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---
# Encoder/decoder trial: encoder-marginal report
- Dataset: `G-reen/encoder-decoder-trial-stat`
- Rows analysed: 122,933 (every kept (encoder, decoder, source row) triple; source `G-reen/cc-re-2021-filtered` shard 0, 2000 rows of at most 4000 words)
- Prompt file: `prompts/indirect_reference_dataset_train.json` (turn 0 encodes the document, turn 1 reconstructs it from the encoding alone)
- Encoders: 9 (granite-4.2-30b-nvfp4 [0], Ornith-1.5-35B-A3B-NVFP4 [1], Llama-3.3-70B-Instruct-NVFP4 [2], Qwen3.8-27B-AWQ-INT4 [3], Mistral-Small-4-119B-2603-NVFP4 [4], gemma-4-31B-it-AWQ-4bit [5], Laguna-S-2.1-NVFP4 [6], claude-sonnet-5 [8], gpt-5.6-terra [9])
- Decoders: 7 (granite-4.2-30b-nvfp4 [0], Ornith-1.5-35B-A3B-NVFP4 [1], Llama-3.3-70B-Instruct-NVFP4 [2], Qwen3.8-27B-AWQ-INT4 [3], Mistral-Small-4-119B-2603-NVFP4 [4], gemma-4-31B-it-AWQ-4bit [5], Laguna-S-2.1-NVFP4 [6])
Models (trial index: config):
- 0: `config/gen/train/shard_0.toml`
- 1: `config/gen/train/shard_1.toml`
- 2: `config/gen/train/shard_2.toml`
- 3: `config/gen/train/shard_3.toml`
- 4: `config/gen/train/shard_4.toml`
- 5: `config/gen/train/shard_5.toml`
- 6: `config/gen/train/shard_6.toml`
- 7: `config/gen/train/shard_7.toml` (decode only)
- 8: `config/encdec/claude_sonnet_5.toml` (encode only)
- 9: `config/encdec/gpt_5_6_terra.toml` (encode only)
Statistics:
- every configured statistic was present.
## Summary: per-encoder means, marginalised over decoders
Every encoder encoded the same 2000 rows and every decoder decoded all of every encoder's encodings (the same source rows for every encoder), so each encoder's row is an average over the same decoders and source texts. Values are the decoder-balanced means (mean of the per-decoder means); `Rows` counts the kept rows behind each.
For reference, the human source texts score 0.0615 (std 0.1016) on the same EditLens model.
| Encoder | Editlens Score | Editlens Bucket | Cosdist | Jaccard 1 | Levenshtein | Softngram | Bertscore | Moverscore | Reranker | Rows |
|---|---|---|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5099 | 1.4517 | 0.1582 | 0.6346 | 2105.3807 | 0.5588 | 0.1327 | 0.5235 | -5.1181 | 13,601 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.4636 | 1.2975 | 0.1559 | 0.6168 | 2042.6838 | 0.5054 | 0.1265 | 0.5114 | -5.1840 | 13,615 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5696 | 1.6533 | 0.2060 | 0.6611 | 2132.4408 | 0.5877 | 0.1448 | 0.5472 | -3.3607 | 13,723 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.5102 | 1.4585 | 0.1580 | 0.6467 | 2065.3474 | 0.5613 | 0.1336 | 0.5289 | -5.2792 | 13,639 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5443 | 1.5684 | 0.1761 | 0.6657 | 2237.7226 | 0.5832 | 0.1415 | 0.5456 | -4.6743 | 13,731 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5160 | 1.4680 | 0.1720 | 0.6694 | 2073.2000 | 0.5851 | 0.1410 | 0.5459 | -4.9291 | 13,509 |
| Laguna-S-2.1-NVFP4 [6] | 0.5377 | 1.5442 | 0.1788 | 0.6693 | 2309.0983 | 0.6054 | 0.1424 | 0.5484 | -4.5894 | 13,662 |
| ❗ claude-sonnet-5 [8] | 0.4449 | 1.2363 | 0.1315 | 0.6124 | 2086.9193 | 0.5250 | 0.1214 | 0.5002 | -5.8408 | 13,732 |
| gpt-5.6-terra [9] | 0.4816 | 1.3664 | 0.1365 | 0.6467 | 2231.6794 | 0.5662 | 0.1312 | 0.5254 | -5.7724 | 13,721 |
✔️ marks the highest EditLens score (most AI-like reconstructions), ❗ the lowest.
Kept rows per encoder x decoder (after the decoders' post-processing):
| Encoder \ Decoder | Granite-4.2-30B-Nvfp4 [0] | Ornith-1.5-35B-A3B-Nvfp4 [1] | Llama-3.3-70B-Instruct-Nvfp4 [2] | Qwen3.8-27B-Awq-Int4 [3] | Mistral-Small-4-119B-2603-Nvfp4 [4] | Gemma-4-31B-It-Awq-4Bit [5] | Laguna-S-2.1-Nvfp4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 1,936 | 1,940 | 1,979 | 1,929 | 1,946 | 1,945 | 1,926 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 1,920 | 1,947 | 1,970 | 1,946 | 1,945 | 1,959 | 1,928 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 1,935 | 1,964 | 1,984 | 1,969 | 1,952 | 1,974 | 1,945 |
| Qwen3.8-27B-AWQ-INT4 [3] | 1,934 | 1,959 | 1,974 | 1,921 | 1,958 | 1,960 | 1,933 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 1,939 | 1,960 | 1,982 | 1,963 | 1,971 | 1,970 | 1,946 |
| gemma-4-31B-it-AWQ-4bit [5] | 1,901 | 1,939 | 1,966 | 1,924 | 1,939 | 1,925 | 1,915 |
| Laguna-S-2.1-NVFP4 [6] | 1,936 | 1,948 | 1,981 | 1,954 | 1,953 | 1,963 | 1,927 |
| claude-sonnet-5 [8] | 1,942 | 1,959 | 1,987 | 1,965 | 1,957 | 1,970 | 1,952 |
| gpt-5.6-terra [9] | 1,950 | 1,957 | 1,982 | 1,959 | 1,970 | 1,964 | 1,939 |
Decoder post-processing:
- Decoder 0 (`granite-4.2-30b-nvfp4`): 17,393 kept / 603 trashed of 17,996; last pass decoded 9,000 rows; failed requests 6; runtime 4.2 h (rejection reasons, last pass: empty or too short: 83, refusal: 12, filler output: 4, unfilled placeholder: 93, task meta-commentary: 14, echoed instruction: 10, identical to source: 18)
- Decoder 1 (`Ornith-1.5-35B-A3B-NVFP4`): 17,573 kept / 423 trashed of 17,996; last pass decoded 15,296 rows; failed requests 2; runtime 2.2 h (rejection reasons, last pass: empty or too short: 23, refusal: 71, filler output: 25, unfilled placeholder: 85, task meta-commentary: 101, echoed instruction: 2, identical to source: 62)
- Decoder 2 (`Llama-3.3-70B-Instruct-NVFP4`): 17,805 kept / 191 trashed of 17,996; last pass decoded 15,296 rows; failed requests 2; runtime 9.7 h (rejection reasons, last pass: empty or too short: 45, refusal: 19, filler output: 3, unfilled placeholder: 53, task meta-commentary: 8, echoed instruction: 14, identical to source: 36)
- Decoder 3 (`Qwen3.8-27B-AWQ-INT4`): 17,530 kept / 466 trashed of 17,996; last pass decoded 9,000 rows; failed requests 4; runtime 5.9 h (rejection reasons, last pass: empty or too short: 85, refusal: 29, filler output: 4, unfilled placeholder: 69, task meta-commentary: 26, echoed instruction: 3, identical to source: 23)
- Decoder 4 (`Mistral-Small-4-119B-2603-NVFP4`): 17,591 kept / 405 trashed of 17,996; last pass decoded 9,000 rows; failed requests 0; runtime 1.6 h (rejection reasons, last pass: empty or too short: 50, refusal: 2, filler output: 7, unfilled placeholder: 151, task meta-commentary: 7, echoed instruction: 2, identical to source: 21)
- Decoder 5 (`gemma-4-31B-it-AWQ-4bit`): 17,630 kept / 366 trashed of 17,996; last pass decoded 9,000 rows; failed requests 0; runtime 6.3 h (rejection reasons, last pass: empty or too short: 37, refusal: 104, filler output: 2, unfilled placeholder: 79, task meta-commentary: 24, echoed instruction: 1, identical to source: 20)
- Decoder 6 (`Laguna-S-2.1-NVFP4`): 17,411 kept / 585 trashed of 17,996; last pass decoded 9,000 rows; failed requests 5; runtime 2.4 h (rejection reasons, last pass: empty or too short: 35, refusal: 36, filler output: 5, unfilled placeholder: 117, task meta-commentary: 27, echoed instruction: 5, identical to source: 19)
## Encoding length per encoder
Length of each encoder's turn-0 output over all of its encodings, in whitespace-separated words and in characters. Empty encodings (failed requests) are counted in `Empty` and left out of the means. A run of emoji without spaces counts as one word, so the character column is given as well. For reference, the source texts average 569 words.
