docs: Hy4-preview learnings mapped

TODO.qwen-next/07-hy4-learnings.md

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+# 07 — Tencent Hy4-preview learnings, mapped to our stack
+
+Sources: model card (huggingface.co/tencent/Hy4-preview, fetched
+2026-09-01), hy.tencent.ai/research/hy4-preview, vLLM recipes page.
+No arXiv technical report exists yet — unlike the GLM-5 paper, there
+are NO disclosed training specifics (no optimizer, RL, curriculum,
+token budget, or MoE training tricks). What the card carries is
+architecture + a post-training data philosophy. Scope: what transfers
+to byte-level seq2seq at our scale (<=580M teachers, <=300M
+students), what does not, and why.
+
+## 1. Architecture inventory (for the papers' related-work)
+
+- MoE 770B total / 49B active; 78 layers (first dense, rest MoE);
+  256 routed + 1 shared expert, top-8 routing
+- Gated DeepSeek Sparse Attention + IndexCache (cross-layer sparse
+  index reuse); 64 heads, Q compressed to 2048 / KV to 512
+- iHC: identity hyper-connections, 4 residual streams
+- MTP layer: 10B/0.7B for speculative decoding; 1M context
+
+## 2. Applicability map
+
+| Hy4 idea | Verdict for us | Why |
+|---|---|---|
+| MTP as auxiliary training objective | **candidate rung (E5?)** | per-position multi-step heads densify supervision for decode-bound byte students; might harden against the repetition pathologies. Cheap probe: MTP-aux head on ByT5-small + Muon, same labels/gate |
+| expert co-created data ("built around the work they ship") | **adopted principle** | our ~2.0pp domain residual is exactly the axis this addresses -> bumps the Tashkeela++/label-scale rung (rababa PR #1, open) |
+| iHC (4 residual streams) | paper note only | adjacent to the microkimi stitch/geometry observations; on frozen ByT5 students it is matrix surgery, and the width law says the pretrained geometry is load-bearing |
+| Gated DSA / sparse attention | not applicable | our windows are <=1400B; attention is not the serving bottleneck (measured, bench E1/E2) |
+| MoE student | closed territory | E2's PKM probe: sparse capacity below the pre-registered bar; gap is optimization + domain, not capacity |
+| "ship early, hear what breaks" | already our practice | previews/runs |
+
+## 3. What we deliberately do not adopt
+
+- Frontier-scale MoE anything (the client tier's point is avoiding it)
+- LLM-as-teacher (standing rule; unchanged by Hy4's data philosophy)
+- Speculative decoding in IMF decode (greedy KV is already fast at
+  our sizes; MTP's value here would be as a TRAINING aux, not serving)
+
+## 4. Net
+
+One cheap testable idea (MTP-aux distillation rung), one principle
+reinforcement (domain data rung rises in priority), zero disclosed
+training mechanics to borrow. Revisit if a technical report lands.

results/sadeed-glm-5-3-flash/README.md

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+# GLM-5.3-Flash on SadeedDiac-25 (2026-08-31)
+
+First measurement of glm-5.3-flash on this benchmark. Protocol: neutral
+completion prompt, temperature 0, max_tokens 8192, **reasoning_effort=low**
+(explicitly pinned — absent/unrecognized values default to MAX; the 5.2-era
+`thinking:{type:disabled}` knob is rejected by 5.3 with HTTP 400 code 1210
+and must not be combined with reasoning_effort). Reasoning content is
+present at effort=low (tripwire disclosed); checkpoint
+/tmp/sadeed_glm53_clean.jsonl, 1,200/1,200 non-empty.
+
+| Protocol | DER (CE) | 
+|---|---|
+| raw (their default output) | 8.5911 |
+| projected zero-skip | **8.8995** |
+
+Context: GLM-5.2 zero-skip scored 2.6911 (our reproduction, thinking
+disabled). The 5.3 flash tier regresses on this benchmark to below
+dedicated Sadeed-1.5B (7.2915) — supporting the dedicated-vs-frontier
+positioning: diacritization quality is not carried by general-frontier
+scale at the flash tier. Effort=high/max remains unmeasured (protocol
+prefers plain completion; a max-effort run would not be protocol-matched
+to the other rows).