Name That LLM · Research Terminal

BEST / SCORED0.68212 submissions

RANK / TEAMS437of 5,558 · -103 since 11 Nov

PERCENTILEtop 7.9%top-10% line 0.682

LEADER0.768NguyenThanhNhan

GAP / LEADER-0.086to top 10: -0.065

IDEAS TRIED5224 worked · 10 lost · 10 flat

SPENT / USD$32.45GPU $19.50 · data $12.95

BOARD AS OF11 Nov20:25 Eastern

01 / Board race

OUR ENTRIES (S#, SEE LEDGER) VS LEADER AND TOP-10 CUTOFF

Our best so farOur entryEntry below its controlCrashed (no score)Board leaderTop-10 cutoff

02 / Where the field sits

ALL 5,558 TEAMS · 2026-11-11 20:25 ET

03 / Board ledger

EVERY SUBMISSION · CHANGE VS ITS MATCHED CONTROL

#	Date	What went in	Score	vs control
S12	10 Nov	Public 0.683 recipe, control The public 0.683 recipe run unchanged on our account. The author reported 0.683 and we got 0.679, 0.003 under our best.	0.679	—
S11	10 Nov	Full-pool Qwen added to the 0.683 recipe The public 0.683 recipe with our new full-pool Qwen model mixed in at ten percent. It lost to the same recipe without it.	0.675	-0.004
S10	10 Nov	Tokenizer and byte models on the 0.682 recipe The public 0.682 recipe with ten percent tokenizer models and twenty percent raw-byte models mixed in. It lost.	0.669	-0.013
S9	10 Nov	Public 0.682 recipe, control (our best) The public 0.682 recipe run unchanged on our account. It matched the author's score and is our best entry.	0.682	—
S8	10 Nov	Earlier Qwen added to the 0.682 recipe The public 0.682 recipe with ten percent of our earlier Qwen3-8B model mixed in. It lost.	0.678	-0.004
S7	10 Nov	Tokenizer model added to the 0.682 recipe The public 0.682 recipe with ten percent of our tokenizer model mixed in. It lost.	0.675	-0.007
S6	9 Nov	Public 0.681 ensemble run A full run of the public 0.681 ensemble on our account. It beat our old best of 0.651 by 0.030.	0.681	—
S5	31 Oct	Our own Qwen model alone Our own trained Qwen3-8B model submitted by itself to test the pipeline end to end. It scored 0.585, close to its 0.588 local score and well below the public ensemble.	0.585	—
S4	27 Oct	First public neural ensemble The public neural ensemble, the 0.651 recipe, run in full on our account. Our first strong board score.	0.651	—
S3	27 Oct	Our own CPU classifier Our own character, word and style classifier, trained on the official labels, to prove an entry of ours runs on the hidden test.	0.348	—
S2	26 Oct	Public baseline, second try A second try at the public 0.651 notebook, still with our extra wrapper code. It crashed on the hidden test and earned no score.	crashed	—
S1	26 Oct	Public baseline, first try The first try at the public 0.651 notebook, with our extra wrapper code. It crashed on the hidden test and earned no score.	crashed	—

04 / Board watch

TOP 15 TEAMS AND US · GAP TO OUR BEST

#	Team	Score	vs us	Subs	Last entry (UTC)
1	NguyenThanhNhan	0.768	+0.086	13	2026-11-11 13:48
2	James Liebel	0.765	+0.083	35	2026-11-11 13:37
3	You’re Absolutely Right!	0.756	+0.074	75	2026-11-11 06:50
4	Le Viet	0.752	+0.070	106	2026-11-11 00:05
5	Meni David Gottesman	0.752	+0.070	102	2026-11-10 00:08
6	Keanan	0.750	+0.068	65	2026-11-11 14:04
7	kulshekhar	0.748	+0.066	79	2026-11-11 00:04
8	Team Aillis	0.747	+0.065	126	2026-11-12 00:05
9	Hod Carriers	0.747	+0.065	66	2026-11-04 22:38
10	Lian Matsuo	0.747	+0.065	44	2026-11-11 00:10
11	Harveen Singh Chadha	0.746	+0.064	56	2026-11-11 11:58
12	Søren Ravn Andersen	0.743	+0.061	75	2026-11-11 12:29
13	Civitasmass	0.740	+0.058	68	2026-11-11 15:34
14	ymg_aq	0.737	+0.055	51	2026-11-11 18:00
15	吃白饭的大肥鱼	0.737	+0.055	2	2026-11-10 20:18
437	Mark Barney (us)	0.682	—	12	2026-11-11 00:00

