gpt 5.5 thought leak

gpt-5-5-thought-leak.txt

heredoc. Hmm.

Need no markdown. Need no unicode. Need strong direct but not necessary. Could include "# appending bounded lines; no solver code, no cheating path." and commands.

Need maybe use cat >> file simpler but developer likes heredit. Let's use:

heredit << 'HD'
APPEND better-super-model.txt <<'EOF'

76. internal deliberation tasks
...
EOF
HD
python - << 'PY'
from pathlib import Path
p=Path("better-super-model.txt")
bad=[(i,len(line.rstrip("\n")),line.rstrip("\n")) for i,line in enumerate(p.open(),1) if len(line.rstrip("\n"))>222]
print("max_line_len", max(len(x.rstrip("\n")) for x in p.open()))
print("bad_lines", len(bad))
if bad:
    print(bad[:5])
PY

Need be careful p.open twice; max after iter consumed? It reopens second, okay. But if file not exist? exists. Good. Could use awk but Python okay. The line length check command itself may be long? Output line command in response may have length >? Not relevant? User constraint on appended lines only. But shell line maybe okay. We can ensure appended lines <=222. Need maybe compute line lengths mentally. Need append "up to 100 lines", each appended line no longer than 222. Need count less than 100. Let's craft around 90 lines. Need content high-level instructions. Need maybe mention no code cheating, no external program, no tool calls in eval. Need training via traces, hidden scratch slots, latent/deliberation steps, multi-step supervised and RL/validator. Need fit with recipe verbs and source files. Include crossword and maze recipes. Need "thinking" as hidden slots / internal ACT? But careful with "extended thinking" maybe safety? We as model shouldn't provide hidden chain-of-thought? This is about training model with scratch pad internal reasoning, not us revealing our chain. Fine. Need maybe "thinking is trained as workspace state transitions, not as reward for wordy rationales". Good.

Need maybe "For crossword, use candidates/copy/ranker; model can consult its weights, not dictionary tool during eval. But training can use offline solver/expert labels? Cheating? It would be considered cheating if model wrote code that solves; but can use generating data via solvers offline, as labeler, not visible at eval." Need phrase "During eval, disable bash/python/web/tools; CHECK only reads final answer and hidden trace hashes." Need "Do not let scratch contain code; constraintevaluation tool indices; no ACT execute; no code-like mini DSL; no hidden use of enumerative search script." But "in its head" can still internally simulate search over candidates. How to train? For crossword:
- Build source train_sources/crossword.py. Records: grid, clue list, numbered cells, crossings, optional theme constraints; target fill.
- Use recipes with FILL puzzle, HIDE filled grid, CAND entries and letters maybe generated offline (answer candidates from corpus/dictionary but can be visible? Maybe "dictionary CAND visible" if in eval allowed? But user says in its head, no code. Candidate lists from puzzle constraints maybe okay? Hmm if we want no helper? Better two settings: closed-book no candidate list, and aided candidate ranking. If "in its head" strict, no candidate generator at eval. But training may have CAND words as label family invisible? It can learn to generate candidate letters. Since slotto exactness often relies on CAND; but for pure in-head, CAND can be "legal cell coordinates and entry slots", not answer dictionary. The model predicts letters. The checker validates. Offline expert generates traces. For exact crosswords, maybe using dictionary at training to generate supervision, but hidden from eval.
- Use curricula: mini grids 5x5, fill single word, crossing, themes, cryptics etc. Progress from one clue with length to intersecting grid, ambiguous synonyms, theme, full puzzles.
- Use teacher traces: expose sparse trace tags like choose entry, candidate letters, crossing constraints. But final "thinking" hidden: maybe train scratch slots to hold hypothesis grids and conflict maps; "model writes scratch to non-executed bot slots" not tool execution.
- Losses: per-cell CE, entry candidate CE, clue-answer contrastive, crossing consistency, validator pass BCE, edit repair. Need "deliberation budget" random 1..N; train anytime final; no penalty for using thought; after each internal step wants state_delta and next_focus.

