Dense Model Verification

Worker-Verifier Method

Worker Evidence

The worker performs normal decode generation and records the following evidence:

input_ids
output_ids
worker_selected_logprob[d]
worker_selected_rank[d]
worker_topk_logprobs[d]

Here, d is the output token depth. The worker generation stage can use greedy decoding or sampling. In the main experiments, we use:

temperature = 0.0
top_p = 1.0
top_k = 0
max_new_tokens = 128

Verifier Replay

The verifier does not regenerate text. Instead, it fixes the path submitted by the worker:

full_prompt_ids = input_ids + output_ids

It then uses vLLM prompt logprobs to perform full-prefill replay. For output token output_ids[d], the verifier alignment position is:

prompt_pos = len(input_ids) + d

Therefore, the comparison is between the model distributions produced by the worker and verifier on the exact same token path, rather than a text similarity comparison between two freely generated outputs.

Logprob Metrics

This paper mainly uses the following metrics:

It is important to emphasize that these experiments compare selected/top-k logprobs exposed by vLLM, not full-vocabulary raw logits.

Dense Model Experiments

For dense models, the GPU comparison is mainly in the logprob verifier dimension: Qwen3-32B covers 6000ws and H100 NVL verifiers, while Qwen3-8B covers 4090 and L4 verifiers. Therefore, the dense-model conclusion compares not only model and quantization differences, but also whether the same-model logprob baseline remains stable across GPUs.

Qwen3-32B Setup

The Qwen3-32B dense experiment uses 300 prompts covering the following input token buckets:

32, 128, 512, 2048, 8192, 16384

Each bucket has 50 prompts. The worker is:

Qwen/Qwen3-32B BF16
GPU: 6000ws
backend: vLLM 0.23.0
batch size: 8
logprobs: 64
output rows: 36882

The verifiers cover:

• Qwen3-32B BF16
• Qwen3-32B-FP8
• Qwen3-32B-AWQ
• Qwen3-14B BF16
• Qwen3-8B BF16
• verifier GPUs: 6000ws and H100 NVL

Qwen3-32B Results

Using 6000ws worker -> 6000ws Qwen3-32B BF16 verifier as the baseline:

The results show that same-model BF16 forms a narrow valid drift envelope on both 6000ws and H100 NVL. FP8 already deviates significantly, while AWQ and smaller models deviate even more strongly. Therefore, for dense models such as Qwen3-32B, selected/top-k logprob traces form an effective model identity fingerprint.

Qwen3-8B Results

The Qwen3-8B experiment uses 200 prompts covering the following input buckets:

32, 128, 512, 2048

The worker is Qwen3-8B BF16 on a 4090 GPU. The verifiers cover 4090 and L4, and include Qwen3-8B BF16, Qwen3-8B-FP8, Qwen3-8B-AWQ, and Qwen2/Qwen2.5-7B models.

The Qwen3-8B results further confirm that dense models have a narrow same-model BF16 baseline, small cross-GPU differences, and clear deviations for FP8/AWQ/other models. Based on these results, reasonably clear batch-level pass/reject thresholds can be defined, for example:

BF16 pass:
mean_abs_logprob_diff <= 0.015
p95_abs_logprob_diff <= 0.090
p99_abs_logprob_diff <= 0.200
rank_delta_rate <= 0.012
topk_jaccard_mean >= 0.940
union_js_p99 <= 0.012

Reject:
mean_abs_logprob_diff > 0.020
p95_abs_logprob_diff > 0.120
p99_abs_logprob_diff > 0.250
rank_delta_rate > 0.030
topk_jaccard_mean < 0.920
union_js_p99 > 0.020

Limitations

1. The logprob experiments use selected/top-k logprobs exposed by vLLM, not full-vocabulary raw logits.
2. The output length limit is mainly 128 tokens; long-output settings require separate calibration.
3. The main Dense 32B experiment uses 6000ws worker evidence and does not cover every worker/verifier direction.
4. MoE logprobs already fail to form a clean and robust decision boundary in the same-GPU setting, so cross-GPU logprob verification is not a key missing experiment required for the paper's conclusion. MoE cross-GPU stability should mainly be verified through routed_experts, especially the layer-0 route fingerprint.
5. routed_experts experiments already cover 6000ws/H100 cross-GPU comparisons. For production deployment, the vLLM version, prefix cache, batching policy, and routed_experts return format should still be fixed or calibrated.
6. This paper studies statistical identity verification, not a cryptographic proof.