docs(cma): add Qwen3-Coder-Next model context to format findings (refs #68)

Researched the model under test. Qwen3-Coder-Next is a 3B-activated MoE (80B
total, Feb 2026) trained for AGENTIC coding (codebase + tools + RL on
environment feedback), not one-shot generation of unfamiliar syntax — exactly
our task, so we tested its weakest discipline. Its tool-calling strength is
agentic environment interaction, not emitting deep structured AST JSON,
consistent with the tool probe being worst. The format direction is likely
general but the magnitude is model-specific; bigger active models on the same
IONOS endpoint (Llama-3.1-405B, Llama-3.3-70B) were never tried and are the
obvious next step to separate format-effect from small-model-effect.

experiments/2026-05-12-cross-model-authoring/format-findings.md

@@ -169,3 +169,30 @@ before that ceiling is even reached.
 **For an LLM-authored language the lever is redundant, self-checking structure
 (depth-annotated brackets), not a lighter surface and not a more familiar one
 that carries extra bookkeeping.**
+
+## Model context (researched 2026-06-02)
+
+All of the above is Qwen3-Coder-Next, which the research reframes: it is a
+Mixture-of-Experts model with only **3B activated parameters** (80B total),
+released Feb 2026, and **trained specifically for *agentic* coding** — reading
+a codebase, calling tools in an executable environment, writing fixes, and
+recovering from failures via RL on environment feedback ([qwen.ai blog](https://qwen.ai/blog?id=qwen3-coder-next),
+[technical report](https://arxiv.org/abs/2603.00729)). It is NOT optimised for
+one-shot generation of an unfamiliar, non-mainstream syntax from a spec — which
+is exactly our task. Two consequences:
+
+- The "excels at tool calling" claim is about *agentic environment
+  interaction* (simple calls in a loop), not emitting a deep structured AST as
+  one tool argument — consistent with why our submit_program/AST-JSON probe was
+  the worst.
+- A 3B-active model is unusually sensitive to structure-tracking load, so the
+  format ranking (redundant cue > plain > burden) may be sharper for it than
+  for a larger model. The *direction* is likely general; the *magnitude* is
+  probably model-specific.
+
+**Bigger active models are available on the same IONOS endpoint** and were
+never tried here: `meta-llama/Meta-Llama-3.1-405B-Instruct-FP8` (405B dense —
+~135× the active params), `meta-llama/Llama-3.3-70B-Instruct` (70B),
+`mistralai/Mistral-Small-24B-Instruct`. The obvious next step to separate
+"format effect" from "small-model effect" is to re-run the annotated-paren vs
+plain-paren comparison on Llama-3.1-405B.