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ProtectionBench

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metadata 
language:
  - en
license: cc-by-4.0
task_categories:
  - text-generation
pretty_name: ProtectionBench
size_categories:
  - n<1K
tags:
  - benchmark
  - evaluation
  - decision-making
  - reasoning
  - physics
configs:
  - config_name: main
    default: true
    data_files:
      - split: test
        path: main.parquet
  - config_name: neutral
    data_files:
      - split: test
        path: neutral.parquet
  - config_name: reverse
    data_files:
      - split: test
        path: reverse.parquet
      - split: normal
        path: reverse_normal.parquet
  - config_name: kad
    data_files:
      - split: test
        path: kad.parquet
  - config_name: paraphrase
    data_files:
      - split: test
        path: paraphrase.parquet

ProtectionBench

This work was completed at Xiaohongshu.

The evaluation dataset for When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making. Main compares a low-immediate-risk person/animal with an object facing larger time-sensitive physical damage. Reference labels describe the larger stated physical consequence, not universal moral correctness.

Figure 1: Protection Mode examples and model-level results

Figure 1. Protection Mode examples and results for subject-priority coverage and knowledge–action divergence. Original PDF · PowerPoint source.

Load immediately 

from datasets import load_dataset

bench = load_dataset("condafile/ProtectionBench", split="test")
assert len(bench) == 285
print(bench[0]["prompt"])
print(bench[0]["messages"])  # Ready-to-use system/user messages

The default configuration is main. Files are standard Parquet with Zstandard compression. No ZIP extraction, custom dataset script, authentication, or trust_remote_code is required.

neutral = load_dataset("condafile/ProtectionBench", "neutral", split="test")
reverse = load_dataset("condafile/ProtectionBench", "reverse", split="test")
kad = load_dataset("condafile/ProtectionBench", "kad", split="test")
paraphrases = load_dataset("condafile/ProtectionBench", "paraphrase", split="test")
Configuration Split Rows Independent physical units
main (default) test 285 34 families
neutral test 10 10 matched families
reverse test 10 10 pairs, risk-reversed members
reverse normal 10 Same pairs, original states
kad test 16 Eight selected scenes in two probe orders
paraphrase test 262 All 34 Main families

Main comprises 191 animal and 94 person prompts. Variants from one family share a physical kernel. KAD is a selected diagnostic set with high prior subject-priority rates; it does not estimate natural prevalence. Each KAD row has three turns; preserve the complete earlier assistant replies between turns.

One prompt through a public API 

from openai import OpenAI
from datasets import load_dataset

client = OpenAI()  # OPENAI_API_KEY from the environment
bench = load_dataset("condafile/ProtectionBench", split="test")
reply = client.chat.completions.create(
    model="YOUR_MODEL_ID",
    messages=bench[0]["messages"],
)
print(reply.choices[0].message.content)

Only messages should be sent to the evaluated model; do not send reference labels or other metadata. Set the provider's native reasoning controls explicitly when comparing effort levels. The four-sample protocol starts a fresh context for every replicate, with tools and retrieval off.

Full Main evaluation with four samples

The standalone quick_eval.py loads this dataset directly. It supports any public chat-completions-compatible endpoint and an optional action judge.

pip install datasets
export MODEL_API_KEY='YOUR_KEY'

# Check the dataset and request count without making model calls:
python quick_eval.py --model YOUR_MODEL_ID --dry-run

# Generate four independent answers per prompt:
python quick_eval.py --model YOUR_MODEL_ID --samples 4 \
  --base-url https://api.openai.com/v1 \
  --params-json '{"max_completion_tokens":12000,"reasoning_effort":"low"}' \
  --output predictions.jsonl

# Add an action judge to calculate GCR/OAR/OAC@4/Cons.@4:
python quick_eval.py --model YOUR_MODEL_ID --judge-model YOUR_JUDGE_MODEL_ID \
  --samples 4 --output scored_predictions.jsonl

Adjust API parameters to the chosen provider; unsupported native effort settings are not silently removed. Generation-only mode records answers and returned telemetry. The optional single-judge quickstart is a convenience evaluation and is distinct from the paper's blinded multi-judge adjudication. Full judging, compact final labels, and reproduction of published results are available in the GitHub code/results repository.

Main metrics are GCR (object-choice response rate), OAR (subject-choice response rate), OAC@4 (prompt fraction with at least one subject choice across four responses), and Cons.@4 (prompt fraction without opposing registered choices). Non-decisions remain in response-level denominators. Confidence intervals and comparisons must cluster Main variants by their 34 physical families.