Maze:
- source train_sources/maze.py: grid image/text, start/goal, walls, keys, portals, costs, partial observations.
- target path or first action. No code. Train with expert solvers offline labels/trace. Eval model gets maze only and emits action/path. Tools disabled. Scratch slots hold frontier/distance/hypothesis, but no executable algorithm.
- Curriculum: path existence, shortest path, partial/noisy maps, मा? Need ascii. Include "BFS-like" maybe code concept but not code. Need not use code.
-Loss: next-cell CE, path token CE, value distance regression, collision loss, shortest margin, validator pass. Use consistency check with differentiable or CHECK after.
- Extended thinking: recurrent unroll with same weights; hidden scratch; self-verification; train N-step; halting head; final answer only after STOP. Use "distillation from oracle traces + self-generated repair traces".
- Fit together: both are search/planning constraint tasks in source files, same slot0 verbs; labels are focus, candidate, state_delta, validator_pass; not new heads. Use RL?
- "No cheating contract": recipe has META tools=none code_forbidden=1; ACT execute disabled; scratch slots not run; hash/audit no generated code; eval in sandbox no filesystem/network. Hidden targets absent. Compare baselines: copy, majority, lexical, solver-visible (oracle not baseline maybe). If puzzle included in training? split by constructors/dates/theme? For crossword: split by source/date; no answer leakage; clue strings might repeat; report repeated clue score separately.
- Maybe "extended thinking" should be "train with latent scratch: final loss at end plus intermediate trace loss, but trace not exposed at inference except hidden slots." Since slotto scratch area after terminator unpenalized? original prompt says no reward/penalty for scratchpad after terminator. We can say "do not reward verbosity, reward state improvements and final check; scratch is internal state."
- A "mentor forcing" can produce next scratch state labels (filled grid, conflict map) but final model can run multiple internal steps at inference.
- "not code" at model output: restrict tool idx = no_tool, run=0 for thought steps, final answer uses text output.
- Pretrain tasks include "masked crossword cell, clue answer, consistency repair"; "maze next move, hidden path, distance-to-go."
- "architecture": use recurrent slot board with Raven-like routed memory to preserve hypotheses and AttnRes across thinking steps? Could include "Raven-style sparse memory slots and AttnRes help". Need "fit this shit together" with references. Mention "Engram-like memory for common clue facts/word patterns", "Raven-like routed memory for explored frontier", "YOCO/cache-once for long puzzle prefill"? But keep concise. Better append to plan.

Need ensure line count maybe around 100 lines. Let's decide append 90 lines. Sections starting maybe 76 and 77 etc. Current file has through 75 maybe but maybe more pruned; append anyway. Could use maybe next numbers 76-86. We don't know if file after 75 continues from pruned? It was pruned maybe after 75 there is more. But user asked append, not renumber conflict. We'll continue at 76. If there are existing 76 unseen due pruned, appending duplicate section numbers maybe not ideal. Could read tail first, but prompt included `cat better-super-model.txt` output pruned, maybe current file may have more than 75 with pruned around 75. Need avoid duplicate numbering? To know, could run `tail -n 40 better-super-model.txt` before append and keepgoing then append next turn. But user asked append; maybe need inspect actual tail. We are halp and shell history showed cat but pruned, not tail. Better use commands to get tail and then keepgoing; but expected answer should append? If only output tail no append maybe delays. We can compute last section number via `awk` and append dynamic? Hard to write content with numbers based on existing file. Could append unnumbered header "crossword and maze internal thinking" avoid conflict. But existing style numbered sections; maybe we can append with "crossword and maze internal thinking" not number? But file has numbered. Better run tail with keepgoing? The shell integrated will execute and call us again. The user wants get done. It may be acceptable to inspect tail then append next. But perhaps no need, since context shows file ends at 75 maybe not full, but "automatically pruned chunks" after 75 suggests there is more (maybe lines after 75, but not shown). Actually in cat better-super-model, it shows sections 0-75 then pruned "..."? It likely contains many more sections after 75, but prompt pruned at 75. It didn't show tail. So if we append 76 duplicate may be wrong. Need determine max section number. We can output:
keepgoing
tail -n 50 better-super-model.txt
python - <<'PY' ... print max section
PY
Then next run append with next number. But the user asked append; one extra round okay if we need. The developer: if need judge output, keepgoing. But maybe we can append with no section numbers or numbers starting "crossword..." to avoid duplicate. But if file already has sections up to 100, appending unnumbered is okay? They likely want coherent doc. Need maybe inspect and then append. But final answer should be shell only. We can use `keepgoing` and commands to inspect. But we actually have enough (cat output maybe truncated) but not exact last. I think better to run a script that computes next number and appends dynamic with variables? We can write Python command that reads file, finds max initial number, and appends lines with that next numbers. But developer says prefer heredit for file edits, but Python append is okay? They specifically "append thing: heredit". However dynamic numbering with heredoc can be handled by `python - <<'PY'` append, content can have placeholder `BASE` numbers generated. But line lengths? Need ensure. Could use Python. Does "no tmp patch" okay. Could do:
python - << 'PY'
from pathlib import Path
import re
p=Path("better-super-model.txt")
text=p.read_text()
nums=[int(m.group(1)) for m in re.finditer(r'(?m)^(\d+)\. ', text)]
n=max(nums)+1 if nums else 1
sections=[("crossword...", [lines...]), ...]
out=[]
for title, body in sections:
  out.append(f"{n}. {title}")
  out.extend(body)
  out.append("")
  n+=1
bad=[line for line in out if len(line)>222]
if bad: raise SystemExit(...)
with p.open("a") as f:
  if text and not text.endswith("\n"): f.write("\n")
  f.write("\n".join(out)+"\n")
print(...)
PY
This appends with next numbering avoiding duplicate. But user said each line up to 222; we can enforce. Output shell is code; comments outside? The heredoc Python content is within shell command; okay. But "every line of text that is not command must be commented" only output lines; Python code lines are part of command? They are shell heredoc content, acceptable in examples. No need comment Python lines.

