MA-Bench: Measuring Operational Planning in Language Models

MA-Bench evaluates one step of the Joint Planning Process (JP 5-0): mission analysis. Each of the 10 scenarios pairs a full synthetic staff packet (10 to 12 documents, 35,649 to 242,658 characters) with an expert-authored rubric of 74 to 170 atomic, binary items (1,257 items in total). All scenarios are original and unpublished; no packet or rubric text exists elsewhere. This is version 1.0.0, with rubrics frozen on 2026-09-14.

Files

file	rows	one row per
data/cases.jsonl	10	scenario: case_id, operation, scenario, the three judge header fields (planning_level, document_type, difficulty), counts
data/documents.jsonl	112	model-facing document: case_id, doc_number, doc_order, title, source_file, text
data/rubric_items.jsonl	1,257	rubric item: item_id, item_uid, step, step_name, tier, severity, polarity, weight, question, pass_condition, fail_condition, source_fact, artifact_anchor, source_documents
runs/briefs.jsonl	1,560	generated brief: brief_id, condition, case_id, scenario, model, model_slug, seed, text (as graded)
runs/verdicts.jsonl	2,796	judge verdict on a brief: brief_id, judge, case_id, failed_items, failures (each with evidence and rationale), unknown_ids, overall_rationale, judge_model, served_model
runs/expert_study.jsonl	190	expert item verdict (see Expert study)
runs/expert_study_briefs.jsonl	10	brief the experts graded: brief, scenario, model, text

• packets/<case>_<operation>/<document>.md: each scenario's documents, one file per document, exactly as models receive them (markdown converted from the authored Word or markdown files; images omitted).
• rubrics/<case>_<operation>.csv: each scenario's rubric, one row per item: step, tier, severity and weight, the question with its pass and fail conditions, and the source fact and documents it rests on. UTF-8; opens in Excel.
• scoring/: the task prompt, the frozen judge prompt and its input builder (judge_input.py), the severity weights, and the reference scorer (score.py).
• MANIFEST.json: the sha256 of every file.

Scenarios

case	code	operation	documents	packet characters	rubric items
C1	SF-FULL	Silent Fathom	12	134,337	87
C2	JA-FULL	Jagged Anvil	11	70,196	147
C3	CT-FULL	Coral Tempest	11	35,649	130
C4	ST-FULL	Sea Tiger	12	181,678	170
C5	GG-FULL	Gulf Guardian	12	80,341	165
C6	PH-FULL	Pacific Harrier	10	229,187	125
C7	SS-FULL	Southern Shield	12	242,658	163
C8	SM-FULL	Steadfast Mariner	11	156,729	111
C9	RH-FULL	Resolute Herald	11	151,265	85
C10	OR-FULL	Onyx Rain	10	93,897	74

The code is how brief ids name each scenario.

Model input

A model receives the task prompt (scoring/task_prompt.md, with the operation name filled in and the length-cap paragraph appended), a blank line, # SOURCE DOCUMENTS, and then the packet: the scenario's documents in doc_order, assembled exactly as

packet = f"# {operation} — Mission Analysis Source Packet\n" + "".join(
    f"\n\n## Document {doc_number} — {title} (source file: {source_file})\n\n{text}\n" for each document)

Rubric

Each item is an atomic, binary question about the brief, with its pass and fail conditions and the source fact and documents it rests on. Every item has a mission analysis step (S1 to S12, named in step_name), a tier and a severity.

• Tier: 1 = stated in one document (400 items), 2 = synthesis across facts (406), 3 = planning judgment (451).
• Severity: CI, critically important (484); I, important (536); SI, slightly important (80); D, detrimental (82); CD, critically detrimental (75). D and CD items name a wrong move, and their pass condition is the presence of that move.

Judging and scoring

The system under test receives the task prompt (scoring/task_prompt.md, operation name filled in, cap paragraph appended) followed by the packet (see documents.jsonl) and returns one brief, truncated to 3,000 words before grading. The cap is part of the protocol: it equalizes the length budget across models. A reasoning-model judge grades the brief against every rubric item in one call, returning only failed items with evidence; the judge never sees the packet. Items are binary and atomic; absence is failure. Negative items (severity D, CD) describe a mistake and count against the brief when the mistake is present.

Severity weights: CI +9, I +5, SI +2, D -5, CD -9. Weighted score = (positive weight earned + negative weight incurred) / total positive weight, not clamped.