05 / Day-by-day blotter

WHAT WE TRIED, WHAT HAPPENED · NEWEST FIRST · 13 DAYS

Date	Verdict	Tried	What happened	Spent
11 Nov	The three-pass Qwen model finished and was picked for the next Kaggle test run. The public 0.684 recipe is confirmed real. The style and GPT-correction ideas added nothing.			$13.21
	lost	Finished training the three-pass version of our Qwen model and scored it on its own against the one-pass model.	By itself it came out lower, 0.609 against 0.621.
	worked	Swapped the three-pass model in for the one-pass model at 10 percent of the ensemble and checked it on two sets of creative writing.	It beat the one-pass model on 16 of 18 creative comparisons, so we chose it for the next Kaggle test run.
	worked	Checked the new public 0.684 recipe.	Two public copies run identical code and the page shows 0.684. Our best is still 0.682.
	no gain	Trained bigger style networks on 245,081 passages.	On the official passages they beat the old style model, 0.324 against 0.278. On the two creative sets the whole-passage network did worse than the old model.
	dead end	Added the style networks to the ensemble, then added word-order style features on top.	The style networks added about four tenths of a point on the main test. The word-order features made it worse, so we dropped them.
	no gain	Added a correction that separates Qwen from the GPT models it gets confused with.	Scores moved by less than a point either way: up a little on the independent test sets, flat to slightly down on the official folds. We did not use it.
	worked	Finished training the Liquid model with three separate runs and mixed it into an earlier public blend.	It added about half a point at a 5 percent share and about three quarters of a point at 10 percent on our local test. It has not been on the board.
10 Nov	Our best board score moved up to 0.682 by running the public recipe as it is. Every one of our models mixed in scored lower.			$12.34
	lost	Submitted the public 0.682 recipe by itself, then three versions with our models mixed in: our tokenizer model, our earlier Qwen, and tokenizer plus raw-byte models.	The plain recipe scored 0.682, our new best. The three additions scored 0.675, 0.678 and 0.669, so all three lost.
	lost	Submitted the public 0.683 recipe by itself, then the same recipe with our new full-pool Qwen mixed in at 10 percent.	The plain copy scored 0.679 and the Qwen version 0.675, so Qwen lost. Our best stays 0.682.
	worked	Trained our Qwen model on the whole 253,598-passage public pool for one pass on a rented GPU.	Our local five-fold score rose from 0.587 to 0.621. Two earlier rented hosts failed before training, about 90 cents lost, and the third host finished the run.
	dead end	Ran the 540-row comparison of our model against the full public 0.681 stack on Kaggle.	It took three tries after setup and file-path crashes. The result could not judge our model: the public stack had already seen those passages and scored 0.978 alone, and blending ours in only lowered it.
	worked	Tested the shared Mistral and OLMo predictions against our stack.	Mistral helped most, +0.018 at a 20 percent share on our local test. OLMo added +0.009. Running the public Mistral model on Kaggle got as far as three test rows.
	worked	Built token and raw-byte specialist models for the five GPT authors.	The byte model added about four tenths of a point inside the GPT group on our local test, 0.579 to 0.583. We have not put it on the board.
	no gain	Retested the 6,000 passages we bought from six models, using three separate training runs.	The score moved by -0.001, -0.006 and +0.007 across the three runs, with no consistent gain.
	no gain	Tested GLM and Qwen likelihood fingerprints as extra features.	Most versions made the score slightly worse.
	built	Got the large GLM model running on a rented GPU.	It loaded and read three full passages on the fourth attempt, for about 54 cents. The rental was shut down.
9 Nov	We reproduced the public 0.681 notebook and submitted it. Our model helped an earlier public blend on our local test. The tokenizer signal added almost nothing.
	worked	Ran the public 0.681 notebook in full on Kaggle's free GPUs and submitted it that night.	It scored 0.681, up from 0.651.