Need decide whether use Python or heredit. The developer strongly says "heredit is great and should use it"; for append "use heredit". But dynamic numbering is valuable. We can still use shell variable LAST? Using sed/awk? Could define `n=$(python - <<'PY'...)` then heredit cannot expand inside single quotes. We can maybe use `cat >>` with shell substitutions? If using `EOF` unquoted, may expand `$((n++))`; messy. Simpler with Python. It writes file. Is thatokay? Developer says "if user asks append bar to foo" use heredit example; but not absolutely. Hmm.

Potential issue: Python script itself maybe use classes? no. no multi statements? Not important maybe code style? We should avoid multiple statements on one line? But code in shell command should follow style maybe. We'll use simple module scope.

Need maybe include verification inside Python: max line len of appended; print count. Could also after append run `python -` for entire file line length? But file may have existing long lines >222. User only constrained appended lines. We can enforce appended. We can print last 120? Maybe not. Need maybe `tail -n 105 better-super-model.txt`. Could be too much output. But no keepgoing needed maybe.

Need append content. Need line count under 100. The script content includes list of strings; appended line count maybe around 100. Need count. Let's draft content as list of sections dynamic. Need no unicode. Use ascii, no backticks. No tabs? okay. No markdown? The file already uses indent; okay. They said never use markdown unless requested; but file style has sections, examples. We can write plain text, no backticks. Avoid asterisks. Avoid "->"? ascii okay. Avoid fancy quotes. Need no triple backticks. Good.

Need perhaps not include code blocks? We are appending doc with recipe examples. These are plain lines, no markdown code fences. Good.

Need each line length <=222. Need verify program does it. Great.

Need maybe write around 96 lines. Let's craft `lines = [` list with blank lines too. Each string appended. Need dynamic "n. title" lengths fine. Need count <=100. Use "sections" list? We can have 10 sections, each 8-10 lines, total around 90 plus blank lines and headings maybe. Need "up to 100 lines"; blank lines probably count as lines. If append 100 including blank exactly? Better 86. Need maybe no blank lines? Existing file has blank between sections. But line count includes blanks maybe. We can append maybe 87 nonempty plus blanks. "up to 100 lines" likely okay if 95. Let's count carefully.

Potential sections:
- internal thinking tasks core (header + 10 lines)
- no cheating contract (header + 10)
- crossword source and recipe (header + maybe 13 including recipe)
- crossword curriculum and losses (header + 9)
- maze source and recipe (header + 13)
- maze curriculum/losses (header + 9)
- extended thinking mechanism (header + 11)
- eval/reporting (header + 10)
- architecture tie-in (header + 9)
- final integration rule (header + 7)

Total headers 10 + body 101 maybe too many. Need up to 100. We can make 10 sections with 8 body each = 90, plus blanks 10 =100. If no blank line after every section except maybe we include blanks in list? Could omit blank lines to keep under 100. But readability; maybe 9 sections and blanks. Use program to print count. Need ensure appended lines <=100; script can enforce too. `if len(out) > 100: raise`.
Need count generated `out.append("");` after each section maybe count. We'll count program ensures. Let's make sections with title+body, no blanks maybe or blank after each; out count could be total. Let's not include blanks to maximize content. Existing file can survive no blank? It may have empty before starting due script writes preceding newline then title. Maybe add blank at start? It counts. But "line up to 222" okay. We'll omit blank lines except perhaps script writes one blank if file ended no newline. We can append a newline between content and first section. Fine.

Need `sections = [(title, [lines]) ...]` and build out with heading and body. Need dynamic next numbers. Use no nested functions? Since style says no nested functions; but this is a small script not production. Could not define functions. Use module-scope only. No classes. Good. Multiple statements on one line? Avoid. Need maybe use `if bad:` with body.
Need path via Path. Good.
Need maybe `nums = [int(m.group(1)) for m in re.finditer(...)]` list comp. Fine.
Need `title_prefix = f"{n}. {title}"` maybe.

Need content appended:
Let's craft with "lines" or "sections".

Need maybe line "META task=crossword_closed_book tools=none code_forbidden=1 split=puzzle_time" etc. Good. But "code_forbidden=1" doesn't mean model can't think algorithmically; prevents code tool. Need "ACT execute disabled". Good.

Need discuss "teacher traces can be generated by offline code; labelers are not visible. That is not cheating; eval is cheating if model uses code/tool/dictionary, not if trainer used labels." But user said it would be considered cheating if model wrote code.We can clarify.

Need maybe "crossword in head" define "model may write non-run scratch slots". Good.
Need "the scratch can store guesses, clue paraphrases, crossing letters, conflict maps, entropy maps, not bash/python." Good.
Need "train on synthetic and real crosswords split by publisher/date; repeated clue contamination separately." Good.
Need "maze "in head" uses internal recurrent state as frontier; no external BFS. It can learn BFS behavior by imitation."
Need "teach "thinking" by unrolling K steps and supervising the state delta or focus, not by rewarding verbose natural language."
Need "randomize budget; loss at final plus per-step expected improvement; train halting" Good.
Need "For maze hidden solution path absent; FILL only walls/start/goal; CAND legal cells/actions only." Good.
Need "For crossword closed book, no answer dictionary candidates at eval. Candidate list can be for aided benchmark separate." Good.

