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

Do alternative surface formats help Qwen? — format 4 (annotated parens)

Date: 2026-06-02 Model: Qwen/Qwen3-Coder-Next (IONOS) Question (user): the fully-parenthesised surface is only one encoding of the tree. If no human must read it, try formats that lift the paren-counting burden — indent, annotated indent, keywords, annotated parens. Does any help?

Discipline (the "watch out"): these formats have NO ail parser, so ail check cannot judge them directly. For each, a converter (format → Form-A) is built and round-trip-verified (Form-A → format → back must be parse-identical to the original, on the known-good demos) BEFORE any Qwen output is judged. Format 4's converter passed: parse-identity + green check on all three demos, and end-to-end on a fenced annotated program.

Format 4 tested: annotated parens (#N ... #N)

Every paren carries its nesting depth; opener (#N pairs with closer #N). Qwen writes the annotated dialect; the verified stripper removes the annotations back to plain Form-A, then ail check/run. Same ablation ladder as the plain-Form-A baseline, so results are comparable. Raw: qwen-fmt4.md, qwen-fmt4-seqhint.md.

Result: it helps, measurably

Level	plain Form-A	format 4
L0–L3	✅	✅
L4 build-list + recursion	❌ paren misplaced	✅
L5, L6 Series	✅	✅
L7 SMA	❌	❌ (but different — see below)

Plain Form-A: 6/8. Format 4: 7/8. The annotation cracked the bracket-balance wall that broke L4 — the per-paren depth keeps openers and closers paired.

What format 4 does to L7 (the hardest level)

In plain Form-A, L7 failed on bracket imbalance. In format 4, Qwen produced perfectly balanced brackets even at the deepest nesting (the depth annotations all matched through the whole SMA) AND the correct SMA logic ((/ (+ (at 0) (+ (at 1) (at 2))) 3.0)). Proof the building blocks were right: taking Qwen's exact logic and only fixing the one remaining issue makes it run and emit the exact expected output (3.0 / 5.33333 / 5.66667 / 5.33333) — see /tmp/abl/qwen_seqfix.ail reconstruction in session.

The one remaining issue was not a format problem: Qwen treats seq as variadic (Lisp progn) — (seq A B C) — but AILang seq is strictly binary.

Pushing one more level (format 4 + an explicit seq-is-binary hint)

Gave Qwen the binary-seq rule plus a nested-seq example, re-ran L7. It fixed the seq nesting (correct (seq (seq print nl) (let …))), but L7 still failed — now on two model-behaviour limits, not surface encoding:

1. it simplified the logic (printed at 0 instead of the average), and
2. a termination-degeneration tail: hundreds of repeated #0) — Qwen knows via #0 it is at the outermost level, yet keeps closing. A pure repetition loop, the same failure mode the ~80k-token full-SMA run hit.

Takeaway

• Annotated parens (format 4) measurably help: they crack the bracket-balance wall (L4 green; L7 brackets perfect). For everything up to medium-deep nesting, the format lifts the exact burden the ablation isolated.
• The hardest level (L7) is no longer a bracketing problem — it is (a) a construct-arity quirk (seq binary vs variadic; fixable by an example or by making seq n-ary) and (b) a single-shot complexity ceiling where the model simplifies and falls into a closing-token repetition loop.
• The repetition tail is paren-driven (#0) repeated). The natural next test was a bracket-free format — run below.

Bracket-free formats tested: indent / YAML — they made it WORSE

The hypothesis was that removing the closing token would remove the repetition tail. It did the opposite — bracket-free formats are clearly worse. Two were tested, same ladder, each with a round-trip-verified converter:

• YAML via PyYAML (qwen-yaml.md): 2/8. First lesson was about the instrument, not the model: a real YAML library auto-quotes and gives *, -, true special meaning — and the AILang operators collide. Qwen wrote - * (multiply), valid as an atom but a YAML alias marker, so the library crashed. You cannot use an auto-quoting parser for an operator-rich vocabulary. Discarded.
• yamlish, own literal parser (qwen-yamlish.md): 3/8. Re-built with a hand-written indent parser that takes atoms verbatim (no quoting, no alias) — - * round-trips fine. With the instrument fixed, the model still only reached 3/8: trivial programs (L0–L2) pass, but at the first real nesting (L3 ADT, L4 data-def) the indentation structure breaks (e.g. a con field type lands as a data attribute — the model lost the indent level).