	worked	Blended our own model into an earlier public ensemble on all 2,699 training passages.	Balanced accuracy rose from 0.543 to 0.613 in the three-response setting, and recall rose for all 18 models. On the real board, mixing our Qwen into the 0.682 recipe the next day lost.
	no gain	Tested tokenizer fingerprints, meaning how different tokenizers split a passage, as a 1 percent blend.	It was weak on its own and moved the earlier ensemble from 0.5431 to 0.5438. The tokenizer idea later lost on the board.
5 Nov	An outside review said we spent ten days on checks instead of the board, and ranked what to do instead.
	built	Had a team of reviewers go through the whole repository and propose a better order of work.	We held 0.651 against a leader at 0.730 and had made no submission since October 31. The plan puts a safe Kaggle submission first, then the group-context idea, then new model ideas, and recommends closing the extra-data work.
4 Nov	The clean-pool result held up on Kaggle's own machine. The 6,000 bought passages lost again on a second training run.
	worked	Ran the clean-pool fold on Kaggle's CPU machine and compared it with the result from our own machine.	The two matched except for one prediction, and that one was wrong on both. The result carries over to Kaggle.
	lost	Finished the second training run on Jethro that compares the 6,000 bought passages against the combined-data control.	The bought passages scored 0.580 against 0.586 for the control.
3 Nov	The memory crash was repaired and the full ensemble ran on Son's GPU machine. The extra data's small gain vanished when three training runs were averaged. Group context helped on whole groups and hurt on pairs.
	worked	Tested a group-aware scoring head that judges passages from the same prompt together.	It gained about six points when whole groups were present and lost about three points when groups were split into pairs. We did not switch it on everywhere. A rule based on group size is next.
	worked	Finished the four-way data comparison across three training runs.	The combined data beat the plain data in every run, by 0.001 to 0.009.
	no gain	Averaged the three runs of the combined-data model and compared it with the average of the plain-data runs.	The combined data scored 0.6005 against 0.6016 for plain. The gain disappeared once averaged.
	worked	Repaired the adapter memory step that crashed on Kaggle.	The repaired step gives exactly the same results as the original on real model weights.
	worked	Ran the whole upgraded public ensemble on Son's RTX 4090 machine, on the three visible test passages.	Every stage finished, including all six main models and all 18 adapters. This was not a board score and not a test of Kaggle's two-card machine.
	worked	Swapped a cleaner public data pool into the word-pattern model and scored it on all five folds.	By itself it rose from 0.490 to 0.512.
	no gain	Blended the word-pattern model on the cleaner pool with the three-run neural models.	The gain shrank to 0.613 to 0.616 once blended.
	lost	Ran the cleaner data pool on Kaggle's CPU machine.	Kaggle attached a newer version of the pool than the one we asked for, and the run stopped on a file check. We saved the exact pool as a private dataset of our own.
	dead end	Pulled the source of the new public 0.667 notebook.	Kaggle refused the download. We could only read the rendered page.
2 Nov	The upgraded public ensemble ran on Kaggle and crashed on memory. The 6,000 bought passages did not help on the first training run.
	lost	Ran the upgraded public ensemble on Kaggle's two-card GPU machine, without submitting it for a score.	It crashed with out-of-memory on the second card after its side models had finished. No score came out of it.
	built	Built the upgraded public ensemble into our own runner and checked it on the real competition data.	All eight checks passed, including empty and one-row inputs.
	no gain	Compared our model trained with the 6,000 bought passages against the plain version, same training run, same held-out prompts.	The bought passages scored 0.594 against 0.595. No gain.
31 Oct	Our own trained model scored 0.585 on Kaggle's hidden test, and we found why Jethro's training math was slow.
	worked	Submitted our own Qwen3-8B model alone to check the whole pipeline.	It scored 0.585 on the hidden test, close to its 0.588 local score. The public ensemble at 0.651 stayed our best.