| Encoder | Encodings | Mean Words | Median Words | Mean Characters | Empty |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 2,000 | 417.1670 | 248.0000 | 2776.8305 | 0 |
| ✔️ Ornith-1.5-35B-A3B-NVFP4 [1] | 2,000 | 509.1720 | 287.0000 | 3291.3920 | 0 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 2,000 | 326.9910 | 208.0000 | 2079.2785 | 0 |
| Qwen3.8-27B-AWQ-INT4 [3] | 2,000 | 386.0980 | 247.0000 | 2522.8660 | 0 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 2,000 | 364.5440 | 220.5000 | 2368.4520 | 0 |
| ❗ gemma-4-31B-it-AWQ-4bit [5] | 2,000 | 314.0585 | 201.0000 | 1972.3145 | 0 |
| Laguna-S-2.1-NVFP4 [6] | 2,000 | 396.7795 | 239.0000 | 2507.4320 | 0 |
| claude-sonnet-5 [8] | 1,996 | 446.6914 | 359.0000 | 2927.3612 | 4 |
| gpt-5.6-terra [9] | 2,000 | 488.0585 | 254.5000 | 3363.1065 | 0 |
✔️ marks the longest encodings on average, ❗ the shortest.
Mean words per encoding family:
| Encoder | Descriptive | Partial | Translation | Prompt |
|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 521.5380 | 433.8340 | 571.5000 | 141.7960 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 656.6380 | 498.4680 | 708.1940 | 173.3880 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 332.6500 | 328.9360 | 589.1860 | 57.1920 |
| Qwen3.8-27B-AWQ-INT4 [3] | 422.1640 | 373.2620 | 576.4660 | 172.5000 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 434.8280 | 359.7780 | 550.2180 | 113.3520 |
| gemma-4-31B-it-AWQ-4bit [5] | 251.1440 | 297.6640 | 590.8420 | 116.5840 |
| Laguna-S-2.1-NVFP4 [6] | 388.5400 | 468.5040 | 563.9320 | 166.1420 |
| claude-sonnet-5 [8] | 501.8998 | 421.5714 | 595.3340 | 267.9200 |
| gpt-5.6-terra [9] | 895.3940 | 325.9700 | 583.7060 | 147.1640 |
## Detection: TPR at 0.1% FPR and AUROC
EditLens used as a detector. The threshold is calibrated on the whole human pool of `G-reen/cc-re-2021-filtered`: 387,327 human texts (20 shards, checkpoint pangram/editlens_roberta-large). At a 0.1% false positive budget the threshold is **0.9570**: a text counts as AI when its score is above it, which flags 0.100% of the human pool. As a check, 0.10% of the trial's own 2,000 source texts score above it. `TPR` is the share of decoded texts above the threshold; `AUROC` ranks each slice of decoded texts against the same human pool.
### Detection by encoding family
| Slice | AUROC | TPR | N |
|---|---|---|---|
| All decoded texts | 0.9155 | 0.2572 | 122,933 |
| All except translation | 0.9312 | 0.3433 | 91,515 |
| descriptive | 0.9646 | 0.4018 | 30,480 |
| partial | 0.8614 | 0.1825 | 30,909 |
| translation | 0.8701 | 0.0064 | 31,418 |
| prompt | 0.9688 | 0.4492 | 30,126 |
### Detection by encoder
`TPR Decoder Balanced` is the mean of the per-decoder TPRs.
| Encoder | AUROC | TPR | TPR Decoder Balanced | N |
|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.9162 | 0.2635 | 0.2636 | 13,601 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.9048 | 0.2174 | 0.2176 | 13,615 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.9179 | 0.3580 | 0.3582 | 13,723 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.9218 | 0.2362 | 0.2363 | 13,639 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.9219 | 0.3055 | 0.3056 | 13,731 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.9176 | 0.2593 | 0.2594 | 13,509 |
| Laguna-S-2.1-NVFP4 [6] | 0.9213 | 0.2890 | 0.2891 | 13,662 |
| ❗ claude-sonnet-5 [8] | 0.9042 | 0.1681 | 0.1683 | 13,732 |
| gpt-5.6-terra [9] | 0.9142 | 0.2178 | 0.2179 | 13,721 |
✔️ marks the highest TPR (easiest to detect), ❗ the lowest.
AUROC per encoder and encoding family:
| Encoder | Descriptive | Partial | Translation | Prompt |
|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.9638 | 0.8716 | 0.8653 | 0.9665 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.9401 | 0.8680 | 0.8642 | 0.9490 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.9823 | 0.8504 | 0.8505 | 0.9921 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.9702 | 0.8794 | 0.8796 | 0.9596 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.9777 | 0.8473 | 0.8879 | 0.9777 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.9778 | 0.8604 | 0.8709 | 0.9675 |
| Laguna-S-2.1-NVFP4 [6] | 0.9684 | 0.8754 | 0.8649 | 0.9787 |
| claude-sonnet-5 [8] | 0.9500 | 0.8378 | 0.8659 | 0.9653 |
| gpt-5.6-terra [9] | 0.9512 | 0.8632 | 0.8815 | 0.9626 |
TPR at 0.1% FPR per encoder and encoding family:
| Encoder | Descriptive | Partial | Translation | Prompt |
|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.3871 | 0.1555 | 0.0057 | 0.5176 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.3324 | 0.1452 | 0.0069 | 0.3937 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5504 | 0.2008 | 0.0060 | 0.6897 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.3961 | 0.1889 | 0.0057 | 0.3617 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.4726 | 0.1888 | 0.0046 | 0.5684 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.4395 | 0.2285 | 0.0072 | 0.3788 |
| Laguna-S-2.1-NVFP4 [6] | 0.4598 | 0.2183 | 0.0049 | 0.4830 |
| claude-sonnet-5 [8] | 0.2921 | 0.1025 | 0.0114 | 0.2721 |
| gpt-5.6-terra [9] | 0.2879 | 0.2131 | 0.0054 | 0.3716 |
### Detection by decoder
| Decoder | AUROC | TPR | N |
|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.9263 | 0.2656 | 17,393 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 0.8948 | 0.2068 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.8946 | 0.2494 | 17,805 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.9051 | 0.2400 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.9412 | 0.2901 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.9185 | 0.2303 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.9288 | 0.3193 | 17,411 |
### Detection excluding translation
The same threshold (0.9570) and human pool, with the decoded texts of the translation family left out (`detection_excluded_families` in the trial config). 91,515 of 122,933 decoded texts remain.
By encoder:
| Encoder | AUROC | TPR | TPR Decoder Balanced | N |
|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.9337 | 0.3524 | 0.3525 | 10,114 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.9188 | 0.2897 | 0.2900 | 10,133 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.9409 | 0.4783 | 0.4787 | 10,227 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.9364 | 0.3155 | 0.3156 | 10,149 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.9336 | 0.4083 | 0.4084 | 10,235 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.9339 | 0.3473 | 0.3476 | 10,013 |
| Laguna-S-2.1-NVFP4 [6] | 0.9405 | 0.3862 | 0.3864 | 10,181 |
| ❗ claude-sonnet-5 [8] | 0.9174 | 0.2217 | 0.2219 | 10,236 |
| gpt-5.6-terra [9] | 0.9253 | 0.2903 | 0.2904 | 10,227 |
✔️ marks the highest TPR (easiest to detect), ❗ the lowest.