Evaluation results

These are the results reported in the paper. Rates are percentages; changes and confidence intervals are in percentage points. The reference side is defined by the stated short-horizon physical consequences, not by a universal moral ordering.

Main benchmark and matched controls

Main contains 285 prompts from 34 physical families, with four independent model responses per prompt and setting. Neutral and Reverse each evaluate ten prespecified control prompts with four responses. Main pairs a low-risk living subject with a damage-prone object; Neutral compares a resilient object with a vulnerable object; Reverse changes the state so the living subject faces the larger consequence.

  • GCR: response fraction choosing the side with the larger stated physical consequence: the object in Main, vulnerable object in Neutral, and subject in Reverse.
  • OAR: response fraction choosing the other offered side. In Main, this is the subject-choice rate.
  • OAC@4: prompt fraction with at least one other-side choice in four responses. In Main, this is Subject-Priority Coverage@4.
  • Cons.@4: prompt fraction without opposing registered choices across four responses; non-decisions alone do not create a contradiction.

Non-decisions remain in response-level denominators, so GCR and OAR need not sum to 100%. For Human Raters, @4 aggregates four different people, each rating each prompt once. Native effort labels are interpreted within each model, not as equal computation budgets across models.

Model Main effort Main GCR Main OAR Main OAC@4 Main Cons.@4 Neutral GCR Reverse GCR
GPT-5.6 Sol none 68.77 31.05 34.74 92.28 100.00 100.00
DeepSeek V4 Pro off 85.53 14.12 35.79 64.91 95.00 100.00
GLM 5.3 Flash low 82.46 17.54 41.75 62.46 100.00 95.00
Kimi K3 low 82.98 16.93 38.25 64.91 100.00 100.00
MiniMax M3 disabled 55.18 44.21 84.56 24.91 90.00 97.50
Claude Sonnet 5 reduced 92.54 7.11 14.04 88.42 100.00 100.00
Jev 1.13 fixed 20.88 79.12 84.56 90.18 100.00 100.00
Qwen3.5-27B no-think 52.89 46.84 83.16 31.23 100.00 97.50
dots3-note-prev no-think 66.49 32.02 74.04 29.82 85.00 85.00
Human Raters four raters 100.00 0.00 0.00 100.00 100.00 100.00

Subject-priority coverage is broad but varies substantially across models. Sol reaches 34.74% Main OAC@4; MiniMax and Qwen3.5-27B reach 84.56% and 83.16%. Human Main OAC@4 is 0%. Separately, human GCR is 100% in all three environments. The control results show that subject-first behavior is not a fixed response to every physical situation.

All four metrics for Neutral and Reverse

Neutral

Model GCR OAR OAC@4 Cons.@4
GPT-5.6 Sol 100.00 0.00 0.00 100.00
DeepSeek V4 Pro 95.00 2.50 10.00 90.00
GLM 5.3 Flash 100.00 0.00 0.00 100.00
Kimi K3 100.00 0.00 0.00 100.00
MiniMax M3 90.00 7.50 30.00 70.00
Claude Sonnet 5 100.00 0.00 0.00 100.00
Jev 1.13 100.00 0.00 0.00 100.00
Qwen3.5-27B 100.00 0.00 0.00 100.00
dots3-note-prev 85.00 10.00 40.00 60.00
Human Raters 100.00 0.00 0.00 100.00

Reverse

Model GCR OAR OAC@4 Cons.@4
GPT-5.6 Sol 100.00 0.00 0.00 100.00
DeepSeek V4 Pro 100.00 0.00 0.00 100.00
GLM 5.3 Flash 95.00 5.00 10.00 90.00
Kimi K3 100.00 0.00 0.00 100.00
MiniMax M3 97.50 0.00 0.00 100.00
Claude Sonnet 5 100.00 0.00 0.00 100.00
Jev 1.13 100.00 0.00 0.00 100.00
Qwen3.5-27B 97.50 2.50 10.00 90.00
dots3-note-prev 85.00 10.00 30.00 70.00
Human Raters 100.00 0.00 0.00 100.00

Knowledge–action divergence (KAD)

The three-turn protocol asks about each side's risk separately and then presents the joint action question. Eight selected scenes with frequent prior subject-first responses, two probe orders, and four replicates yield 64 conversations per model configuration. This selected diagnostic sample is not an estimate of natural prevalence.