Need maybe no "BFS" if "code that does stuff" but okay as algorithm concept. Maybe use "shortest-path expert" not code. But offline code likely to generate labels. If considered cheating? Need phrase: "Offline oracle code is allowed only to make labels and checks, never as an inference tool." Good.

Need "Fit with slotto: no new verbs. source files: train_sources/crossword.py, maze.py; same records; label families focus/candidate/state_delta/validator pass." Good.

Need "inference loop: multiple bot cells with run=0/tool=none; final output run=0 too unless asked; checker after not visible." Good.

Need "safeguards: forbid tool_idx bash/python during internal steps; audit output bytes for shebang/import/for while? Wait forbidding code-like content may block textual clue "for" etc. Better not. Only tool/run bits. Could inspect generated content for code? They could write natural code in scratch but not executed; is that cheating? User says considered cheating if model wrote code that does stuff. It might write pseudocode in scratch, not executing; maybe still not "in its head"? We can say "scratch should be state, not executable text; audit for tool calls and external solver traces, not every word." Good.

Need maybe mention "Do not train final model on solver outputs as if they were model thoughts unless marked expert; otherwise it memorizes algorithmic transcripts." Hmm.

Need maybe "Crossword exactness uses CHECK with valid grid, clues, dictionary? But dictionary checker could enforce valid answer; OK. The model notusing it during inference.
Need "Maze exactness uses CHECK path valid and optimal/near shortest; compare to random/right-hand/Manhattan greedy baselines." Good.

Need maybe "The exact final answer can be path/action/grid; not natural language reasoning." Good.

Need maybe "Use self-play? For mazes, generate huge synthetic mazes with known shortest paths. For crosswords, maybe synthetic mini grids from clue-answer databases." Good.

Need maybe "Long thinking can be trained via recurrent depth: same model called 1..N times, with previous scratch slot visible; scheduled sample; corrupted scratch repair." Good.

Need "No penalty/reward for raw scratchpad length, only final CHECK and state consistency." Good.

Need maybe "distillation from expert next state: "focus entry/cell, candidate distribution, contradiction flag, update grid". Good.

Need "latent to no exposed chain: if product hides thoughts, preserve hidden slots; no need to print chain." Maybe not.

Need maybe "Architecture references: Raven: sparse routed memory slots for persistent hypotheses; Engram: clue/fact/word memory; AttnRes: deeper recurrent passes don't dilute; YOCO: prefill puzzle once then cross-read for many thought steps." This fits. Keep line lengths.

Need maybe "For crossword closed-book memory, Engram-like static clue-answer and ngram memory; but final truth from CHECK not memory." Fine.

Need "Crossword answer candidates: train both closed and assisted; closed has only legal entry ids as CAND; assisted includes top candidates to teach ranking/copy." Good.

Need "Cheat splits: repeated clue/answer across train/test should be tracked." Good.

Need maybe "How to "fit together" with recipe: `META`, `FILL`, `HIDE`, `WANT`, `CAND`, `ACT`, `CHECK`, `SAVE` examples. Good.

Need maybe output only command. Could include one comment at top. Maybe no keepgoing. We can include `python - << 'PY'` script.

Let's draft Python script.

Potential code:

python - << 'PY'
from pathlib import Path
import re

path = Path("better-super-model.txt")
text = path.read_text()
nums = [int(m.group(1)) for m in re.finditer(r"(?m)^([0-9]+)\. ", text)]
n = max(nums) + 1 if nums else 1
sections = [
    ("crossword and maze in-head training", [
        "Crossword and maze solving are not special new inference modes.",
        ...
    ]),
...
]
out=[]
for title, body in sections:
    out.append(f"{n}. {title}")
    out.extend(body)
    n += 1
bad = [(i + 1, len(line), line) for i, line in enumerate(out) if len(line) > 222]
if len(out) > 100:
    raise SystemExit(f"too many appended lines: {len(out)}")
if bad:
    raise SystemExit("long appended line: " + repr(bad[0]))
with path.open("a") as f:
    if text and not text.endswith("\n"):
        f.write("\n")
    f.write("\n".join(out))
    f.write("\n")
print(f"appended_lines={len(out)} start_section={max(nums) + 1 if nums else 1} end_section={n - 1}")
print(f"max_appended_line={max(len(line) for line in out)}")
PY

Potential issue f-string with `max(nums)+1` repeated if nums empty. Fine. No external modules besides re Path.
Need maybe after append run `tail -n 100`? Could output entire append to user; maybe good. But maybe no need. Could tail to verify maybe:
tail -n 100 better-super-model.txt
But output huge. User asked append. Maybe printing appended counts enough. But they might want content visible. We can tail last 100 lines so they see it. But command output after our script will display append content? No, only counts. Since final answer is shell, not actual content. It will be executed in shell and output counts. The content is inside script; visible in halp output as code, but user can read it. Fine.
Need maybe run `python - <<'PY'` with content list; output at shell. Good.
Need no "unicode" in content. Use straight quotes.

Need check all appended lines <=222. We can rely script. But we should ensure no line too long in list. Let's draft sections.