By decoder:
| Decoder | AUROC | TPR | N |
|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.9452 | 0.3556 | 12,906 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 0.9097 | 0.2768 | 13,081 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.9260 | 0.3325 | 13,312 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.9077 | 0.3201 | 13,043 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.9491 | 0.3848 | 13,096 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.9340 | 0.3077 | 13,137 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.9467 | 0.4272 | 12,940 |
### Detection by encoding instruction
| [Family] Encoding instruction | AUROC | TPR | N |
|---|---|---|---|
| [descriptive] Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.9287 | 0.1925 | 5,227 |
| [descriptive] Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.9887 | 0.5303 | 5,029 |
| [descriptive] Reformat this text into a sensible, structured JSON object. | 0.9542 | 0.2970 | 5,192 |
| [descriptive] Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.9876 | 0.6584 | 4,787 |
| [descriptive] Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.9429 | 0.2326 | 5,181 |
| [descriptive] Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.9890 | 0.5282 | 5,064 |
| [partial] Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.9812 | 0.3075 | 6,283 |
| [partial] Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.7169 | 0.0333 | 6,073 |
| [partial] Summarize this text, attempting to preserve as much of the original language of the text a... | 0.9347 | 0.2352 | 6,196 |
| [partial] Take this text and replace one word in every four with a single underscore (use one unders... | 0.7318 | 0.0443 | 6,096 |
| [partial] Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.9351 | 0.2843 | 6,261 |
| [prompt] Create a prompt that might cause an LLM to generate an output resembling this text. | 0.9916 | 0.6094 | 6,167 |
| [prompt] Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.9787 | 0.3712 | 6,148 |
| [prompt] Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.9541 | 0.5091 | 5,806 |
| [prompt] This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.9836 | 0.4198 | 6,091 |
| [prompt] Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.9341 | 0.3348 | 5,914 |
| [translation] Translate the given text to French. | 0.8611 | 0.0141 | 7,849 |
| [translation] Translate the given text to German. | 0.8799 | 0.0081 | 7,866 |
| [translation] Translate the given text to Hindi. | 0.8852 | 0.0032 | 7,843 |
| [translation] Translate the given text to Spanish. | 0.8543 | 0.0003 | 7,860 |
## Rows filtered out per encoder
Decoded rows that the decoders' post-processing rejected, per encoder (summed over decoders). A rejected row may carry several reasons, so the reason columns can add up to more than `Trashed`. `Empty Encodings At Encode` counts the encoder's own failed requests (those rows were never sent to a decoder).
| Encoder | Decoded | Kept | Trashed | Trashed Rate | Echoed Instruction | Empty Or Too Short | Filler Output | Identical To Source | Refusal | Task Meta-Commentary | Unfilled Placeholder | Empty Encodings At Encode |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 14,000 | 13,601 | 399 | 0.0285 | 10 | 38 | 9 | 63 | 68 | 91 | 149 | 0 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 14,000 | 13,615 | 385 | 0.0275 | 17 | 82 | 12 | 70 | 59 | 72 | 98 | 0 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 14,000 | 13,723 | 277 | 0.0198 | 4 | 21 | 2 | 18 | 38 | 63 | 147 | 0 |
| Qwen3.8-27B-AWQ-INT4 [3] | 14,000 | 13,639 | 361 | 0.0258 | 3 | 31 | 24 | 144 | 37 | 66 | 79 | 0 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 14,000 | 13,731 | 269 | 0.0192 | 6 | 13 | 10 | 9 | 54 | 73 | 127 | 0 |
| gemma-4-31B-it-AWQ-4bit [5] | 14,000 | 13,509 | 491 | 0.0351 | 9 | 184 | 20 | 4 | 88 | 74 | 190 | 0 |
| Laguna-S-2.1-NVFP4 [6] | 14,000 | 13,662 | 338 | 0.0241 | 1 | 30 | 21 | 30 | 51 | 88 | 138 | 0 |
| claude-sonnet-5 [8] | 13,972 | 13,732 | 240 | 0.0172 | 4 | 15 | 20 | 4 | 24 | 58 | 129 | 4 |
| gpt-5.6-terra [9] | 14,000 | 13,721 | 279 | 0.0199 | 9 | 26 | 14 | 39 | 62 | 56 | 105 | 0 |
Trashed rows per encoder x decoder:
| Encoder \ Decoder | Granite-4.2-30B-Nvfp4 [0] | Ornith-1.5-35B-A3B-Nvfp4 [1] | Llama-3.3-70B-Instruct-Nvfp4 [2] | Qwen3.8-27B-Awq-Int4 [3] | Mistral-Small-4-119B-2603-Nvfp4 [4] | Gemma-4-31B-It-Awq-4Bit [5] | Laguna-S-2.1-Nvfp4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 64 | 60 | 21 | 71 | 54 | 55 | 74 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 80 | 53 | 30 | 54 | 55 | 41 | 72 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 65 | 36 | 16 | 31 | 48 | 26 | 55 |
| Qwen3.8-27B-AWQ-INT4 [3] | 66 | 41 | 26 | 79 | 42 | 40 | 67 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 61 | 40 | 18 | 37 | 29 | 30 | 54 |
| gemma-4-31B-it-AWQ-4bit [5] | 99 | 61 | 34 | 76 | 61 | 75 | 85 |
| Laguna-S-2.1-NVFP4 [6] | 64 | 52 | 19 | 46 | 47 | 37 | 73 |
| claude-sonnet-5 [8] | 54 | 37 | 9 | 31 | 39 | 26 | 44 |
| gpt-5.6-terra [9] | 50 | 43 | 18 | 41 | 30 | 36 | 61 |
## Contents
1. [EditLens score (higher = more AI-like) (`final_response_editlens_score_roberta_large`)](#editlens-score-higher-more-ai-like-final-response-editlens-score-roberta-large)
2. [EditLens bucket (higher = more AI-like) (`final_response_editlens_bucket_roberta_large`)](#editlens-bucket-higher-more-ai-like-final-response-editlens-bucket-roberta-large)
3. [Jaccard-1 distance to the source (`jaccard_1`)](#jaccard-1-distance-to-the-source-jaccard-1)
4. [Jaccard-2 distance to the source (`jaccard_2`)](#jaccard-2-distance-to-the-source-jaccard-2)
5. [Levenshtein distance to the source (`levenshtein`)](#levenshtein-distance-to-the-source-levenshtein)
6. [Soft n-gram distance to the source (`softngram`)](#soft-n-gram-distance-to-the-source-softngram)
7. [Embedding cosine distance to the source (`cosdist`)](#embedding-cosine-distance-to-the-source-cosdist)
8. [BERTScore distance to the source (`bertscore`)](#bertscore-distance-to-the-source-bertscore)
9. [BERTScore precision distance (`bertscore_precision`)](#bertscore-precision-distance-bertscore-precision)
10. [BERTScore recall distance (`bertscore_recall`)](#bertscore-recall-distance-bertscore-recall)
11. [MoverScore distance to the source (`moverscore`)](#moverscore-distance-to-the-source-moverscore)
12. [Reranker distance to the source (`reranker`)](#reranker-distance-to-the-source-reranker)
## Statistics
### EditLens score (higher = more AI-like) (`final_response_editlens_score_roberta_large`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5097 | 0.3686 | 0.5099 | 0.0442 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.4634 | 0.3616 | 0.4636 | 0.0513 | 13,615 | 7 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5693 | 0.3910 | 0.5696 | 0.0428 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.5100 | 0.3573 | 0.5102 | 0.0510 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5442 | 0.3744 | 0.5443 | 0.0352 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5158 | 0.3681 | 0.5160 | 0.0416 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | 0.5375 | 0.3731 | 0.5377 | 0.0396 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | 0.4447 | 0.3417 | 0.4449 | 0.0549 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.4815 | 0.3562 | 0.4816 | 0.0477 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5246 | 0.4331 | 0.4760 | 0.4990 | 0.5683 | 0.5047 | 0.5634 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.4805 | 0.3882 | 0.4221 | 0.4360 | 0.5375 | 0.4512 | 0.5297 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5895 | 0.5133 | 0.5158 | 0.5558 | 0.6326 | 0.5639 | 0.6160 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.5341 | 0.4387 | 0.4735 | 0.4758 | 0.5860 | 0.4908 | 0.5722 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5550 | 0.4919 | 0.5115 | 0.5350 | 0.5877 | 0.5332 | 0.5959 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5474 | 0.4542 | 0.4839 | 0.4957 | 0.5731 | 0.4953 | 0.5623 |
| Laguna-S-2.1-NVFP4 [6] | 0.5591 | 0.4763 | 0.5057 | 0.5270 | 0.5976 | 0.5194 | 0.5785 |
| claude-sonnet-5 [8] | 0.4777 | 0.3662 | 0.3898 | 0.4359 | 0.5195 | 0.4152 | 0.5100 |
| gpt-5.6-terra [9] | 0.5083 | 0.4054 | 0.4386 | 0.4691 | 0.5402 | 0.4666 | 0.5429 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5307 | 0.3627 | 0.5307 | 0.0350 | 17,393 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 0.4408 | 0.3623 | 0.4408 | 0.0459 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.4685 | 0.3819 | 0.4685 | 0.0409 | 17,805 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.4922 | 0.3652 | 0.4921 | 0.0398 | 17,530 |
| ✔️ Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5714 | 0.3511 | 0.5714 | 0.0329 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.4934 | 0.3584 | 0.4934 | 0.0422 | 17,630 |
| Laguna-S-2.1-NVFP4 [6] | 0.5634 | 0.3738 | 0.5634 | 0.0308 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.6573 | 0.3009 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.5166 | 0.3330 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.8406 | 0.2435 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.6947 | 0.2984 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.7360 | 0.3348 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.7971 | 0.2697 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.6119 | 0.3335 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.1625 | 0.2208 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.5316 | 0.3415 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.1916 | 0.2520 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.5736 | 0.3555 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.7343 | 0.2903 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.8412 | 0.2699 | 4,787 |