Generated briefs

runs/briefs.jsonl holds 1,560 briefs, each exactly as graded (in capped conditions, text beyond the 3,000th word is already removed). brief_id is <condition>/<scenario code>__<model_slug>__s<seed>.

condition	briefs	settings	models
benchmark_capped	540	3,000-word cap, medium reasoning effort where the provider exposes it	Claude Fable 5.1, Claude Opus 5, DeepSeek-V4-Pro, GLM-5.3, GPT-5.6 Luna, GPT-5.6 Sol, GPT-6 Astra, Gemini 3.1 Pro, Gemma 4 31B, Grok 4.6, Kimi K3, MiniMax M3, Mistral Medium 3.5, Muse Spark 1.3, Nemotron 3 Ultra, Qwen3.8-Max
ladder_capped	120	the same settings; the capability ladder	Claude Sonnet 5, DeepSeek-V4-Flash, GPT-5.4, Gemini 3.8 Flash
effort_low	150	3,000-word cap, low reasoning effort	Claude Sonnet 5, GPT-5.4, GPT-5.6 Sol, GPT-6 Astra, Gemini 3.8 Flash
effort_high	150	3,000-word cap, high reasoning effort	Claude Sonnet 5, GPT-5.4, GPT-5.6 Sol, GPT-6 Astra, Gemini 3.8 Flash
uncapped	600	no length cap, medium reasoning effort	Claude Fable 5.1, Claude Opus 5, Claude Sonnet 5, DeepSeek-V4-Flash, DeepSeek-V4-Pro, GLM-5.3, GPT-5.4, GPT-5.6 Luna, GPT-5.6 Sol, GPT-6 Astra, Gemini 3.1 Pro, Gemini 3.8 Flash, Gemma 4 31B, Grok 4.6, Kimi K3, MiniMax M3, Mistral Medium 3.5, Muse Spark 1.3, Nemotron 3 Ultra, Qwen3.8-Max

Models

slug	model
anthropic_claude_fable_5_1	Claude Fable 5.1
anthropic_claude_opus_5	Claude Opus 5
anthropic_claude_sonnet_5	Claude Sonnet 5
azure_ai_deepseek_v4_flash	DeepSeek-V4-Flash
azure_ai_deepseek_v4_pro	DeepSeek-V4-Pro
gemini_gemini_3_1_pro_preview	Gemini 3.1 Pro
gemini_gemini_3_8_flash	Gemini 3.8 Flash
google_gemma_4_31b_it	Gemma 4 31B
fireworks_ai_glm_5p3	GLM-5.3
openai_gpt_5_4	GPT-5.4
openai_gpt_5_6_luna	GPT-5.6 Luna
openai_gpt_5_6_sol	GPT-5.6 Sol
openai_gpt_6_astra	GPT-6 Astra
xai_grok_4_6	Grok 4.6
fireworks_ai_kimi_k3	Kimi K3
fireworks_ai_minimax_m3	MiniMax M3
mistral_mistral_medium_3_5	Mistral Medium 3.5
meta_ai_muse_spark_1_3	Muse Spark 1.3
fireworks_ai_nemotron_3_ultra_nvfp4	Nemotron 3 Ultra
fireworks_ai_qwen3p8_max	Qwen3.8-Max

Verdicts

runs/verdicts.jsonl holds each judge's verdict on a brief. All judges used the same frozen judge prompt (scoring/judge_prompt.md):

• gpt-5.6, at high reasoning effort: every brief (1,560).
• gemini-3.1-pro: every brief outside uncapped, and the uncapped briefs of GPT-6 Astra and GPT-5.4 (1,020).
• claude-opus-5: 216 benchmark_capped briefs (scenarios C1, C2, C3, C5, C6 and C8, ten models, every seed).

A verdict lists only the items the brief fails (failures, each with quoted evidence and a rationale); every rubric item not listed passes. A positive item (severity CI, I, SI) earns its weight when it passes. For a negative item (D, CD) the pass condition is the presence of the wrong move it names, so a listed negative item is a mistake the brief avoided and an unlisted one is a mistake it made, charged its (negative) weight. scoring/score.py turns a verdict into a brief's weighted score. A model's score is the mean over scenarios of its mean brief score. python scoring/score.py data/rubric_items.jsonl <verdicts> scores a JSONL of verdicts for one judge. In some verdicts, condition, brief_id and model_slug were renamed to match this layout; the judge's output is unchanged.

Expert study

Four JPME-credentialed planners, de-identified as expert_1 to expert_4, independently graded the same 190 item verdicts: nineteen rubric items in each of ten briefs, sampled across every tier and severity. They saw each brief and rubric item as the judge does, without the packet and without the model's name. Each row gives the brief (B01 to B10), its scenario code and model, the rubric item as the experts saw it (id, step, tier, severity, question, pass and fail conditions), each expert's verdict, the expert majority (Pass, Fail, or tie for a two-two split), and the production judge's verdict on the same item (gpt-5.6 at high reasoning effort, with the judge prompt that was then frozen) with its note.

The briefs were sampled before the packets were frozen on 2026-09-08, so 53 of the 190 items carry rubric text that later changed; the text given is what the experts graded, and the judge graded the same text. runs/expert_study.jsonl has one row per item verdict and runs/expert_study_briefs.jsonl the ten briefs.

Provenance

Rubrics frozen 2026-09-14 from the ops team's expert-reviewed final sheet (1,156 delivered, 100 gap-fill items and 1 item restored after the expert grading study; 3 earlier items withdrawn). Relative to the 2026-09-08 freeze, 5 items had their question or pass/fail text revised, 7 gained source-document references, and 1 item was restored; the document sets are unchanged. Rubrics were authored against the packet plus downstream products of a completed mission analysis (component orders, CCIRs, event templates, and for some scenarios an OSINT bundle) that the model never receives; those documents are not part of this release.