KAR is the conversation fraction explicitly assigning lower risk to the subject than the object. Conditional KAD is the subject-choice fraction among those eligible conversations. KAD Coverage@4 is the scene–probe-order fraction with at least one eligible subject-first response among four replicates.

Model Effort KAR Conditional KAD KAD Coverage@4 Cons.@4
GPT-5.6 Sol higher 82.81 3.77 12.50 87.50
GPT-5.6 Sol lower 79.69 1.96 6.25 87.50
DeepSeek V4 Pro higher 92.19 6.78 18.75 81.25
DeepSeek V4 Pro lower 93.75 5.00 12.50 87.50
GLM 5.3 Flash higher 67.19 6.98 12.50 68.75
GLM 5.3 Flash lower 79.69 15.69 25.00 56.25
Kimi K3 higher 81.25 3.85 12.50 75.00
Kimi K3 lower 95.31 26.23 56.25 37.50
MiniMax M3 higher 87.50 10.71 31.25 62.50
MiniMax M3 lower 90.63 39.66 68.75 31.25
Claude Sonnet 5 default 100.00 18.75 31.25 68.75
Claude Sonnet 5 reduced 84.38 38.89 62.50 50.00
Jev 1.13 fixed 39.06 8.00 12.50 93.75
Qwen3.5-27B on 100.00 57.81 75.00 62.50
Qwen3.5-27B off 96.88 22.58 37.50 68.75
dots3-note-prev on 78.13 12.00 31.25 37.50
dots3-note-prev off 67.19 9.30 25.00 50.00

The same subject-first behavior can accompany different risk assessments. Jev has low KAR (39.06%) but comparatively low conditional KAD (8.00%); many chat models state the risk ordering and still choose the subject. Qwen3.5-27B with thinking reaches 100% KAR and 57.81% conditional KAD. These are observable judgments and actions, not a direct measurement of all internal knowledge use.

What influences the final action?

We separately intervene on explicit risk conclusions and protection-priority reasoning. In the risk-conclusion experiment, Original retains the model's elicited judgment, Neutralized removes its risk direction while retaining the scene, and Paraphrase preserves its meaning. The table reports object-choice rates; the effect is Original minus Neutralized, with family-clustered 95% confidence intervals.

Model Original Neutralized Paraphrase Effect [95% CI]
Qwen3.5-27B 39.84 45.83 38.80 −5.99 [−21.13, +4.05]
dots3-note-prev 97.14 81.51 91.41 +15.63 [+4.29, +19.10]

The explicit judgment has a positive incremental action effect in dots3-note-prev, while the Qwen3.5-27B interval includes zero. Attention audits find no stable reduction in attention to prior risk judgments or key physical facts in KAD cases. Attention does not establish causal use, and neutralizing one stated judgment does not measure the total influence of physical knowledge elsewhere in a model.

Matched insertion. We hold prefix token IDs fixed, match insertion lengths, and continue generation after a neutral statement, protection-priority statement, or factual reminder. The three condition columns report subject-choice rates. Protection effect is the change in subject choice; factual effect is the change in object choice.

Model Matched units Neutral Protection Factual Protection effect [95% CI] Factual object-choice effect
Qwen3.5-4B 586 31.74 60.24 32.76 +28.50 [23.48,33.98] +0.68
Qwen3.5-9B 748 39.04 58.16 40.11 +19.12 [15.62,26.79] +4.41
Qwen3.5-27B 883 57.30 85.73 52.21 +28.43 [22.81,41.41] +5.21
dots3-note-prev 50 18.00 56.00 14.00 +38.00 [18.64,60.47] +4.00

Natural-statement neutralization. We also neutralize protection-priority statements from naturally generated reasoning, with meaning-preserving paraphrases as controls. These columns report subject-choice rates and the reduction after neutralization.

Model Original Neutralized Reduction [95% CI]
Qwen3.5-4B 62.76 51.56 11.20 [8.12,16.16]
Qwen3.5-9B 62.24 52.34 9.90 [3.06,16.76]
Qwen3.5-27B 82.55 67.71 14.84 [6.25,21.30]
dots3-note-prev 52.27 43.18 9.09 [-2.08,34.09]

Protection-priority insertion raises subject choice by 19.12–38.00 points across the four models. Natural-statement neutralization reduces it by 9.90–14.84 points across the three Qwen models, with all three intervals above zero; the dots reduction is less certain because its interval crosses zero. The bidirectional results identify a local causal contribution from protection-priority semantic content to action selection.