| Translate the given text to French. | 0.2129 | 0.1872 | 7,849 |
| Translate the given text to German. | 0.2175 | 0.1652 | 7,866 |
| Translate the given text to Hindi. | 0.2248 | 0.1603 | 7,843 |
| Translate the given text to Spanish. | 0.1742 | 0.1259 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.6100 | 0.3620 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.5776 | 0.3326 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.8114 | 0.2575 | 5,064 |
### EditLens bucket (higher = more AI-like) (`final_response_editlens_bucket_roberta_large`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 1.4513 | 1.2834 | 1.4517 | 0.1459 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 1.2968 | 1.2665 | 1.2975 | 0.1688 | 13,615 | 7 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 1.6525 | 1.3439 | 1.6533 | 0.1401 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 1.4582 | 1.2665 | 1.4585 | 0.1634 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 1.5681 | 1.2994 | 1.5684 | 0.1158 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 1.4674 | 1.2950 | 1.4680 | 0.1366 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | 1.5437 | 1.3019 | 1.5442 | 0.1286 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | 1.2356 | 1.2144 | 1.2363 | 0.1761 | 13,732 | 7 |
| gpt-5.6-terra [9] | 1.3660 | 1.2578 | 1.3664 | 0.1548 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 1.5052 | 1.1995 | 1.3380 | 1.4142 | 1.6434 | 1.4334 | 1.6282 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 1.3490 | 1.0508 | 1.1503 | 1.2122 | 1.5429 | 1.2660 | 1.5114 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 1.7235 | 1.4638 | 1.4788 | 1.6074 | 1.8637 | 1.6424 | 1.7933 |
| Qwen3.8-27B-AWQ-INT4 [3] | 1.5476 | 1.2297 | 1.3440 | 1.3514 | 1.7079 | 1.3852 | 1.6441 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 1.6055 | 1.4010 | 1.4470 | 1.5420 | 1.7164 | 1.5365 | 1.7302 |
| gemma-4-31B-it-AWQ-4bit [5] | 1.5786 | 1.2702 | 1.3535 | 1.4023 | 1.6596 | 1.4005 | 1.6115 |
| Laguna-S-2.1-NVFP4 [6] | 1.6105 | 1.3393 | 1.4523 | 1.5005 | 1.7373 | 1.4885 | 1.6809 |
| claude-sonnet-5 [8] | 1.3404 | 0.9837 | 1.0564 | 1.2300 | 1.4798 | 1.1294 | 1.4344 |
| gpt-5.6-terra [9] | 1.4554 | 1.1185 | 1.2200 | 1.3313 | 1.5553 | 1.3228 | 1.5616 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 1.5239 | 1.2686 | 1.5240 | 0.1187 | 17,393 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 1.2286 | 1.2696 | 1.2285 | 0.1504 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 1.3156 | 1.3528 | 1.3156 | 0.1370 | 17,805 |
| Qwen3.8-27B-AWQ-INT4 [3] | 1.3993 | 1.2700 | 1.3990 | 0.1268 | 17,530 |
| ✔️ Mistral-Small-4-119B-2603-NVFP4 [4] | 1.6562 | 1.2271 | 1.6563 | 0.1107 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 1.4006 | 1.2622 | 1.4005 | 0.1429 | 17,630 |
| Laguna-S-2.1-NVFP4 [6] | 1.6217 | 1.2995 | 1.6217 | 0.1034 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 1.9452 | 1.1293 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 1.4791 | 1.2074 | 5,227 |
| ✔️ Create a prompt that might cause an LLM to generate an output resembling this text. | 2.5465 | 0.8898 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 2.0597 | 1.1038 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 2.1988 | 1.1656 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 2.3941 | 0.9896 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 1.7987 | 1.2107 | 5,192 |
| Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.3359 | 0.7415 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 1.5350 | 1.2135 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.4221 | 0.8448 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 1.6790 | 1.2669 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 2.2033 | 1.0782 | 6,091 |
| Translate the entirety of this text into a sequence of emojis that captures the literal me... | 2.5402 | 0.9567 | 4,787 |
| Translate the given text to French. | 0.4640 | 0.7240 | 7,849 |
| Translate the given text to German. | 0.4652 | 0.6533 | 7,866 |
| Translate the given text to Hindi. | 0.4867 | 0.6452 | 7,843 |
| ❗ Translate the given text to Spanish. | 0.3095 | 0.4888 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 1.7973 | 1.2894 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 1.6889 | 1.2322 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 2.4690 | 0.9505 | 5,064 |
### Jaccard-1 distance to the source (`jaccard_1`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.6346 | 0.2601 | 0.6346 | 0.0258 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.6167 | 0.2521 | 0.6168 | 0.0279 | 13,615 | 7 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.6610 | 0.2578 | 0.6611 | 0.0161 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.6467 | 0.2441 | 0.6467 | 0.0256 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.6656 | 0.2437 | 0.6657 | 0.0157 | 13,731 | 7 |
| ✔️ gemma-4-31B-it-AWQ-4bit [5] | 0.6694 | 0.2292 | 0.6694 | 0.0160 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | 0.6693 | 0.2403 | 0.6693 | 0.0164 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | 0.6123 | 0.2314 | 0.6124 | 0.0245 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.6466 | 0.2298 | 0.6467 | 0.0181 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.6463 | 0.6076 | 0.6239 | 0.6010 | 0.6606 | 0.6260 | 0.6772 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.6278 | 0.5895 | 0.6027 | 0.5818 | 0.6492 | 0.6055 | 0.6612 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.6699 | 0.6500 | 0.6458 | 0.6459 | 0.6817 | 0.6491 | 0.6853 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.6606 | 0.6232 | 0.6317 | 0.6163 | 0.6767 | 0.6321 | 0.6867 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.6693 | 0.6566 | 0.6538 | 0.6475 | 0.6784 | 0.6580 | 0.6961 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.6770 | 0.6637 | 0.6540 | 0.6560 | 0.6861 | 0.6528 | 0.6964 |
| Laguna-S-2.1-NVFP4 [6] | 0.6762 | 0.6566 | 0.6545 | 0.6526 | 0.6890 | 0.6605 | 0.6960 |
| claude-sonnet-5 [8] | 0.6257 | 0.5912 | 0.5939 | 0.5895 | 0.6392 | 0.5939 | 0.6530 |
| gpt-5.6-terra [9] | 0.6586 | 0.6366 | 0.6353 | 0.6298 | 0.6601 | 0.6270 | 0.6793 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.6568 | 0.2384 | 0.6568 | 0.0184 | 17,393 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.6305 | 0.2512 | 0.6305 | 0.0272 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.6328 | 0.2425 | 0.6328 | 0.0212 | 17,805 |
| ❗ Qwen3.8-27B-AWQ-INT4 [3] | 0.6245 | 0.2560 | 0.6245 | 0.0268 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.6690 | 0.2330 | 0.6690 | 0.0164 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.6338 | 0.2510 | 0.6339 | 0.0221 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.6812 | 0.2307 | 0.6813 | 0.0146 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.7801 | 0.0888 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.5981 | 0.2342 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.8516 | 0.0511 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.7967 | 0.0909 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.8225 | 0.1740 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.8534 | 0.0621 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.6797 | 0.1892 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.2187 | 0.1756 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.6119 | 0.2350 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.2510 | 0.2141 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.7016 | 0.1656 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.8229 | 0.0692 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.9088 | 0.0530 | 4,787 |
| Translate the given text to French. | 0.4678 | 0.0828 | 7,849 |
| Translate the given text to German. | 0.4749 | 0.0869 | 7,866 |
| Translate the given text to Hindi. | 0.5467 | 0.0988 | 7,843 |
| Translate the given text to Spanish. | 0.4175 | 0.0952 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.8199 | 0.1645 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.7838 | 0.0908 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.8804 | 0.0349 | 5,064 |
### Jaccard-2 distance to the source (`jaccard_2`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.7686 | 0.2655 | 0.7686 | 0.0261 | 13,601 | 7 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 0.7522 | 0.2543 | 0.7523 | 0.0270 | 13,615 | 7 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.7888 | 0.2464 | 0.7889 | 0.0160 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.7862 | 0.2492 | 0.7862 | 0.0247 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.8011 | 0.2310 | 0.8012 | 0.0141 | 13,731 | 7 |
| ✔️ gemma-4-31B-it-AWQ-4bit [5] | 0.8119 | 0.2044 | 0.8120 | 0.0145 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | 0.8055 | 0.2323 | 0.8056 | 0.0149 | 13,662 | 7 |
| claude-sonnet-5 [8] | 0.7615 | 0.2245 | 0.7616 | 0.0229 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.7943 | 0.2228 | 0.7944 | 0.0159 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.7805 | 0.7394 | 0.7586 | 0.7356 | 0.7982 | 0.7589 | 0.8088 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.7624 | 0.7233 | 0.7415 | 0.7183 | 0.7869 | 0.7416 | 0.7920 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.7990 | 0.7783 | 0.7744 | 0.7760 | 0.8106 | 0.7730 | 0.8107 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.8004 | 0.7623 | 0.7725 | 0.7585 | 0.8171 | 0.7700 | 0.8226 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.8056 | 0.7942 | 0.7907 | 0.7864 | 0.8126 | 0.7904 | 0.8284 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.8201 | 0.8091 | 0.7968 | 0.8040 | 0.8258 | 0.7927 | 0.8353 |
| Laguna-S-2.1-NVFP4 [6] | 0.8115 | 0.7939 | 0.7923 | 0.7923 | 0.8253 | 0.7949 | 0.8287 |
