second tier think-on 16k board: dataset/README.md

dataset/README.md

@@ -129,6 +129,25 @@ Results are **split by reasoning mode**: comparing a thinking-on (reasoning) mod
 
 > **Sampling.** MMLU & HellaSwag use 50% stratified sampling (seed=42); ARC-Challenge, GSM8K, and HumanEval run the full item counts (HumanEval = all 164). Full per-model reports in [`reports/`](reports/).
 
+### Second tier, thinking on (IFEval · MATH-500 · HumanEval+ · MBPP+)
+
+The five-task boards above are short-answer tasks. This table is the harder second tier, run with thinking on at a fixed budget: `max_tokens 16384`, `ctx 24576`, greedy, zero-shot, one pass per item, llama.cpp b9653, 1,583 items per model. Scores are percentages; IFEval is prompt-strict. **cap** is the share of items whose completion hit the 16k budget: a cell at 10% or more (▲) is a floor, not a ceiling, and is never flat-compared with a low-cap cell. ◆ marks a format miss (answers without `\boxed{}`). Full per-task counts, capped items, parse failures, tokens per correct answer and median completion length: [`second_tier.csv`](second_tier.csv). Report with the reads and limits: [thinking on at a 16k budget](../reports/second-tier-thinkon-16k.md) · [chart](../reports/second-tier-thinkon-16k.png).
+
+| Model | Quant | GGUF | IFEval | MATH-500 | HumanEval+ | MBPP+ | Mean | MATH-500 cap |
+|-------|-------|-----:|-------:|---------:|-----------:|------:|-----:|-------------:|
+| Gemma 4 31B-it | QAT Q4_0 | 17.7 GB | 91.1 | 94.0 | 94.5 | 81.0 | **90.1** | 2.2% |
+| Qwen3.8-27B | Q6_K | 22.9 GB | 89.7 | 95.2 | 92.1 | 81.8 | **89.7** | 1.6% |
+| Qwen3.8-27B | UD-IQ3_XXS | 11.9 GB | 90.2 | 94.6 | 92.7 | 80.7 | **89.5** | 1.2% |
+| Nemotron-3.5-Lightning 30B-A3B | Q4_K_M | 24.5 GB | 87.8 | 90.2 | 81.1 | 80.4 | **84.9** | 6.2% |
+| Ornith 1.5 35B-A3B | Q4_K_M | 21.7 GB | 77.3 | 88.2 | 91.5 | 81.5 | **84.6** | 2.2% |
+| Qwen3.6-35B-A3B | UD-Q5_K_M | 26.5 GB | 87.2 | 72.8 ▲ | 95.1 | 81.2 | **84.1** | 30.2% |
+| Qwen3.6-27B | Q6_K | 22.9 GB | 88.5 | 73.2 ▲ | 94.5 | 79.4 | **83.9** | 27.0% |
+| Qwable-27B | Q4_K_M | 16.5 GB | 87.6 | 71.6 ▲ | 93.9 | 72.2 ▲ | **81.3** | 28.8% |
+| Ornith 1.5 9B | Q6_K | 7.4 GB | 69.5 | 84.6 | 89.0 | 77.0 | **80.0** | 5.8% |
+| Qwopus3.8-27B-Flash | Q6_K | 22.4 GB | 83.6 | 33.6 ◆ | 74.4 | 80.7 | **68.1** | 3.8% |
+
+The top three are a tie: the Wilson 95% half-width on a four-task mean here is 1.6 points. The three ▲ MATH-500 cells spent 12.4k to 12.9k completion tokens per correct answer against 1.8k to 1.9k for the two Qwen3.8 rows; a 32k pass on exactly those three legs is queued and will publish as a separate table. Qwable-27B MBPP+ capped 12.2%. The Qwopus3.8-27B-Flash MATH-500 cell has 325 of 500 answers unboxed at 3.8% capped, a formatting result in the same family as its main-board HumanEval (footnote 15).
+
 ### Methodology
 
 | Benchmark | Dataset | Few-shot | Scoring | Items |
@@ -145,7 +164,7 @@ Full per-model reports with MMLU category breakdowns, parse reliability stats, a
 