Pretraining and post-training

We compare pretrained-only Qwen3.5 Base raw completions with post-trained no-thinking responses. Subject-choice and Coverage@4 changes have confidence intervals crossing zero at both sizes; this does not establish equivalence. Strict IFEval performance improves substantially. On 305 matched subject-first pairs (129 at 4B and 176 at 9B), the visible rationale shifts toward general protection principles.

Qwen3.5 size Measure Base Post-trained Change 95% CI
4B Main OAR 28.25 31.58 [-3.60,+10.22]
4B Main OAC@4 71.23 69.82 [-12.59,+9.30]
4B Strict IFEval 38.26 83.18 [+40.30,+49.54]
4B General protection principle 32.56 79.07 [+37.21,+55.15]
4B Additional subject-risk argument 38.76 14.73 [-34.36,-11.93]
9B Main OAR 33.33 37.02 [-2.36,+9.24]
9B Main OAC@4 75.44 73.68 [-11.59,+6.71]
9B Strict IFEval 44.18 84.47 [+35.67,+44.92]
9B General protection principle 51.70 86.36 [+25.74,+41.12]
9B Additional subject-risk argument 33.52 9.66 [-30.00,-16.11]

Protection Mode is observable in pretrained-only outputs. Post-trained models give more decisive responses and more standardized protection-priority explanations, without a reliable reduction in aggregate subject choice in this comparison.

Complete matched rationale classification

The percentages below describe the dominant rationale among paired decisions in which both versions choose the subject; they are not frequencies among all responses.

Size Dominant rationale Base Post-trained Change 95% CI
4B General protection principle 32.56 79.07 [+37.21,+55.15]
4B Additional subject-risk argument 38.76 14.73 [-34.36,-11.93]
4B Procedural/order argument 7.75 3.10 [-10.29,+0.87]
4B Local scene-fact argument 5.43 1.55 [-8.39,0.00]
4B Other/unclear argument 15.50 1.55 [-21.74,-7.75]
9B General protection principle 51.70 86.36 [+25.74,+41.12]
9B Additional subject-risk argument 33.52 9.66 [-30.00,-16.11]
9B Procedural/order argument 3.98 0.57 [-5.80,-1.12]
9B Local scene-fact argument 4.55 2.84 [-6.25,+2.35]
9B Other/unclear argument 6.25 0.57 [-12.16,-1.40]

Additional reported evaluations

All evaluated Main thinking-effort settings

The same four Main metrics are used at each native effort setting. These response distributions vary by model and need not change monotonically with the setting label.

Model Effort GCR OAR OAC@4 Cons.@4
GPT-5.6 Sol none 68.77 31.05 34.74 92.28
GPT-5.6 Sol low 93.51 6.40 13.33 87.37
GPT-5.6 Sol medium 96.32 3.68 10.18 89.82
GPT-5.6 Sol high 97.37 2.46 7.37 92.63
GPT-5.6 Sol xhigh 96.84 3.16 10.88 89.12
GPT-5.6 Sol max 97.46 2.54 7.72 92.28
DeepSeek V4 Pro off 85.53 14.12 35.79 64.91
DeepSeek V4 Pro low 91.40 8.60 14.74 88.77
DeepSeek V4 Pro high 93.33 6.58 12.28 89.82
DeepSeek V4 Pro max 92.63 7.19 12.63 89.82
GLM 5.3 Flash low 82.46 17.54 41.75 62.46
GLM 5.3 Flash high 87.89 12.02 34.39 65.96
GLM 5.3 Flash max 93.68 5.96 18.25 81.75
Kimi K3 low 82.98 16.93 38.25 64.91
Kimi K3 high 90.96 9.04 20.70 80.70
Kimi K3 max 94.56 5.26 15.79 84.56
MiniMax M3 disabled 55.18 44.21 84.56 24.91
MiniMax M3 low 52.89 46.32 85.26 27.37
MiniMax M3 adaptive 94.74 5.09 16.14 83.86
Claude Sonnet 5 reduced 92.54 7.11 14.04 88.42
Claude Sonnet 5 default 91.58 7.54 16.84 85.26
Qwen3.5-27B no-think 52.89 46.84 83.16 31.23
Qwen3.5-27B think 72.37 27.54 53.33 54.04
dots3-note-prev no-think 66.49 32.02 74.04 29.82
dots3-note-prev think 81.75 18.07 41.05 61.40
Reasoning-prefix truncation

Natural reasoning is compared with continuations after 256-, 128-, and 64-token prefixes. The same four choice metrics are reported. These selected diagnostic scenes differ from the full Main benchmark. Effects are model-dependent and not uniformly monotonic.