| claude-sonnet-5 [8] | 0.7745 | 0.7422 | 0.7436 | 0.7423 | 0.7890 | 0.7420 | 0.7977 |
| gpt-5.6-terra [9] | 0.8053 | 0.7868 | 0.7846 | 0.7812 | 0.8078 | 0.7738 | 0.8212 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.7955 | 0.2293 | 0.7955 | 0.0178 | 17,393 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.7699 | 0.2493 | 0.7699 | 0.0279 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.7728 | 0.2373 | 0.7728 | 0.0196 | 17,805 |
| ❗ Qwen3.8-27B-AWQ-INT4 [3] | 0.7661 | 0.2594 | 0.7661 | 0.0273 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.8081 | 0.2196 | 0.8081 | 0.0135 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.7708 | 0.2469 | 0.7708 | 0.0191 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.8161 | 0.2168 | 0.8162 | 0.0139 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.9255 | 0.0667 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.7426 | 0.2499 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.9673 | 0.0266 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.9395 | 0.0595 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.9245 | 0.1811 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.9635 | 0.0383 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.8286 | 0.2053 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.3124 | 0.2106 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.7485 | 0.2589 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.3533 | 0.2535 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.8228 | 0.1761 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.9530 | 0.0421 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.9851 | 0.0361 | 4,787 |
| Translate the given text to French. | 0.6669 | 0.0862 | 7,849 |
| Translate the given text to German. | 0.6758 | 0.0859 | 7,866 |
| Translate the given text to Hindi. | 0.7480 | 0.0904 | 7,843 |
| Translate the given text to Spanish. | 0.6132 | 0.1066 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.9063 | 0.1566 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.9089 | 0.0773 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.9775 | 0.0155 | 5,064 |
### Levenshtein distance to the source (`levenshtein`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 2105.0217 | 2445.1394 | 2105.3807 | 145.5901 | 13,601 | 7 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 2041.9071 | 2797.1727 | 2042.6838 | 141.2548 | 13,615 | 7 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 2131.9093 | 2515.1605 | 2132.4408 | 105.1155 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 2065.1839 | 2758.4968 | 2065.3474 | 121.7832 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 2237.2207 | 2992.4775 | 2237.7226 | 140.1776 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 2073.1535 | 2457.9207 | 2073.2000 | 96.0188 | 13,509 | 7 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 2308.2526 | 3898.7931 | 2309.0983 | 148.3083 | 13,662 | 7 |
| claude-sonnet-5 [8] | 2085.9876 | 4508.4155 | 2086.9193 | 226.0749 | 13,732 | 7 |
| gpt-5.6-terra [9] | 2231.2725 | 4426.4117 | 2231.6794 | 182.3787 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 2142.1364 | 2149.0258 | 2017.1107 | 1949.2789 | 2266.4157 | 1898.2540 | 2315.4434 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 2092.6406 | 2028.9692 | 1936.7614 | 1885.9527 | 2146.8653 | 1900.2323 | 2307.3651 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 2130.6997 | 2194.5234 | 2063.4793 | 2048.6953 | 2279.5835 | 1966.1864 | 2243.9177 |
| Qwen3.8-27B-AWQ-INT4 [3] | 2106.0796 | 2079.4421 | 2021.3582 | 1928.7715 | 2151.6813 | 1893.8138 | 2276.2856 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 2298.4471 | 2221.7383 | 2154.4147 | 2111.7040 | 2388.3876 | 2034.9360 | 2454.4301 |
| gemma-4-31B-it-AWQ-4bit [5] | 2073.0352 | 2081.1078 | 2048.4593 | 2002.4683 | 2186.3847 | 1909.2691 | 2211.6757 |
| Laguna-S-2.1-NVFP4 [6] | 2430.5517 | 2425.7295 | 2221.5962 | 2134.4831 | 2412.0691 | 2077.6032 | 2461.6554 |
| claude-sonnet-5 [8] | 2173.4439 | 2217.3492 | 1941.5279 | 1888.4061 | 2049.8426 | 1809.8386 | 2528.0272 |
| gpt-5.6-terra [9] | 2389.4651 | 2290.8809 | 2351.7992 | 2047.5268 | 2169.4046 | 1914.2164 | 2458.4631 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 2204.5921 | 3731.8047 | 2204.0555 | 126.4551 | 17,393 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 2187.7551 | 3970.7840 | 2187.6407 | 115.0659 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 2084.1770 | 3389.4024 | 2084.0563 | 128.0916 | 17,805 |
| Qwen3.8-27B-AWQ-INT4 [3] | 1999.9944 | 2464.4671 | 1999.6985 | 87.1174 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 2227.9455 | 2532.5306 | 2227.8483 | 112.0787 | 17,591 |
| ❗ gemma-4-31B-it-AWQ-4bit [5] | 1933.9303 | 2608.9887 | 1933.8166 | 76.2289 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 2362.2757 | 3968.4034 | 2361.9182 | 107.6955 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 2708.4195 | 2321.3652 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 2036.5969 | 2627.1115 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 3316.2526 | 2860.5408 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 2835.4305 | 2569.2781 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 2806.7494 | 2901.9416 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 3323.9753 | 3848.3830 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 2593.6308 | 3057.3767 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 430.3017 | 2691.7751 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 2146.6162 | 4226.0453 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 559.8406 | 1643.9632 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 2577.7528 | 2548.4190 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 3104.4745 | 4701.8958 | 6,091 |
| Translate the entirety of this text into a sequence of emojis that captures the literal me... | 3359.8018 | 5816.8765 | 4,787 |
| Translate the given text to French. | 946.0595 | 1025.3997 | 7,849 |
| Translate the given text to German. | 989.9259 | 1223.0919 | 7,866 |
| Translate the given text to Hindi. | 1085.5447 | 1783.5637 | 7,843 |
| Translate the given text to Spanish. | 752.6126 | 643.9184 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 2950.4307 | 4422.2077 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 2858.1764 | 5017.5288 | 5,181 |
| ✔️ Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 3750.6929 | 3652.7911 | 5,064 |
### Soft n-gram distance to the source (`softngram`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5588 | 0.3671 | 0.5588 | 0.0321 | 13,601 | 7 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 0.5053 | 0.3597 | 0.5054 | 0.0369 | 13,615 | 7 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5875 | 0.3806 | 0.5877 | 0.0236 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.5612 | 0.3529 | 0.5613 | 0.0336 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5831 | 0.3566 | 0.5832 | 0.0219 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5850 | 0.3570 | 0.5851 | 0.0245 | 13,509 | 7 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.6052 | 0.3523 | 0.6054 | 0.0232 | 13,662 | 7 |
| claude-sonnet-5 [8] | 0.5249 | 0.3538 | 0.5250 | 0.0339 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.5661 | 0.3447 | 0.5662 | 0.0280 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5715 | 0.5290 | 0.5451 | 0.5177 | 0.5862 | 0.5455 | 0.6167 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.5126 | 0.4800 | 0.4925 | 0.4518 | 0.5372 | 0.4909 | 0.5729 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.6011 | 0.5743 | 0.5717 | 0.5551 | 0.6130 | 0.5735 | 0.6250 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.5745 | 0.5373 | 0.5422 | 0.5143 | 0.5924 | 0.5482 | 0.6201 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5851 | 0.5703 | 0.5694 | 0.5560 | 0.5976 | 0.5760 | 0.6278 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5943 | 0.5784 | 0.5678 | 0.5507 | 0.6087 | 0.5686 | 0.6270 |
| Laguna-S-2.1-NVFP4 [6] | 0.6179 | 0.5907 | 0.5855 | 0.5775 | 0.6292 | 0.5929 | 0.6441 |
| claude-sonnet-5 [8] | 0.5416 | 0.4958 | 0.4988 | 0.4959 | 0.5601 | 0.4991 | 0.5838 |
| gpt-5.6-terra [9] | 0.5854 | 0.5493 | 0.5494 | 0.5377 | 0.5856 | 0.5386 | 0.6173 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5760 | 0.3595 | 0.5760 | 0.0300 | 17,393 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.5450 | 0.3682 | 0.5450 | 0.0360 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5469 | 0.3481 | 0.5469 | 0.0305 | 17,805 |
| ❗ Qwen3.8-27B-AWQ-INT4 [3] | 0.5286 | 0.3670 | 0.5285 | 0.0360 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5900 | 0.3569 | 0.5900 | 0.0262 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5481 | 0.3576 | 0.5482 | 0.0327 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.6149 | 0.3524 | 0.6150 | 0.0212 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.7411 | 0.1765 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.5552 | 0.3115 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.9115 | 0.1060 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.8202 | 0.1903 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.7770 | 0.2809 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.9276 | 0.0950 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.6376 | 0.2604 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.1051 | 0.1730 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.5004 | 0.3040 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.1732 | 0.2321 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.5652 | 0.2894 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.8686 | 0.1397 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.9783 | 0.0735 | 4,787 |