M-expressions tested: middle of the pack

McCarthy's head[arg; arg] notation — explicit structure (brackets + semicolons) in the familiar function-call shape. Hypothesis: explicit + a shape LLMs know cold should win. Result: 4–5/8 (model variance between two runs), BELOW plain parens, ABOVE the bracket-free formats. Two watch-out catches kept the measurement honest:

• A trailing ; before ] (Qwen wrote let[s; v; ], a normal trailing separator) — my parser was too strict and rejected it. Fixed to tolerate it (round-trip preserved). Without this, M-expr would have looked unfairly bad.
• L4 then failed with 43 [ vs 42 ] — a genuine Qwen bracket imbalance (not truncation: 301 completion tokens). The same depth-tracking failure as plain parens, just with [ instead of (.

Why M-expr is worse than plain parens despite being "explicit": swapping () for [] does nothing for the balance burden (square is no easier than round), and the semicolons add a second consistency requirement Qwen does not reliably meet (the trailing-; slips). It is explicit, but with extra load, not redundant cue.

Tool-calling / structured AST tested: WORST, and it confirms the rule

The intuitive bet: Qwen3-Coder is trained hard on tool-calling, the API validates the arguments, so submitting the program as canonical AST JSON via a submit_program tool should be the most reliable channel. Tool-calling is supported (verified: a dummy add tool returned a correct tool_call). But the result is 2/8 — tied for worst. Two failure modes, both the model's:

• check (L1/L3/L5/L6): valid JSON, wrong AST — e.g. * given 3 args.
• bad-json (L4/L7): the tool arguments were not valid JSON at all — completion hit the 2500-token cap. The AST JSON is so verbose (every leaf is {"t":"var","name":"x"}) that a non-trivial program overruns the budget and the JSON is truncated. IONOS does not constrain decoding to the schema, so the structure burden is fully on the model — and it is the heaviest burden of all: braces + brackets + keys + quotes + commas + "t": discriminators, every node.

So the "modern" structured-output channel is the worst surface here, for exactly the reason the rule predicts: AST JSON is maximal extra bookkeeping with no redundant cue.

Re-run with max_tokens raised 2500 → 8000 (the bad-json cases at 2500 were truncation, so this is the fair correction): it did NOT help — 1/8, slightly worse. The full budget just gives the model more room to degenerate. At L2 ("print 7", a trivial program) Qwen emitted 8000 tokens — ~20 KB of AST JSON — and ran off the end (a repetition loop on a one-line program); at L7 it produced JSON nested so deep the parser hit its recursion limit. So the AST-JSON surface is not budget-limited in a recoverable way: given more room it loops or nests without end. The verbose surface invites the same degeneration the annotated-paren run hit at its hardest level — but here it strikes even the trivial tasks.

Overall finding across all six surfaces

surface	green	structure marking
format 4 — annotated parens	7/8	maximal-explicit, redundant (every paren + its depth)
plain Form-A — parens	6/8	explicit, one channel
M-expressions head[a; b]	4–5/8	explicit, but []+; adds a separator burden
yamlish / indent (own parser)	3/8	implicit (indentation)
YAML (library)	2/8	implicit + special-char collision
tool-calling / AST JSON	2/8	maximal bookkeeping, verbose, no redundancy

The result is monotone and the opposite of the starting hypothesis. The discriminator is sharper than "explicit vs implicit": it is redundant cue vs extra burden. Qwen's weakness is structure-tracking at depth. What helps is a marking that makes that tracking easier without adding work — depth-annotated parens carry the nesting depth redundantly, so the model cannot lose its place (format 4, best). What hurts is either (a) moving the tracking onto an implicit channel it must maintain itself — indentation (yamlish/YAML, worst) — or (b) adding a second thing to keep consistent on top of bracket balance — the M-expr semicolons (middle), or (c) maximising the bookkeeping outright — AST JSON over the tool channel, the heaviest surface and tied for worst, even though tool-calling is the model's home turf. Plain parens sit in between: one explicit channel, no redundancy, no extra burden.

The L7 closing-token repetition tail is a separate single-shot ceiling, not something any surface here fixes — the bracket-free surfaces fail earlier, 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.