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@@ -180,6 +180,7 @@ One-shot investigations that don't fit the leaderboard format — claim verifica
 
 | Report | Finding |
 |---|---|
+| [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. |
 | [Reasoning is load-bearing for LFM2.5-2.6B on agentic tool-use, and a broken toggle nearly hid it](reports/t127.md) · [chart](reports/chart_t127.png) | LFM2.5-2.6B, a small reasoning model, tested on a native tool-calling battery (30 tasks, 3 reps, greedy, one RTX 5090) with reasoning on vs off plus three larger anchors. The first sweep reported a clean null: 96.7% both legs, McNemar p=1.0, not one of 30 tasks moving. It was a measurement artifact. LFM2.5's shipped GGUF chat template hard-forces `<think>` on every assistant turn and ignores enable_thinking/reasoning/reasoning_effort, so the off leg still logged ~700 reasoning tokens per task and both legs reasoned. Serving a patched template (--chat-template-file) that pre-closes `<think></think>` when thinking is off dropped the off leg to 0 reasoning tokens across 90 runs and reproduced the on leg exactly, and the result flipped: reasoning on 96.7% to off 70.0%, a 26.7pt fall, McNemar p=0.0078 (8 of 30 tasks flipped, 0 the other way). The drop is worst on the easy tier (single-tool 100 to 60): with no think block the model acts without a plan, making more tool calls (4.16 vs 2.7) and more bad ones (1.48 vs 0.97) at the same output length, so the failures are misfires not truncation. Reasoning roughly doubles the token bill (1403 vs 685 per task) and is worth it on this battery. Honest limits: LFM-on tops all three anchors (Qwen3.5-9B 86.7, Gemma-4-E4B 83.3, Qwen3.5-4B 80.0) but that comparison is underpowered at n=30 (p≥0.0625), so treat it as directional; the significant result is the on/off effect. The probe that gated the run checked only that the on leg reasoned, never that the off leg stopped, a one-sided check that passes a broken A/B, now fixed to check both directions. |
 | [NVFP4 on a 5090: real FP4 lands on the Qwen3.6-27B frontier where K-quants do](reports/nvfp4-frontier.md) · [chart](reports/chart_nvfp4_headtohead.png) · [frontier](reports/chart_nvfp4_frontier.png) | NVIDIA's NVFP4 is Blackwell's native 4-bit float; benchmarked as a GGUF rung on the exact Qwen3.6-27B K-quant frontier the quant-tax ladder measured, same harness (MMLU/GSM8K/HumanEval, greedy, think-off), one RTX 5090. It lands on the frontier: composite 92.11 (between Q5 and Q6), with MMLU (86.67) and GSM8K (97.60) identical to Q8_0 and Q6_K, and HumanEval one question under Q8. Clean measurement (zero empty outputs, no grader fallback). Two mechanisms. First, its decode sits on the memory-bound line: 78.6 tok/s from 18.1 GiB served, exactly where the K-quant curve predicts for that footprint (Q5 71.8 at 19.2 GiB, Q4 80.4 at 16.8), so Blackwell's FP4 dequant adds nothing measurable. That is the direct contrast with BTL-3-Compact's AVQ2, which is compute-bound and decodes slower than Q8. Second, it confirms NVFP4 is near-lossless: the NVFP4-vs-Q6 gap is 0.21pt on the 3-suite composite (June's t034 put it near a point on the 5-suite q_avg; the suite and build differ, both say near-lossless rather than cliff). The framing contrast: same rig and base, NVFP4 on the frontier, BTL-3's sub-2.5-bit 13pt below it. Honest limits: the 20.09 GB file carries an inert BF16 MTP draft head, so the single-stream served footprint is 18.1 GiB; the NVFP4-Q8 variant and the vLLM NVFP4-vs-AWQ throughput question were not run; 3-suite composite, one model. |