| Translate the given text to French. | 0.2061 | 0.1110 | 7,849 |
| Translate the given text to German. | 0.2029 | 0.1118 | 7,866 |
| Translate the given text to Hindi. | 0.3047 | 0.1719 | 7,843 |
| Translate the given text to Spanish. | 0.1621 | 0.0921 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.7272 | 0.3211 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.8208 | 0.1449 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.9684 | 0.0474 | 5,064 |
### Embedding cosine distance to the source (`cosdist`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1582 | 0.1651 | 0.1582 | 0.0086 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1559 | 0.1760 | 0.1559 | 0.0090 | 13,615 | 7 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.2061 | 0.2024 | 0.2060 | 0.0102 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1580 | 0.1646 | 0.1580 | 0.0090 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1762 | 0.1806 | 0.1761 | 0.0085 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1720 | 0.1695 | 0.1720 | 0.0096 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | 0.1788 | 0.1791 | 0.1788 | 0.0082 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | 0.1315 | 0.1443 | 0.1315 | 0.0094 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.1365 | 0.1465 | 0.1365 | 0.0096 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1562 | 0.1426 | 0.1717 | 0.1587 | 0.1532 | 0.1590 | 0.1661 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1524 | 0.1436 | 0.1692 | 0.1500 | 0.1588 | 0.1495 | 0.1676 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1997 | 0.1879 | 0.2225 | 0.2091 | 0.2024 | 0.2067 | 0.2139 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1571 | 0.1415 | 0.1706 | 0.1559 | 0.1615 | 0.1521 | 0.1672 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1680 | 0.1619 | 0.1890 | 0.1763 | 0.1772 | 0.1762 | 0.1845 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1721 | 0.1560 | 0.1887 | 0.1682 | 0.1740 | 0.1656 | 0.1795 |
| Laguna-S-2.1-NVFP4 [6] | 0.1751 | 0.1625 | 0.1896 | 0.1809 | 0.1802 | 0.1766 | 0.1866 |
| claude-sonnet-5 [8] | 0.1332 | 0.1157 | 0.1420 | 0.1266 | 0.1381 | 0.1226 | 0.1422 |
| gpt-5.6-terra [9] | 0.1377 | 0.1238 | 0.1501 | 0.1329 | 0.1402 | 0.1240 | 0.1470 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1612 | 0.1634 | 0.1613 | 0.0191 | 17,393 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1484 | 0.1683 | 0.1484 | 0.0205 | 17,573 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1770 | 0.1841 | 0.1770 | 0.0226 | 17,805 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1621 | 0.1730 | 0.1621 | 0.0238 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1650 | 0.1715 | 0.1651 | 0.0194 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1591 | 0.1694 | 0.1591 | 0.0250 | 17,630 |
| Laguna-S-2.1-NVFP4 [6] | 0.1727 | 0.1716 | 0.1727 | 0.0205 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.1574 | 0.0848 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.1183 | 0.0921 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.2321 | 0.1096 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.1822 | 0.1116 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.2315 | 0.1545 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.2712 | 0.1404 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.1178 | 0.0733 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.0215 | 0.0535 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.0988 | 0.0787 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.0418 | 0.0735 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.1571 | 0.0973 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.1941 | 0.0977 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.6000 | 0.1837 | 4,787 |
| Translate the given text to French. | 0.0333 | 0.0372 | 7,849 |
| Translate the given text to German. | 0.0306 | 0.0363 | 7,866 |
| Translate the given text to Hindi. | 0.0550 | 0.0635 | 7,843 |
| Translate the given text to Spanish. | 0.0319 | 0.0330 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.3234 | 0.1773 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.2848 | 0.1359 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.4019 | 0.1472 | 5,064 |
### BERTScore distance to the source (`bertscore`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1327 | 0.0748 | 0.1327 | 0.0057 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1265 | 0.0736 | 0.1265 | 0.0066 | 13,615 | 7 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1448 | 0.0786 | 0.1448 | 0.0042 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1335 | 0.0702 | 0.1336 | 0.0060 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1415 | 0.0736 | 0.1415 | 0.0042 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1410 | 0.0712 | 0.1410 | 0.0042 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | 0.1424 | 0.0726 | 0.1424 | 0.0042 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | 0.1213 | 0.0663 | 0.1214 | 0.0068 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.1312 | 0.0672 | 0.1312 | 0.0054 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1343 | 0.1259 | 0.1318 | 0.1259 | 0.1377 | 0.1305 | 0.1429 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1271 | 0.1196 | 0.1251 | 0.1186 | 0.1337 | 0.1236 | 0.1380 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1447 | 0.1402 | 0.1459 | 0.1396 | 0.1495 | 0.1421 | 0.1514 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1349 | 0.1275 | 0.1309 | 0.1270 | 0.1405 | 0.1303 | 0.1437 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1401 | 0.1373 | 0.1414 | 0.1367 | 0.1448 | 0.1406 | 0.1498 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1411 | 0.1379 | 0.1399 | 0.1367 | 0.1460 | 0.1370 | 0.1483 |
| Laguna-S-2.1-NVFP4 [6] | 0.1431 | 0.1379 | 0.1415 | 0.1377 | 0.1474 | 0.1398 | 0.1494 |
| claude-sonnet-5 [8] | 0.1237 | 0.1147 | 0.1189 | 0.1153 | 0.1283 | 0.1152 | 0.1333 |
| gpt-5.6-terra [9] | 0.1331 | 0.1274 | 0.1309 | 0.1262 | 0.1349 | 0.1247 | 0.1415 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1358 | 0.0706 | 0.1358 | 0.0067 | 17,393 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1298 | 0.0725 | 0.1298 | 0.0086 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1340 | 0.0735 | 0.1340 | 0.0083 | 17,805 |
| ❗ Qwen3.8-27B-AWQ-INT4 [3] | 0.1293 | 0.0730 | 0.1293 | 0.0083 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1403 | 0.0702 | 0.1403 | 0.0068 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1315 | 0.0731 | 0.1315 | 0.0086 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.1443 | 0.0731 | 0.1443 | 0.0057 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.1661 | 0.0365 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.1156 | 0.0562 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.1916 | 0.0285 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.1683 | 0.0382 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.1906 | 0.0575 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.1954 | 0.0325 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.1343 | 0.0454 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.0344 | 0.0385 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.1162 | 0.0549 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.0452 | 0.0461 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.1438 | 0.0473 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.1814 | 0.0321 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.2518 | 0.0558 | 4,787 |
| Translate the given text to French. | 0.0727 | 0.0219 | 7,849 |
| Translate the given text to German. | 0.0730 | 0.0234 | 7,866 |
| Translate the given text to Hindi. | 0.0900 | 0.0315 | 7,843 |
| Translate the given text to Spanish. | 0.0642 | 0.0218 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.1984 | 0.0629 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.1752 | 0.0336 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.2160 | 0.0249 | 5,064 |
### BERTScore precision distance (`bertscore_precision`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1325 | 0.0768 | 0.1325 | 0.0079 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1235 | 0.0741 | 0.1236 | 0.0087 | 13,615 | 7 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1414 | 0.0803 | 0.1414 | 0.0065 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1315 | 0.0699 | 0.1315 | 0.0084 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1392 | 0.0744 | 0.1392 | 0.0062 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1370 | 0.0708 | 0.1370 | 0.0064 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | 0.1414 | 0.0746 | 0.1414 | 0.0063 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | 0.1226 | 0.0699 | 0.1226 | 0.0088 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.1314 | 0.0693 | 0.1314 | 0.0077 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1342 | 0.1240 | 0.1309 | 0.1233 | 0.1401 | 0.1284 | 0.1466 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1238 | 0.1155 | 0.1222 | 0.1127 | 0.1320 | 0.1195 | 0.1394 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1416 | 0.1357 | 0.1418 | 0.1330 | 0.1495 | 0.1366 | 0.1516 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1331 | 0.1240 | 0.1287 | 0.1212 | 0.1402 | 0.1270 | 0.1464 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1375 | 0.1339 | 0.1381 | 0.1324 | 0.1446 | 0.1366 | 0.1514 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1368 | 0.1329 | 0.1350 | 0.1298 | 0.1444 | 0.1316 | 0.1486 |
| Laguna-S-2.1-NVFP4 [6] | 0.1423 | 0.1358 | 0.1398 | 0.1345 | 0.1486 | 0.1365 | 0.1522 |
| claude-sonnet-5 [8] | 0.1251 | 0.1140 | 0.1184 | 0.1165 | 0.1308 | 0.1145 | 0.1391 |