 #### Error bars
 
-Every score above is a single greedy pass, so each is a binomial proportion with a sampling interval. [`board_ci.csv`](board_ci.csv) publishes a Wilson 95% interval per task per row (from the `correct`/`total` counts in each run's detail files) together with the parse-failure count, and a `q_avg` row whose half-width propagates the five task variances through the mean, treating tasks as independent. Measured across the 47 rows with all five tasks (regenerated 2026-09-08), the median q_avg half-width is 0.97 points (the four think-on rows scored on small subsets run 2 to 4 points), and humaneval dominates it in 44 of 47 rows with a median 82% share of the variance, because 164 problems at ~93% pass gives a task half-width of about 4.5 points against under 1 point for the other four. Two rows whose q_avg differ by less than that half-width are a tie; the tables stay sorted by q_avg for continuity, so read neighbouring rows as a band of roughly one point rather than a ranking. Regenerate with `python3 scripts/board_ci.py results/`.
+Every score above is a single greedy pass, so each is a binomial proportion with a sampling interval. [`board_ci.csv`](board_ci.csv) publishes a Wilson 95% interval per task per row (from the `correct`/`total` counts in each run's detail files) together with the parse-failure count, and a `q_avg` row whose half-width propagates the five task variances through the mean, treating tasks as independent. Measured across the 47 rows with all five tasks (regenerated 2026-09-10), the median q_avg half-width is 0.97 points (the four think-on rows scored on small subsets run 2 to 4 points), and humaneval dominates it in 44 of 47 rows with a median 82% share of the variance, because 164 problems at ~93% pass gives a task half-width of about 4.5 points against under 1 point for the other four. Since 2026-09-10 the file also carries one `gpqa_thinkon` row per think-on GPQA leg (seven rows) with three extra columns, `tokens_per_correct`, `completion_tokens_median`, `capped_rate`, filled where the leg recorded per-item tokens (legs run since 2026-09-09; the August legs did not record them). The per-budget legs of the [reasoning-budget curve](reports/reasoning-budget-curve.md) live under `results/<slug>-thinkon/budget-<N>/` and are tabulated in that report rather than in this file. Two rows whose q_avg differ by less than that half-width are a tie; the tables stay sorted by q_avg for continuity, so read neighbouring rows as a band of roughly one point rather than a ranking. Regenerate with `python3 scripts/board_ci.py results/`.
 
 ---
 
@@ -220,6 +239,8 @@ One-shot investigations that don't fit the leaderboard format — claim verifica
 