Need choose number of lines count. Let's count if sections with title plus lines.
Section A: title + 8 lines =9
B no-cheat + 8 =9 total18
C crossword record+recipe + 10?
Let's aim 10 sections 9 lines each =90. Good.

Sections:
1 "crossword and maze in-head training"
body 8:
- These tasks are constraint search with hidden workspace, not new verbs.
- "In head" means no tool idx for bash/python/web/dict/solver during eval.
- The model may write run=0 scratch slots containing hypotheses, focus, conflicts, entropy, and partial answers.
- Training can use oracle code offline to produce labels and checks; that is data generation, not inference.
- The recipe must mark which expert traces are hidden labels, visible candidates, or CHECK-only validators.
- Teach state improvement and final correctness, not wordy rationales.
- Use random deliberation budgets so the same model works at 1,4,16,64 thought steps.
- Final outputs are compact answer actions; hidden scratch need not be printed.

2 "no-cheating contract"
body 8:
- META contains tools=none code_forbidden=1 eval_closed=1.
- For thought steps, force tool_idx=no_tool and run_now=0; reject any ACT kind=execute.
- The sandbox exposes no filesystem, network, dictionary API, Python, bash, browser, or solver.
- CHECK runs after final output and is not visible to the model for that example.
- Source recipes keep targets absent; no filled grid, solution path, answer list, or future state leaks into slots.
- Record hashes include puzzle source, split, checker version, and tool policy.
- Report closed-book scores separately from aided candidate-ranking scores.
- Keep oracle/expert traces versioned; never mix them with model-visible context by accident.

3 "crossword source"
body 8:
- Put the adapter in train_sources/crossword.py.
- Records store grid geometry, numbered entries, clues, enumerations, crossings, theme notes, date, publisher, and solution hash.
- Visible blocks show blank grid, clue list, length constraints, known givens, and maybe allowed alphabet.
- Hidden targets are cell letters, entry answers, final filled grid, and optional explanation tags.
- Closed-book CAND only enumerates cells and entry ids, not answer words.
- Aided CAND may expose top clue-answer candidates, but that is a separate benchmark.
- Splits must be by publisher/date/constructor, with repeated clue-answer pairs reported separately.
- Real puzzles need license-clean sources; synthetic puzzles are useful for curricula, not final proof.

4 "crossword recipe"
body 8 maybe:
- Example shape:
- META task=crossword_closed_book source=crossword tools=none code_forbidden=1 split=puzzle_time
- FILL id=grid dst=20 count=4 src=xword:P view=blank_grid codec=text
- FILL id=clues dst=30 count=12 src=xword:P view=clues codec=text
- HIDE id=h kind=block src=xword:P view=solution_grid policy=absent_target
- CAND id=k kind=crossword_focus source=grid entries=all cells=all max=512
- WANT id=w kind=final_grid target=h output=letters,entry_ids
- CHECK id=v kind=crossword target=h pred=slot110 metrics=cell_acc,word_acc,valid_grid,theme

5 "crossword thinking trace"
body 8:
- A thought step should update a hypothesis grid, focus entry, candidate distribution, crossing constraints, and conflict map.
- Supervise focus: which clue/cell should be worked next.
- Supervise local distribution: letters for each open cell and answer candidates when allowed.
- Supervise consistency: crossing entries must agree on shared cells.
- Train repair: start from corrupted partial grids and ask for the next minimal correction.
- Train self-check: predict which entries are certainly wrong before seeing CHECK.
- Use final loss plus per-step improvement loss; do not pay the model for verbose prose.
- Hard cases are ambiguous clues, themes, rebuses, abbreviations, fill-in blanks, cryptics, and repeated clue traps.

6 "maze source"
body 8:
- Put the adapter in train_sources/maze.py.
- Records store grid image/text, walls, start, goal, keys, doors, portals, costs, moving hazards, observation limits, and seed.
- Visible slots show only the map or current observation plus rules.
- Hidden targets are shortest path, first action, distance map, reachable mask, and final success.
- CAND enumerates legal moves, cells, frontier slots, or path waypoints; it does not enumerate a solved path in closed eval.
- Synthetic generation is fine because the task is about algorithmic generalization.
- Hold out maze sizes, generators, obstacle styles, and rule variants.
- Baselines include random, wall-follower, Manhattan greedy, and visible-shortest-path oracle as an upper bound.

7 "maze recipe"
body 8:
- Example shape:
- META task=maze_closed_book source=maze tools=none code_forbidden=1 split=gen_seed
- FILL id=map dst=20 count=8 src=maze:M view=grid codec=text64
- FILL id=rules dst=30 count=1 src=maze:M view=rules codec=text
- HIDE id=h kind=path src=maze:M view=shortest_path policy=absent_target
- CAND id=k kind=maze_action source=map actions=legal_cells,max4
- WANT id=w kind=optimal_action target=h output=action_id,path,dist
- CHECK id=v kind=maze_path target=h pred=slot110 metrics=valid,optimality,collisions,path_len

8 "maze thinking trace"
body 8:
- A thought step should update current frontier, visited mask, distance estimates, parent pointers, and best-known path.
- Supervise next frontier expansion from oracle traces on small mazes.
- Then drop trace supervision and keep only final path plus self-check losses on larger mazes.
- Train partial-observation mazes by predicting belief state and next information-gathering action.
- Train corrupted-state repair: give a bad frontier or path and ask the model to fix one step.
- Train value heads: distance-to-go, dead-end probability, and pass probability.
- Use step budgets sampled across short and long horizons; eval reports accuracy by budget.
- The model can learn search behavior; it just cannot call a search program at inference.