| gpt-5.6-terra [9] | 0.1335 | 0.1257 | 0.1300 | 0.1246 | 0.1368 | 0.1227 | 0.1464 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1342 | 0.0721 | 0.1342 | 0.0061 | 17,393 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1268 | 0.0735 | 0.1268 | 0.0078 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1316 | 0.0740 | 0.1316 | 0.0074 | 17,805 |
| ❗ Qwen3.8-27B-AWQ-INT4 [3] | 0.1253 | 0.0732 | 0.1253 | 0.0073 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1408 | 0.0730 | 0.1408 | 0.0063 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1282 | 0.0713 | 0.1282 | 0.0076 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.1468 | 0.0765 | 0.1469 | 0.0046 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.1661 | 0.0385 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.1205 | 0.0605 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.1980 | 0.0336 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.1741 | 0.0403 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.1771 | 0.0587 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.2033 | 0.0377 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.1409 | 0.0515 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.0371 | 0.0452 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.1159 | 0.0608 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.0485 | 0.0527 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.1303 | 0.0563 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.1883 | 0.0367 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.2467 | 0.0614 | 4,787 |
| Translate the given text to French. | 0.0711 | 0.0211 | 7,849 |
| Translate the given text to German. | 0.0715 | 0.0226 | 7,866 |
| Translate the given text to Hindi. | 0.0867 | 0.0297 | 7,843 |
| Translate the given text to Spanish. | 0.0621 | 0.0213 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.1751 | 0.0666 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.1651 | 0.0366 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.2148 | 0.0295 | 5,064 |
### BERTScore recall distance (`bertscore_recall`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1321 | 0.0765 | 0.1321 | 0.0036 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1283 | 0.0783 | 0.1283 | 0.0046 | 13,615 | 7 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1474 | 0.0802 | 0.1474 | 0.0021 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1346 | 0.0749 | 0.1346 | 0.0037 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1430 | 0.0767 | 0.1430 | 0.0024 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1440 | 0.0755 | 0.1440 | 0.0021 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | 0.1426 | 0.0743 | 0.1426 | 0.0022 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | 0.1195 | 0.0657 | 0.1195 | 0.0049 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.1304 | 0.0686 | 0.1304 | 0.0032 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1336 | 0.1269 | 0.1318 | 0.1279 | 0.1346 | 0.1317 | 0.1383 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1292 | 0.1226 | 0.1270 | 0.1233 | 0.1341 | 0.1266 | 0.1354 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1471 | 0.1440 | 0.1494 | 0.1452 | 0.1488 | 0.1470 | 0.1505 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1357 | 0.1302 | 0.1322 | 0.1317 | 0.1398 | 0.1327 | 0.1400 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1419 | 0.1400 | 0.1438 | 0.1403 | 0.1441 | 0.1439 | 0.1474 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1445 | 0.1421 | 0.1440 | 0.1424 | 0.1467 | 0.1416 | 0.1471 |
| Laguna-S-2.1-NVFP4 [6] | 0.1432 | 0.1393 | 0.1423 | 0.1401 | 0.1454 | 0.1423 | 0.1456 |
| claude-sonnet-5 [8] | 0.1218 | 0.1147 | 0.1189 | 0.1136 | 0.1253 | 0.1154 | 0.1268 |
| gpt-5.6-terra [9] | 0.1319 | 0.1283 | 0.1313 | 0.1270 | 0.1325 | 0.1261 | 0.1358 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.1365 | 0.0733 | 0.1365 | 0.0078 | 17,393 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | 0.1320 | 0.0750 | 0.1320 | 0.0094 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.1356 | 0.0768 | 0.1356 | 0.0093 | 17,805 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.1324 | 0.0768 | 0.1324 | 0.0098 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.1390 | 0.0715 | 0.1390 | 0.0074 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.1341 | 0.0780 | 0.1341 | 0.0098 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.1407 | 0.0738 | 0.1408 | 0.0071 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.1653 | 0.0420 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.1100 | 0.0565 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.1846 | 0.0300 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.1621 | 0.0399 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.2018 | 0.0669 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.1865 | 0.0357 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.1270 | 0.0452 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.0314 | 0.0339 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.1154 | 0.0563 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.0413 | 0.0428 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.1555 | 0.0498 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.1737 | 0.0332 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.2558 | 0.0558 | 4,787 |
| Translate the given text to French. | 0.0742 | 0.0235 | 7,849 |
| Translate the given text to German. | 0.0744 | 0.0252 | 7,866 |
| Translate the given text to Hindi. | 0.0930 | 0.0345 | 7,843 |
| Translate the given text to Spanish. | 0.0662 | 0.0234 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.2188 | 0.0693 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.1839 | 0.0418 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.2165 | 0.0312 | 5,064 |
### MoverScore distance to the source (`moverscore`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5235 | 0.1911 | 0.5235 | 0.0171 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.5114 | 0.1890 | 0.5114 | 0.0182 | 13,615 | 7 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5472 | 0.1958 | 0.5472 | 0.0102 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.5289 | 0.1821 | 0.5289 | 0.0164 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5456 | 0.1828 | 0.5456 | 0.0108 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5459 | 0.1768 | 0.5459 | 0.0106 | 13,509 | 7 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.5484 | 0.1805 | 0.5484 | 0.0110 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | 0.5001 | 0.1718 | 0.5002 | 0.0173 | 13,732 | 7 |
| gpt-5.6-terra [9] | 0.5254 | 0.1715 | 0.5254 | 0.0136 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5294 | 0.5031 | 0.5236 | 0.5010 | 0.5393 | 0.5162 | 0.5517 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.5166 | 0.4907 | 0.5099 | 0.4887 | 0.5320 | 0.5019 | 0.5402 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5490 | 0.5373 | 0.5481 | 0.5364 | 0.5600 | 0.5368 | 0.5628 |
| Qwen3.8-27B-AWQ-INT4 [3] | 0.5353 | 0.5112 | 0.5249 | 0.5104 | 0.5473 | 0.5179 | 0.5554 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5446 | 0.5363 | 0.5450 | 0.5329 | 0.5543 | 0.5394 | 0.5668 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5478 | 0.5399 | 0.5418 | 0.5382 | 0.5573 | 0.5320 | 0.5644 |
| Laguna-S-2.1-NVFP4 [6] | 0.5506 | 0.5377 | 0.5462 | 0.5365 | 0.5615 | 0.5398 | 0.5668 |
| claude-sonnet-5 [8] | 0.5075 | 0.4838 | 0.4949 | 0.4846 | 0.5175 | 0.4828 | 0.5303 |
| gpt-5.6-terra [9] | 0.5317 | 0.5162 | 0.5244 | 0.5132 | 0.5346 | 0.5073 | 0.5502 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | 0.5347 | 0.1771 | 0.5347 | 0.0143 | 17,393 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | 0.5174 | 0.1872 | 0.5174 | 0.0204 | 17,573 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | 0.5287 | 0.1862 | 0.5287 | 0.0172 | 17,805 |
| ❗ Qwen3.8-27B-AWQ-INT4 [3] | 0.5158 | 0.1901 | 0.5158 | 0.0200 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | 0.5449 | 0.1748 | 0.5449 | 0.0142 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | 0.5193 | 0.1873 | 0.5193 | 0.0185 | 17,630 |
| ✔️ Laguna-S-2.1-NVFP4 [6] | 0.5543 | 0.1759 | 0.5543 | 0.0120 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | 0.6253 | 0.0766 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | 0.5046 | 0.1561 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | 0.6719 | 0.0537 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | 0.6267 | 0.0789 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | 0.6659 | 0.1300 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | 0.6826 | 0.0622 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | 0.5547 | 0.1215 | 5,192 |
| ❗ Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | 0.2354 | 0.1318 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | 0.5046 | 0.1571 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | 0.2773 | 0.1537 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | 0.5809 | 0.1105 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | 0.6546 | 0.0643 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 0.7642 | 0.0852 | 4,787 |
| Translate the given text to French. | 0.3731 | 0.0635 | 7,849 |
| Translate the given text to German. | 0.3737 | 0.0680 | 7,866 |
| Translate the given text to Hindi. | 0.4237 | 0.0813 | 7,843 |
| Translate the given text to Spanish. | 0.3472 | 0.0691 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | 0.6847 | 0.1246 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | 0.6491 | 0.0708 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 0.7177 | 0.0410 | 5,064 |
### Reranker distance to the source (`reranker`)
Per encoder, marginalised over every decoder. `Pooled Mean` weights every kept row equally; `Decoder Balanced Mean` is the mean of the per-decoder means, and `Spread Across Decoders` their standard deviation.