	worked	Hunted for why training on Jethro was slow.	The old math libraries used a slow route. The newer installed ones ran the key steps six to eight times faster in tests.
30 Oct	Six thousand new passages collected, and training moved onto Jethro.			$6.90
	worked	Bought 6,000 new passages, 1,000 from each of six models, after a sixty-passage trial.	All 6,000 arrived for about $7.10, well under the $18 limit.
	worked	Cleared disk space on Jethro and started training on the expanded data.	About 186 gigabytes were freed and training started on the new data.
29 Oct	Our first self-trained model scored 0.588 against 0.480 for the public recipe on the same test folds.
	worked	Trained our own Qwen3-8B model in two stages, starting with a large public pool of writing.	It scored 0.588 on held-back topics, against 0.480 for the public recipe on the same folds.
	no gain	Added 1,752 Qwen passages we bought through OpenRouter to the word-pattern model.	No help: it scored 0.481 with them and 0.484 without. About $7.72 was spent on 2,499 requests, and 1,752 of the replies were usable.
	built	Repaired the training script and ran it on the real Qwen model on Son's RTX 4090.	The test passed, and the twelve-run Qwen data comparison started that evening on the same card.
	built	Had an independent review check our numbers.	It found the 0.588 against 0.480 comparison was not a clean test, because the training run also differed in settings. It could not reproduce an earlier blend figure of 0.611 and got 0.603. It asked for a matched control.
28 Oct	We went looking for new training writing, from Claude sessions and from GPT.
	built	Mined the Mac Mini's saved Claude Code replies for text from Claude models.	Found 2,124 passages labelled with the right model names.
	no gain	Trained our classifier with those Claude replies and saved Astra replies added.	Held-out accuracy rose only from 0.331 to 0.338 and several authors got worse, so the scored entry stayed as it was.
	worked	Measured whether each author's use of dashes helps tell authors apart.	Dashes alone scored 0.137. Added to the classifier they lifted local accuracy from 0.331 to 0.344. We kept it aside and did not put it in the scored entry.
	worked	Tested whether the Mac can collect writing from GPT models through the ChatGPT subscription.	GPT-5.6 Luna, Sol and Terra answered. GPT-5.5 was refused as unsupported.
27 Oct	Two real submissions: a weak classifier of our own at 0.348, and the full public neural ensemble at 0.651.
	worked	Submitted a classifier we built ourselves, to prove an entry of ours can run on Kaggle's hidden test.	It ran clean and scored 0.348, far below the public leaders.
	worked	Ran the strong public neural ensemble in full and submitted it.	Kaggle accepted it and the score came back at 0.651.
	worked	Added writing-style features to our own classifier.	Local accuracy went from 0.275 to 0.331, which is why it was the version we submitted.
26 Oct	We split out our own repo and audited why both earlier Kaggle tries crashed.
	lost	Looked into why our first two Kaggle submissions came back with no score.	Both crashed on the hidden test. Both carried extra wrapper code the original notebook did not have, including a guard that rejects small test inputs.
	built	Rebuilt the entry as an exact copy of the best public notebook, which scores 0.651.	The copy matches the original byte for byte.
	built	Split Name That LLM out of the old shared repo into its own standalone home.	The setup checks pass on the real training data.

06 / Capital ledger

EVERY RENTAL AND DATA PURCHASE · POSTED CHARGES

Date	What it paid for	Kind	USD
11 Nov	Qwen three-pass training run	GPU rental	$13.21
10 Nov	Qwen one-pass training run	GPU rental	$4.86
10 Nov	Two Qwen hosts that failed	GPU rental	$0.90
10 Nov	GLM test on a rented card, four attempts	GPU rental	$0.54
10 Nov	Creative-writing passages from other models	Data generation	$6.05
30 Oct	6,000 generated passages	Data generation	$6.90
	Total		$32.45

Qwen rental cap $18.96 / $24

GLM rental cap $0.54 / $10

Creative data cap $6.05 / $10

07 / Next

QUEUE

Submit the three-pass Qwen on top of the public 0.684 recipe, against the plain recipe.
Train our own Mistral model, the strongest different-model idea so far.
Build our own pruned GLM and fine-tune it (paused for now).