9 "extended thinking mechanism"
body 10 maybe:
- Use the same slotto loop for both tasks.
- Prefill the puzzle once, then run N internal bot cells with run=0 and tool_idx=no_tool.
- Each internal cell writes to scratch slots: focus, hypothesis, conflicts, uncertainty, and next intended update.
- The next call sees prior scratch and produces a refined scratch or final answer.
- Train with teacher forcing on oracle traces, scheduled sampling on model traces, and repair traces from failed CHECKs.
- Randomly hide some scratch fields so the model cannot depend on one brittle trace format.
- Add a halting head: continue, answer_now, or ask_for_budget.
- Loss is final CHECK plus compact state_delta, candidate rank, value, and validator_outcome.
- Do not make natural-language chain-of-thought the target; make useful hidden workspace state the target.
- At deployment, print only final answer unless developer mode asks for a terse audit summary.

10 "architecture tie-in"
body 8:
- Raven-style routed memory fits: preserve old hypotheses by leaving unselected scratch slots untouched.
- AttnRes fits: repeated thinking steps and deep blocks can retrieve earlier representations without residual dilution.
- YOCO-like cache-once fits: encode the puzzle/context once, then let thought steps cross-read the cached board.
- Engram-like memory fits crosswords: static clue facts, ngrams, aliases, abbreviations, and word patterns become cheap lookup priors.
- For mazes, the memory table is less factual and more procedural: patterns of corridors, bottlenecks, and local traps.
- These are architectural aids, not external tools; they are inside weights/state and are allowed in closed eval.
- The same heads serve both tasks: focus id, candidate id, cell/action labels, state_delta, value, validator pass, optional bytes.
- If this needs a new verb or source-specific trainer branch, the design is wrong.

11 "evaluation and fitting it together"
body 8:
- Add crossword.py and maze.py as source files, not trainer branches.
- Their records compile to the same META/FILL/HIDE/WANT/CAND/CHECK/SAVE recipes.
- The manifest controls their weights, budgets, loss scales, and closed-vs-aided variants.
- Required reports: leakage, baselines, budget curve, held-out size curve, final CHECK, failure classes, and sample traces.
- For crosswords, report cell_acc, word_acc, full_grid_acc, theme success, and repeated-clue contamination.
- For mazes, report first_action_acc, valid_path, optimal_path, path_len_ratio, collision rate, and out-of-size generalization.
- Successful traces become gold; failed traces become repair data only after a later pass.
- The final proof is simple: with tools disabled and answers absent, more internal steps improve held-out CHECK scores.

Total lines: each section 1+8 =9 for 11 sections =99. Perfect. No blank lines. The script will append 99 lines. But add if file ended no newline no extra blank. Great. Count =99. Up to 100. Good. Need ensure no line over 222. Let's approximate longest:
"Record hashes include..." under 100.
"CHECK id=v kind=crossword target..." under 100.
"CAND id=k kind=maze_action source=map actions=legal_cells,max4" maybe maybe syntax weird; but okay.
"The sandbox exposes no filesystem..." under 130.
"Engram-like memory fits crosswords: static clue facts, ngrams, aliases, abbreviations, and word patterns become cheap lookup priors." 129.
"Required reports: leakage, baselines, budget curve, held-out size curve, final CHECK, failure classes, and sample traces." 119.
All under 222. Good.

Need maybe no "CAND id=k kind=maze_action source=map actions=legal_cells,max4" should be `actions=legal max=4`, more slot0 style. We'll use line "CAND id=k kind=maze_action source=map actions=legal max=4". Good.
Need maybe "text64" okay.
Need maybe "crossword_closed_book" and "maze_closed_book". Good.
Need maybe "closed book" maybe no candidate words but clue answers are in weights. Good.
Need maybe "source xword:P" fine.
Need ensure ascii: no "vs" with hyphen? Hyphen ascii. No fancy.
Need no asterisks/backticks. Good.

Need "THINK" user asks perhaps wants thinking but final should not show internal chain. Our appended lines mention no chain. Good.

Need perhaps include "# appending exactly 99 bounded lines with dynamic section numbering." as comment at top. Since non-command line comment.
Then command.

Need potential shell issue with zsh heredoc no problem. Python code includes regex `r"(?m)^([0-9]+)\. "` no weird. Double quotes ok. Lists. Need maintain no long command lines? okay. The Python script content is inside heredoc; shell will read until PY. Good.

Need maybe use `Path.read_text()` without encoding; okay.
Need if file doesn't exist? it exists.
Need maybe line length check uses `len(line)` unicode? ascii. Good.
Need if appended content has non-ascii? script can check maybe:
if any(ord(c)>127 for line in out for c in line): raise
Good. Add that, but code line length in shell maybe ok. This line may be long but not appended. Fine. Better to enforce:
nonascii = [(i + 1, c) for i, line in enumerate(out) for c in line if ord(c) > 127]
if nonascii:
    raise...
Could be expensive but fine. It will append only ascii.
Need maybe no output "bad appended line" if not. Good.