| Encoder | Pooled Mean | Pooled Std | Decoder Balanced Mean | Spread Across Decoders | N | Decoders |
|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | -5.1087 | 4.6634 | -5.1181 | 1.2963 | 13,601 | 7 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | -5.1772 | 4.8973 | -5.1840 | 1.2832 | 13,615 | 7 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | -3.3532 | 5.9677 | -3.3607 | 1.4214 | 13,723 | 7 |
| Qwen3.8-27B-AWQ-INT4 [3] | -5.2728 | 4.5851 | -5.2792 | 1.2749 | 13,639 | 7 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | -4.6685 | 5.1148 | -4.6743 | 1.2902 | 13,731 | 7 |
| gemma-4-31B-it-AWQ-4bit [5] | -4.9193 | 4.8794 | -4.9291 | 1.3546 | 13,509 | 7 |
| Laguna-S-2.1-NVFP4 [6] | -4.5815 | 5.0423 | -4.5894 | 1.3084 | 13,662 | 7 |
| ❗ claude-sonnet-5 [8] | -5.8345 | 4.2569 | -5.8408 | 1.2062 | 13,732 | 7 |
| gpt-5.6-terra [9] | -5.7665 | 4.3953 | -5.7724 | 1.3443 | 13,721 | 7 |
Encoder x decoder cell means:
| Encoder \ Decoder | granite-4.2-30b-nvfp4 [0] | Ornith-1.5-35B-A3B-NVFP4 [1] | Llama-3.3-70B-Instruct-NVFP4 [2] | Qwen3.8-27B-AWQ-INT4 [3] | Mistral-Small-4-119B-2603-NVFP4 [4] | gemma-4-31B-it-AWQ-4bit [5] | Laguna-S-2.1-NVFP4 [6] |
|---|---|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | -5.7992 | -6.0496 | -1.9920 | -5.5104 | -5.7292 | -5.3601 | -5.3862 |
| Ornith-1.5-35B-A3B-NVFP4 [1] | -6.0300 | -6.0421 | -2.1075 | -5.7409 | -5.6393 | -5.4786 | -5.2499 |
| Llama-3.3-70B-Instruct-NVFP4 [2] | -4.2751 | -4.3410 | 0.0502 | -3.8787 | -3.9640 | -3.5304 | -3.5856 |
| Qwen3.8-27B-AWQ-INT4 [3] | -6.0635 | -6.1672 | -2.2127 | -5.7549 | -5.7552 | -5.5994 | -5.4017 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | -5.5409 | -5.5674 | -1.5758 | -5.1226 | -5.1078 | -4.9699 | -4.8356 |
| gemma-4-31B-it-AWQ-4bit [5] | -5.5996 | -5.9167 | -1.6619 | -5.5304 | -5.4200 | -5.2505 | -5.1244 |
| Laguna-S-2.1-NVFP4 [6] | -5.3840 | -5.5235 | -1.4411 | -5.0479 | -5.0816 | -4.8792 | -4.7685 |
| claude-sonnet-5 [8] | -6.3986 | -6.8100 | -2.9369 | -6.2911 | -6.2062 | -6.1842 | -6.0587 |
| gpt-5.6-terra [9] | -6.4315 | -6.6533 | -2.5106 | -6.3451 | -6.1760 | -6.2698 | -6.0205 |
Per decoder, marginalised over every encoder (for contrast):
| Decoder | Pooled Mean | Pooled Std | Encoder Balanced Mean | Spread Across Encoders | N |
|---|---|---|---|---|---|
| granite-4.2-30b-nvfp4 [0] | -5.7255 | 4.2002 | -5.7247 | 0.6177 | 17,393 |
| ❗ Ornith-1.5-35B-A3B-NVFP4 [1] | -5.8962 | 4.1339 | -5.8968 | 0.6825 | 17,573 |
| ✔️ Llama-3.3-70B-Instruct-NVFP4 [2] | -1.8208 | 6.4574 | -1.8209 | 0.7970 | 17,805 |
| Qwen3.8-27B-AWQ-INT4 [3] | -5.4675 | 4.4800 | -5.4691 | 0.7017 | 17,530 |
| Mistral-Small-4-119B-2603-NVFP4 [4] | -5.4538 | 4.5753 | -5.4533 | 0.6470 | 17,591 |
| gemma-4-31B-it-AWQ-4bit [5] | -5.2793 | 4.4861 | -5.2802 | 0.7648 | 17,630 |
| Laguna-S-2.1-NVFP4 [6] | -5.1590 | 4.5684 | -5.1590 | 0.6996 | 17,411 |
Per encoding instruction, marginalised over every encoder and decoder:
| Encoding instruction | Mean | Std | N |
|---|---|---|---|
| Convert this text into a series of logical propositions or syllogisms that represent the c... | -3.9055 | 3.9910 | 6,283 |
| Create a detailed dictionary that fully describes all the meaningful phrases and words thi... | -5.1357 | 3.7171 | 5,227 |
| Create a prompt that might cause an LLM to generate an output resembling this text. | -3.5881 | 4.1917 | 6,167 |
| Create a prompt that would allow an LLM to generate this text as accurately/verbatim as po... | -4.9329 | 3.7351 | 6,148 |
| Create the shortest possible prompt that can be sent to an LLM to reproduce this text. The... | -4.1475 | 4.6113 | 5,806 |
| Envision the scenario in which the author wrote this text. Describe that scenario in exhau... | -2.4133 | 4.8027 | 5,029 |
| Reformat this text into a sensible, structured JSON object. | -5.6481 | 3.4504 | 5,192 |
| Replace every verb in this text with the tag [VERB], keeping all nouns, adjectives, articl... | -8.0528 | 1.7414 | 6,073 |
| Summarize this text, attempting to preserve as much of the original language of the text a... | -6.1211 | 3.0805 | 6,196 |
| Take this text and replace one word in every four with a single underscore (use one unders... | -7.4950 | 2.2661 | 6,096 |
| Take this text, but extract only the most meaningful sentences out of it to create a new t... | -5.0779 | 3.7985 | 6,261 |
| This piece of text was cleverely generated by an LLM with a human supervising it so that t... | -4.1690 | 3.9916 | 6,091 |
| ✔️ Translate the entirety of this text into a sequence of emojis that captures the literal me... | 6.9514 | 6.1475 | 4,787 |
| Translate the given text to French. | -8.3883 | 1.3209 | 7,849 |
| Translate the given text to German. | -8.2925 | 1.5709 | 7,866 |
| Translate the given text to Hindi. | -7.8838 | 1.6850 | 7,843 |
| ❗ Translate the given text to Spanish. | -8.4516 | 1.0648 | 7,860 |
| Write a 'few-shot' prompt using minimal, fragmented excerpts from this text as examples. T... | -3.2339 | 4.5885 | 5,914 |
| Write a detailed descriptor for how this text is stylistically differentiated from other t... | -2.3529 | 4.5233 | 5,181 |
| Write a detailed descriptor for the (imaginary) personality of the author who wrote this t... | 1.2162 | 4.9882 | 5,064 |
|