cases match the consensus scoring set used in the reported results.
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+
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+ ## Run results
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+
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+ `run_results` contains 9 code-route runs selected by the final plots:
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+ 999 rows, one per `run_id` and `case_id`, with 9,894
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+ recorded question answers. The `test` split contains every exported row; nothing is partitioned.
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+
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+ Each row includes the model's name, provider, reasoning effort, and question mode;
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+ execution `status`, `cost_usd`, `wall_time_s`, `summed_call_time_s`, token counts, and call count;
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+ and two nested lists:
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+
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+ - `questions`: `node_id`, `question_id`, `kind`, the actual `question_json` and `state_json`,
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+ `status`, `answer_json`, `probabilities`, `expected_score`, `confidence`, and `confidence_method`.
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+ Inputs are repeated so each result is self-contained. Answers are the recorded run values;
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+ `expected_score` is calculated from the distribution for score questions. Only recorded
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+ questions appear: branches not taken do not produce fabricated answers. Dynamic choice
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+ options are preserved exactly, including instances absent from the reference question table.
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+ - `decisions`: `policy_id`, `status`, `actions`, `primary_action`, and `scores`. Each score has
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+ `metric_id` and a boolean `value` against the **consensus** reference (null when inapplicable).
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+ Metrics use only `exact_actions` or `primary_action` comparisons.
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+
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+ Runs use exactly the consensus scoring cases in `cases`. Prediction errors remain in scoring
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+ and count as wrong. Measurements are per case, shared across policy decisions; missing
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+ measurements remain null. Cost is recorded in USD using the basis named in each run summary.
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+
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+ `dataset.json` contains `runs`, keyed by `run_id`, with plotted aggregate scores, denominators,
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+ mean cost/time, and measurement counts. The selected run for a model configuration is the one
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+ used by the plot (highest primary accuracy), not necessarily its newest execution. Overall
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+ plot points average workflows equally for configurations present in all four workflows.
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+
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+ ## Consensus
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+
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+ Question-level consensus averages the available probability distributions for the same question
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+ and input. `contributors` and `weights` specify 0.5 each for two sources or 1.0 for a single
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+ source. Consensus confidence is the maximum blended probability.
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+
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+ Case-level consensus contains the final reference actions from applying the workflow to its
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+ blended signals. These are the references used for evaluation. Distinct dynamic question
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+ instances remain separate in the question table.
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+
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+ ## Coverage
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+
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+ | Labelset | Cases with decisions | Opus fallback cases |
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+ |---|---:|---:|
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+ | openai | 111 | 0 |
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+ | anthropic | 109 | 0 |
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+ | consensus | 111 | 0 |
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+
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+ ## Encoding
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+
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+ Fields ending in `_json` are JSON-encoded text; decode with `json.loads`.
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+ Probability distributions are lists of `option`/`probability` pairs. Action arguments are
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+ preserved in `arguments_json`. Descriptive metadata is separate from model-visible state.
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+ {
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+ "title": "Agent trace triage",
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+ "workflow": "agent_trace_observability",
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+ "snapshot_date": "2026-09-28",
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+ "default_reference": "consensus",
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+ "title": "Disposition agreement",
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+ "encodings": {
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