Update README and 24 associated file(s)

README.md

@@ -548,6 +548,967 @@ configs:
   - split: train
     path: shard_6/train-*
 ---
+# 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`): 8,790 kept / 210 trashed of 9,000; failed requests 6; runtime 4.2 h (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`): 14,948 kept / 348 trashed of 15,296; failed requests 2; runtime 2.2 h (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`): 15,134 kept / 162 trashed of 15,296; failed requests 2; runtime 9.7 h (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`): 8,778 kept / 222 trashed of 9,000; failed requests 4; runtime 5.9 h (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`): 8,772 kept / 228 trashed of 9,000; failed requests 0; runtime 1.6 h (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`): 8,746 kept / 254 trashed of 9,000; failed requests 0; runtime 6.3 h (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`): 8,770 kept / 230 trashed of 9,000; failed requests 5; runtime 2.4 h (empty or too short: 35, refusal: 36, filler output: 5, unfilled placeholder: 117, task meta-commentary: 27, echoed instruction: 5, identical to source: 19)
+
+## 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 |
+
+![EditLens score (higher = more AI-like) per encoder](ENC_final_response_editlens_score_roberta_large.png)
+
+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 |
+
+![EditLens score (higher = more AI-like) encoder x decoder](MATRIX_final_response_editlens_score_roberta_large.png)
+
+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 |
+
+![EditLens bucket (higher = more AI-like) per encoder](ENC_final_response_editlens_bucket_roberta_large.png)
+
+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 |
+
+![EditLens bucket (higher = more AI-like) encoder x decoder](MATRIX_final_response_editlens_bucket_roberta_large.png)
+
+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 |
+
+![Jaccard-1 distance to the source per encoder](ENC_jaccard_1.png)
+
+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 |
+
+![Jaccard-1 distance to the source encoder x decoder](MATRIX_jaccard_1.png)
+
+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 |
+
+![Jaccard-2 distance to the source per encoder](ENC_jaccard_2.png)
+
+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 |
+
+![Jaccard-2 distance to the source encoder x decoder](MATRIX_jaccard_2.png)
+
+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 |
+
+![Levenshtein distance to the source per encoder](ENC_levenshtein.png)
+
+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 |
+
+![Levenshtein distance to the source encoder x decoder](MATRIX_levenshtein.png)
+
+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 |
+
+![Soft n-gram distance to the source per encoder](ENC_softngram.png)
+
+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 |
+
+![Soft n-gram distance to the source encoder x decoder](MATRIX_softngram.png)
+
+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 |
+
+![Embedding cosine distance to the source per encoder](ENC_cosdist.png)
+
+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 |
+
+![Embedding cosine distance to the source encoder x decoder](MATRIX_cosdist.png)
+
+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 |
+
+![BERTScore distance to the source per encoder](ENC_bertscore.png)
+
+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 |
+
+![BERTScore distance to the source encoder x decoder](MATRIX_bertscore.png)
+
+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 |
+
+![BERTScore precision distance per encoder](ENC_bertscore_precision.png)
+
+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 |
+
+![BERTScore precision distance encoder x decoder](MATRIX_bertscore_precision.png)
+
+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 |
+
+![BERTScore recall distance per encoder](ENC_bertscore_recall.png)
+
+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 |
+
+![BERTScore recall distance encoder x decoder](MATRIX_bertscore_recall.png)
+
+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 |
+
+![MoverScore distance to the source per encoder](ENC_moverscore.png)
+
+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 |
+
+![MoverScore distance to the source encoder x decoder](MATRIX_moverscore.png)
+
+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 |
+
+![Reranker distance to the source per encoder](ENC_reranker.png)
+
+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 |
+
+![Reranker distance to the source encoder x decoder](MATRIX_reranker.png)
+
+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 |
 
-# WIP encoder/decoder trial
-Waiting for statistics to finish generating...