Upload README.md with huggingface_hub

README.md

@@ -116,6 +116,31 @@ MoE (3B active) vs Dense (27B) on same-family Qwen3.6 models:
 
 ---
 
+## Field Reports
+
+One-shot investigations that don't fit the leaderboard format — claim verification, new-architecture probes, and consumer-hardware autopsies, all measured on the same rig. Newest first.
+
+| Report | Finding |
+|---|---|
+| [Keye-VL-2.0-30B autopsy](reports/keye-vl-2-autopsy.md) · [chart](reports/keye-walls-chart.png) | Five measured walls: "lossless 256K" needs 25.8GB of KV alone; the shipped sparse attention is O(N²)-memory (one 30.65GiB allocation at ~32K, measured); 4-bit quant reaches 4.7% of params; the code's API window is two transformers release candidates wide. Does not run on consumer hardware. |
+| [LocateAnything-3B](reports/locateanything-3b.md) · [chart](reports/la-screenspot-chart.png) · [raw](raw/la-screenspot.jsonl) | ScreenSpot-Pro 55.3% measured vs 60.3 claimed (32GB forces extra downscale; accuracy tracks screenshot size). Real fault line: text 63.2% vs icons 42.7%. PBD parallel box decoding verified at 2.07x on the SDPA fallback. |
+| [HRM-Text-1B](reports/hrm-text-1b.md) · [chart](reports/hrm-trap.png) · [raw gens](raw/) | GSM8K 79.5% (claimed 84.5: holds at n=200). Omitting `token_type_ids` — which every standard harness does — silently costs 26 points. The recurrence bill: a 1.2B that decodes like a ~5B (42.9 tok/s bf16, 4x KV cache). |
+| [DiffusionGemma vs AR](reports/diffusion-vs-ar.md) · [chart](reports/diffusion-vs-ar.png) | AR wins at every answer length: diffusion pays a fixed ~3s per 256-token canvas (0.8 effective tok/s on short answers; best case still 2.3x slower). Day-0 public GGUFs were unloadable — convert from source. |
+| [Gemma 4 31B QAT + MTP](reports/gemma4-31b-qat-mtp.md) · [chart](reports/gemma4-qat-speed.png) | The MTP draft head lifts decode 76 to 125 tok/s (1.67x). QAT's real value is VRAM, not quality: the Q4 footprint is what fits 128K context plus the draft head on one card. |
+| [NVFP4 vs Q6_K](reports/nvfp4-vs-q6-qwen3-6-27b.md) · [chart](reports/chart-nvfp4-vs-q6.png) | Qwen3.6-27B: NVFP4 trades ~1pt q_avg against Q6_K. |
+| [GRPO on one 5090](reports/fp8-rl-grpo.md) · [chart](reports/chart-grpo-gsm8k.png) | Single-GPU RL: +7.66 GSM8K on a 4B. Train-prompt-to-eval-prompt alignment is the lever. |
+| [Embedding retrieval bench](reports/embedding-retrieval-bench.md) · [chart](reports/embed-bench-scatter.png) | Local embedding models benchmarked for retrieval quality vs speed on the 5090. |
+| [Mistral Small 4 speed](reports/mistral-small-4-speed.md) · [chart](reports/mistral-vs-gptoss-speed.png) | Speed profile vs gpt-oss-20b — and a benchmarking trap: reasoning is gated behind `reasoning_effort`, which defaults off. |
+| [LFM2.5-VL 1.6B extraction](reports/lfm2-5-vl-1-6b-extract.md) · [chart](reports/chart-lfm2-vl-extract.png) | A 1.6B VL model as a local structured-data extractor. |
+| [Nex-N2-mini agentic probe](reports/agentic-nex-n2-mini.md) | Adaptive Thinking saves 65% of tokens but costs 13pts task success. Superseded by the dedicated Agentic Score leaderboard (below). |
+
+## Related Datasets
+
+- [witcheer/agentic-score-leaderboard](https://huggingface.co/datasets/witcheer/agentic-score-leaderboard) — model-agnostic agentic tool-calling benchmark (7 models, 40 tasks) + the SWE-bench reality anchor
+- [witcheer/sovereign-asr-bench](https://huggingface.co/datasets/witcheer/sovereign-asr-bench) — local ASR on the 5090: Parakeet-TDT vs Whisper (WER / RTFx / VRAM)
+
+---
+
 ## Hardware
 
 | Component | Spec |