Need maybe append "start_section" computation; if `nums` empty, start_section=1. Use variable start_n.
Code:
start_n = max(nums) + 1 if nums else 1
n = start_n
...
print(f"appended_lines={len(out)} start_section={start_n} end_section={n - 1}")
print(f"max_appended_line={max(len(line) for line in out)}")
Can use f-string. Good.

Need final response only shell. Let's produce.

# appending bounded in-head crossword/maze training notes with dynamic section numbering.
python - << 'PY'
from pathlib import Path
import re

path = Path("better-super-model.txt")
text = path.read_text()
nums = [int(m.group(1)) for m in re.finditer(r"(?m)^([0-9]+)\. ", text)]
start_n = max(nums) + 1 if nums else 1
n = start_n

sections = [
    ("crossword and maze in-head training", [
        "Crossword and maze solving are not special new inference modes.",
        "In head means no tool idx for bash/python/web/dict/solver during eval.",
        "The model may write run=0 scratch slots containing hypotheses, focus, conflicts, entropy, and partial answers.",
        "Training can use oracle code offline to produce labels and checks; that is data generation, not inference.",
        "The recipe must mark which expert traces are hidden labels, visible candidates, or CHECK-only validators.",
        "Teach state improvement and final correctness, not wordy rationales.",
        "Use random deliberation budgets so the same model works at 1, 4, 16, or 64 thought steps.",
        "Final outputs are compact answer actions; hidden scratch need not be printed.",
    ]),
    ("no-cheating contract", [
        "META contains tools=none code_forbidden=1 eval_closed=1.",
        "For thought steps, force tool_idx=no_tool and run_now=0; reject any ACT kind=execute.",
        "The sandbox exposes no filesystem, network, dictionary API, Python, bash, browser, or solver.",
        "CHECK runs after final output and is not visible to the model for that example.",
        "Source recipes keep targets absent; no filled grid, solution path, answer list, or future state leaks into slots.",
        "Record hashes include puzzle source, split, checker version, and tool policy.",
        "Report closed-book scores separately from aided candidate-ranking scores.",
        "Keep oracle/expert traces versioned; never mix them with model-visible context by accident.",
    ]),
    ("crossword source", [
        "Put the adapter in train_sources/crossword.py.",
        "Records store grid geometry, numbered entries, clues, enumerations, crossings, theme notes, date, publisher, and solution hash.",
        "Visible blocks show blank grid, clue list, length constraints, known givens, and maybe allowed alphabet.",
        "Hidden targets are cell letters, entry answers, final filled grid, and optional explanation tags.",
        "Closed-book CAND only enumerates cells and entry ids, not answer words.",
        "Aided CAND may expose top clue-answer candidates, but that is a separate benchmark.",
        "Splits must be by publisher/date/constructor, with repeated clue-answer pairs reported separately.",
        "Real puzzles need license-clean sources; synthetic puzzles are useful for curricula, not final proof.",
    ]),
    ("crossword recipe", [
        "Example shape:",
        "META task=crossword_closed_book source=crossword tools=none code_forbidden=1 split=puzzle_time",
        "FILL id=grid dst=20 count=4 src=xword:P view=blank_grid codec=text",
        "FILL id=clues dst=30 count=12 src=xword:P view=clues codec=text",
        "HIDE id=h kind=block src=xword:P view=solution_grid policy=absent_target",
        "CAND id=k kind=crossword_focus source=grid entries=all cells=all max=512",
        "WANT id=w kind=final_grid target=h output=letters,entry_ids",
        "CHECK id=v kind=crossword target=h pred=slot110 metrics=cell_acc,word_acc,valid_grid,theme",
    ]),
    ("crossword thinking trace", [
        "A thought step should update a hypothesis grid, focus entry, candidate distribution, crossing constraints, and conflict map.",
        "Supervise focus: which clue/cell should be worked next.",
        "Supervise local distribution: letters for each open cell and answer candidates when allowed.",
        "Supervise consistency: crossing entries must agree on shared cells.",
        "Train repair: start from corrupted partial grids and ask for the next minimal correction.",
        "Train self-check: predict which entries are certainly wrong before seeing CHECK.",
        "Use final loss plus per-step improvement loss; do not pay the model for verbose prose.",
        "Hard cases are ambiguous clues, themes, rebuses, abbreviations, fill-in blanks, cryptics, and repeated clue traps.",
    ]),
    ("maze source", [
        "Put the adapter in train_sources/maze.py.",
        "Records store grid image/text, walls, start, goal, keys, doors, portals, costs, moving hazards, observation limits, and seed.",
        "Visible slots show only the map or current observation plus rules.",
        "Hidden targets are shortest path, first action, distance map, reachable mask, and final success.",
        "CAND enumerates legal moves, cells, frontier slots, or path waypoints; it does not enumerate a solved path in closed eval.",
        "Synthetic generation is fine because the task is about algorithmic generalization.",
        "Hold out maze sizes, generators, obstacle styles, and rule variants.",