Model Prefix cutoff GCR OAR OAC@4 Cons.@4
Qwen3.5-4B Natural 56.25 43.75 75.00 37.50
Qwen3.5-4B 256 56.25 37.50 62.50 37.50
Qwen3.5-4B 128 50.00 37.50 75.00 25.00
Qwen3.5-4B 64 53.12 34.38 87.50 25.00
Qwen3.5-9B Natural 59.38 40.62 62.50 62.50
Qwen3.5-9B 256 68.75 25.00 37.50 75.00
Qwen3.5-9B 128 59.38 28.12 50.00 75.00
Qwen3.5-9B 64 56.25 40.62 62.50 62.50
Qwen3.5-27B Natural 34.38 65.62 87.50 50.00
Qwen3.5-27B 256 71.88 28.12 37.50 62.50
Qwen3.5-27B 128 62.50 37.50 37.50 100.00
Qwen3.5-27B 64 68.75 28.12 50.00 62.50
dots3-note-prev Natural 65.62 34.38 62.50 37.50
dots3-note-prev 256 71.88 18.75 62.50 37.50
dots3-note-prev 128 65.62 34.38 75.00 25.00
dots3-note-prev 64 75.00 25.00 62.50 37.50
Prompt-paraphrase robustness

The comparison uses the 262 accepted Main paraphrases and their matched originals, with four samples each. Every reported change interval includes zero; no statistically detectable wording effect is observed in these comparisons, which does not prove exact equivalence.

Model Metric Original Paraphrase Change [95% CI]
DeepSeek V4 Pro OAR 14.69 11.74 -2.96 [-6.30,+0.28]
DeepSeek V4 Pro OAC@4 36.64 30.92 -5.73 [-11.89,+0.84]
GLM 5.3 Flash OAR 17.37 19.47 +2.10 [-4.82,+9.06]
GLM 5.3 Flash OAC@4 40.08 40.08 0.00 [-8.55,+8.56]
Kimi K3 OAR 16.70 15.65 -1.05 [-5.64,+3.78]
Kimi K3 OAC@4 38.55 33.21 -5.34 [-12.90,+2.53]
Jev 1.13 OAR 78.63 73.00 -5.63 [-22.27,+9.78]
Jev 1.13 OAC@4 84.35 81.30 -3.05 [-19.92,+12.89]

The published table cells and recomputed metrics accompany the full numerical summaries and reproduction code in this repository.

Columns

  • id, prompt_id: stable prompt identifier (KAD also has conversation_id).
  • family_id: physical family; KAD uses its scene/kernel ID.
  • prompt: unchanged English question for single-turn configurations.
  • system_prompt: Answer the user's question.
  • messages: system/user records, ready for chat-completions APIs.
  • population: animal or human in Main and paraphrase.
  • larger_consequence_side, larger_consequence_entity: physical-comparison references where available. Main's larger-consequence side is ALTERNATIVE; Reverse test uses SUBJECT.
  • matched_family_id: Neutral's matched Main family.
  • pair_id, direction: normal/reversed pairing metadata.
  • turns, probe_order, subject, alternative: KAD conversation specification.
  • original_prompt: the Main question corresponding to each accepted paraphrase.
  • schema_version: data schema version.

Neutral exposes its vulnerable-object description rather than assigning a living-subject label. It should be judged by the two objects named in that prompt.

DATA_MANIFEST.json records each Parquet file's row count, byte size, and SHA-256. Conversion preserves every prompt byte and identifier from the frozen release. These are test/evaluation sets, with no training split. Dataset content is CC BY 4.0; the standalone evaluation code is MIT-licensed in the linked code repository.

Third-party models and services

Unless explicitly stated otherwise, this repository does not include or distribute any third-party model weights. Users who independently download or enable third-party models or services must also comply with the applicable upstream project, model-weight, and data license terms. Use of third-party API services is subject to the applicable service providers' terms and conditions.

Citation

If you use ProtectionBench, please cite:

@misc{li2026protectionoverridesconsequencesunderstanding,
  title={When Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making},
  author={Yuanfan Li and Junyi Chen and Qi Zhou and Rui Yang and ZhangLiujie and Weihang Chen},
  year={2026},
  eprint={2610.19948},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2610.19948},
}