 | Report | Finding |
 |---|---|
+| [Speculative decoding and prefix caching on one RTX 5090, Qwen3.8-27B Q6_K: the two free speedups on the model this box now serves](reports/spec-cache-study-qwen38.md) · [chart](reports/spec-cache-study-qwen38.png) | Rerun of the spec-cache study on Qwen3.8-27B Q6_K (the served model since 2026-09-13, own MTP head in the unsloth GGUF), same 304-request design, same flag set (llama.cpp b9653, ctx 64k, parallel 1, KV q8_0, batch 1). MTP n=2 lifts decode from 61 to 109-144 tok/s (1.8x prose and chat, 2.3x code and repetitive) for +9 ms TTFT; n=4 reaches 168-184 on code/repetitive and loses 5-8% on prose/chat; n-gram lookup is 1.00x. Prefix cache: TTFT 1.004 s to 0.069 s on a 2,954-token system prompt (15x), 3.814 s to 0.086 s on 11,570 tokens (44x), decode unchanged. Stacked, a short answer to the 11.5k context goes 4.25 s to 0.30 s. Losslessness: MTP byte-identical to plain greedy decode on 59% of answers; all 13 divergences are the target's own second choice at a near-tie (top-2 gap 0.004 to 0.133 nats, median 0.030). Every cell within noise of the Qwen3.6-27B run a week earlier: the speedups belong to the shape and the serving stack, not the generation. |
+| [How much thinking is worth paying for: GPQA-diamond think-on at three completion budgets](reports/reasoning-budget-curve.md) · [chart](reports/reasoning-budget-curve.png) | Two think-on models, the same 198 items, only `max_tokens` moved (4,096 / 8,192 / 16,384). Qwen3.8-27B Q6_K 60.1 / 70.2 / 79.3, Ornith 1.5 35B-A3B Q4_K_M 70.2 / 77.3 / 81.8. Every point lost at a smaller budget is a capped item (101 of 101 across the four smaller legs, per-item diff against 16k) and finished items are 94.7 to 96.1% correct at every budget, so the budget removes the hard questions rather than degrading the answers. 43% / 38% of items run past 4k, 34% / 30% past 8k, 22% of both still past 16k. Tokens per correct answer: Qwen 3,972 → 5,636 → 7,740, Ornith 3,248 → 4,732 → 6,996 (Ornith 700 to 900 cheaper at every budget, and 4.6x faster to decode). The 16k legs reproduced the August 16k runs on all 198 items. Limits: two models, one quant each, greedy, curve stops at 16k. |
 | [Speculative decoding and prefix caching on one RTX 5090: what the two free speedups buy](reports/spec-cache-study.md) · [chart](reports/spec-cache-study.png) | The two single-GPU optimisations every inference text names first, measured on the model and flag set this box serves daily (Qwen3.6-27B Q6_K with its embedded MTP drafter, llama.cpp b9653, batch 1, 304 requests, one variable at a time). MTP draft n=2 lifts decode from 61 to 112-144 tok/s (1.8x prose, 1.9x chat, 2.3x code and repetitive) for +10 ms TTFT; n=4 reaches 169-183 on code/repetitive and loses to n=2 on prose/chat, because draft depth only pays above ~0.85 acceptance; n-gram lookup is 1.00x on 256-token answers. Prefix caching cuts TTFT on a re-used 2,954-token system prompt from 1.005 s to 0.067 s (15x) and on 11,570 tokens from 3.835 s to 0.084 s (46x), decode untouched; stacked, a short answer to the long context goes 4.18 s to 0.24 s. Losslessness: MTP output is byte-identical to plain greedy decode on 56% of answers; all 14 divergences examined with top-2 logprobs are flips to the target's own second choice at a near-tie (gap under 0.09 nats), so same quality class, not byte-identical. Limits: one model, one quant, batch 1, 256-token answers. |
 | [The benchmark said below-chance. The model was fine: a truncation autopsy](reports/gpqa-truncation-artifact.md) · [chart](reports/chart_gpqa_truncation.png) | A community benchmark harness (SM12X-LLM-BENCH, full profile, 18h, rc=0, zero infra errors) reported Qwen3.6-27B Q6_K at 21.7% on GPQA-diamond — below the 25% random-guessing floor for 4-choice questions. The model was fine: the harness serves thinking models at their default (thinking on) with an 8,192-token completion cap, and on GPQA the model's reasoning chain was still running when the cap hit (ttft p50 184s), so 153/198 items (77%) ended `finish_reason=length` with no extractable answer, each scored as wrong. Anchor leg on the same GGUF file, same resident llama-server, same RTX 5090, thinking off so answers fit the budget: 54.5% (108/198, 0 parse failures) — 2.5x the score from one setting. Reads: below-chance accuracy on a competent model is a pipeline bug until proven otherwise; the truncation count was in the harness's own report all along, but the lane still presented as PASS/publishable, so score fields get read and diagnostic fields get skipped (guard proposed upstream as SM12X-LLM-BENCH#3, with #1 long-context detection and #2 cache-inflated prefill from the same run); thinking-mode benchmarks need deliberation-sized budgets, not answer-sized ones. Honest limits: anchor is think-off (lower bound proving artifact, not the model's think-on ceiling); one model, one quant, one lane; the harness's client plumbing was impeccable — the finding is about score presentation. |
 | [Kitty's 2-bit KV cache holds long-context retrieval, the axis its paper never measured](reports/kitty-needle.md) · [chart](reports/chart_kitty_needle.png) | Kitty (arXiv 2511.18643) reports near-zero loss at 2-bit KV cache, but measures it only on short-input reasoning tasks (GSM8K, MATH, HumanEval, GPQA, MMLU, AIME), never on long-input retrieval, the axis KV compression exists for. Needle-in-a-haystack, Qwen3-8B, one RTX 5090, f16 KV vs Kitty-Pro 2-bit KV on identical prompts. Calibrate f16 first: it retrieves a single needle at 100% across 8k/16k/32k, every depth. 64k does not fit (a one-shot 64k prefill plus f16 KV OOMs the 32GB card, and Kitty's reference path stores the cache dequantized so it cannot rescue the memory), so the ladder caps at 32k, still 16x Kitty's longest tested input. Single needle: 2-bit ties f16 at 100% in all fifteen cells, including the needle at the very start of a 32k context, but a perfect tie is weak evidence (it cannot tell lossless from too-easy). Harder probe: 8 needles at 8 depths, keyed retrieval, 160 recoveries per leg. Now the task discriminates, f16 itself slips to 97.5% at 32k and 90% at the shallowest depth. Through that, 2-bit KV tracks f16 within noise: 99.4 vs 98.8 overall, about one needle, no depth band degrading. Kitty's near-zero loss extends to long-context multi-fact retrieval up to 32k. Honest limits: 64k is a hardware wall not a pass; f16 is still near ceiling at 98.8%, so a harsher task might separate them; this is the reference fake-quant simulator, quality not a deployed kernel's speed or memory; one model, one retrieval task. |