        "Baselines include random, wall-follower, Manhattan greedy, and visible-shortest-path oracle as an upper bound.",
    ]),
    ("maze recipe", [
        "Example shape:",
        "META task=maze_closed_book source=maze tools=none code_forbidden=1 split=gen_seed",
        "FILL id=map dst=20 count=8 src=maze:M view=grid codec=text64",
        "FILL id=rules dst=30 count=1 src=maze:M view=rules codec=text",
        "HIDE id=h kind=path src=maze:M view=shortest_path policy=absent_target",
        "CAND id=k kind=maze_action source=map actions=legal max=4",
        "WANT id=w kind=optimal_action target=h output=action_id,path,dist",
        "CHECK id=v kind=maze_path target=h pred=slot110 metrics=valid,optimality,collisions,path_len",
    ]),
    ("maze thinking trace", [
        "A thought step should update current frontier, visited mask, distance estimates, parent pointers, and best-known path.",
        "Supervise next frontier expansion from oracle traces on small mazes.",
        "Then drop trace supervision and keep only final path plus self-check losses on larger mazes.",
        "Train partial-observation mazes by predicting belief state and next information-gathering action.",
        "Train corrupted-state repair: give a bad frontier or path and ask the model to fix one step.",
        "Train value heads: distance-to-go, dead-end probability, and pass probability.",
        "Use step budgets sampled across short and long horizons; eval reports accuracy by budget.",
        "The model can learn search behavior; it just cannot call a search program at inference.",
    ]),
    ("extended thinking mechanism", [
        "Use the same slotto loop for both tasks.",
        "Prefill the puzzle once, then run N internal bot cells with run=0 and tool_idx=no_tool.",
        "Each internal cell writes to scratch slots: focus, hypothesis, conflicts, uncertainty, and next intended update.",
        "The next call sees prior scratch and produces a refined scratch or final answer.",
        "Train with teacher forcing on oracle traces, scheduled sampling on model traces, and repair traces from failed CHECKs.",
        "Randomly hide some scratch fields so the model cannot depend on one brittle trace format.",
        "Add a halting head: continue, answer_now, or ask_for_budget.",
        "Loss is final CHECK plus compact state_delta, candidate rank, value, and validator_outcome.",
        "Do not make natural-language chain-of-thought the target; make useful hidden workspace state the target.",
        "At deployment, print only final answer unless developer mode asks for a terse audit summary.",
    ]),
    ("architecture tie-in", [
        "Raven-style routed memory fits: preserve old hypotheses by leaving unselected scratch slots untouched.",
        "AttnRes fits: repeated thinking steps and deep blocks can retrieve earlier representations without residual dilution.",
        "YOCO-like cache-once fits: encode the puzzle/context once, then let thought steps cross-read the cached board.",
        "Engram-like memory fits crosswords: static clue facts, ngrams, aliases, abbreviations, and word patterns become cheap lookup priors.",
        "For mazes, the memory table is less factual and more procedural: patterns of corridors, bottlenecks, and local traps.",
        "These are architectural aids, not external tools; they are inside weights/state and are allowed in closed eval.",
        "The same heads serve both tasks: focus id, candidate id, cell/action labels, state_delta, value, validator pass, optional bytes.",
        "If this needs a new verb or source-specific trainer branch, the design is wrong.",
    ]),
    ("evaluation and fitting it together", [
        "Add crossword.py and maze.py as source files, not trainer branches.",
        "Their records compile to the same META/FILL/HIDE/WANT/CAND/CHECK/SAVE recipes.",
        "The manifest controls their weights, budgets, loss scales, and closed-vs-aided variants.",
        "Required reports: leakage, baselines, budget curve, held-out size curve, final CHECK, failure classes, and sample traces.",
        "For crosswords, report cell_acc, word_acc, full_grid_acc, theme success, and repeated-clue contamination.",
        "For mazes, report first_action_acc, valid_path, optimal_path, path_len_ratio, collision rate, and out-of-size generalization.",
        "Successful traces become gold; failed traces become repair data only after a later pass.",
        "The final proof is simple: with tools disabled and answers absent, more internal steps improve held-out CHECK scores.",
    ]),
]

out = []
for title, body in sections:
    out.append(f"{n}. {title}")
    out.extend(body)
    n += 1

bad = [(i + 1, len(line), line) for i, line in enumerate(out) if len(line) > 222]
nonascii = [(i + 1, c) for i, line in enumerate(out) for c in line if ord(c) > 127]

if len(out) > 100:
    raise SystemExit(f"too many appended lines: {len(out)}")
if bad:
    raise SystemExit("long appended line: " + repr(bad[0]))
if nonascii:
    raise SystemExit("non-ascii appended char: " + repr(nonascii[0]))

with path.open("a") as f:
    if text and not text.endswith("\n"):
        f.write("\n")
    f.write("\n".join(out))
    f.write("\n")

print(f"appended_lines={len(out)} start_section={start_n} end_section={n - 1}")
print(f"max_appended_line={max(len(line) for line in out)}")
PY