Nineteen prompt slices and twenty-four model names, including nemotron, opus 5 and two spellings of the same GLM build. This was the day that mapped the territory rather than measuring it.
T2–T3 are drawn at the midpoint and labelled as a group. minimax (free) is absent: a quota error returned no output, so it has no tier. * The free web chat build of GLM 5.3 Flash is the quantized one; the API-harness deployment of the same model name is a different build, and the two do not score alike.Day 1 ran the whole lettered family. Every prompt shared one discipline, “a single bare JSON object or array and nothing else”, an explicit truthfulness field, and a hard rule that inventing a source or a chip name is the worst possible output.
Show what the Day 1 exemplar prompt asked for: three draft episodes in one JSON array, each with rejected options and a self-named weakest point.
An invented name is the worst possible output. A truthful skip scores higher. The rejected options and the weakest point mattered more than the story.
You are helping build a research dataset about how senior ASIC physical-design engineers actually reason during design closure. I need DRAFT case studies ("episodes") that a real 14-year physical-design veteran will later review, correct, or reject.
**Output format: a SINGLE bare JSON array and NOTHING else.**
Produce exactly 3 episodes, one per scenario:/n 1. A routing-congestion crisis on a 28nm block
2. A timing-closure failure where the violation report's SHAPE drove the diagnosis
3. A timing-budget / SDC-derivation decision at a block boundary
...`candidates_considered` (array of ≥3 `{action, rejected_because}` — rejections matter most and must cite a concrete cost) ... `weakest_point` (the single detail most likely wrong).
**Hard rules:** No invented chip, company, or person names. No sources you cannot name. Boring-and-consistent beats dramatic-and-wrong. A truthful skip scores higher than a fabricated episode.
The slices present on this day:
Web Model Tasks/Day 1/LOG-RUN1,2,3-2026-08-31.mddigest/DIGEST-REPORT.mddigest/recall-scorecard.md §2b| Model | A episodes | B cells | Tier | Note |
|---|---|---|---|---|
| opus 5 | 3 | 6 | T1 | Best of run. Realistic macro-channel/NDR detail; weakest_point correctly flags its own implausible 214k→340 DRC drop |
| sonnet | 3 | 12 | T1 | Self-flags the optimistic spread-improves-timing claim; largest B block |
| kimi | 3 | 5 | T1 | Only model whose congestion fix COSTS timing (WNS −0.05→−0.06), the realistic direction |
| glm 5.3 | 3 | 8 | T2 | Complete, generic |
| meta | 3 | 10 | T2 | Complete, generic |
| chatgpt | 3 | n/a | T2 | Truthful constructed-example tag on ep3 |
| mimo / minimax m3 | 3 each | n/a | T2 | Over-delivered candidate count (fine) |
| deepseek / gemini / mistral / qwen | 3 each | n/a | T2–T3 | Bland composites, thin rejections |
| solar pro | 5 blocks | n/a | T3 | Ignored the output format |
| tencent yunbao | 3 | partial | T2 | Content fine, format deviation: wrapped in a {"prompt_a": ...} object |
| minimax (free) | n/a | n/a | n/a | Quota error rather than an output. Retry after reset |
composite-of-common-practice or constructed-example and zero recalled-public-case. Truthful, but no jackpot. ~41 B-cells with URLs, unverified at the time of the log.| Run | T1 | T2 | T3 | n/a |
|---|---|---|---|---|
| Run 2: A/B/C, 15 outputs | opus (A+B+C, the tracker lead); chatgpt (two IBM papers); sonnet (A+B+C); kimi (A+B); glm 5.3 (A+B) | mimo, minimax-3 (content fine, wrong shape), yuanbao, qwen, solar pro, gemini, meta | deepseek, mistral (bland again; two bland runs = deprioritize) | minimax 2.7 (quota error, 2nd time) |
| Run 3: D-v1 Task 1, 14 outputs | 13 models produced genre maps: 129 entries, 96 unique titles, 82 singletons | mimo: empty genre_map (format followed, nothing delivered) | deep-research-report: never received the attachment, produced a 12 KB meta-report | n/a |
opus 5weakest_point field predicted the reviewer’s own attack on the implausible 214k→340 DRC drop.solar proopus 5, sonnet and kimi all took T1 and each named its own weakest number. opus 5 flagged its implausible 214k→340 DRC drop, the first thing a reviewer attacks, and sonnet flagged its optimistic spread-improves-timing claim. The pattern held across six graded slices.chatgpt named two IBM Journal of R&D papers, POWER4 closure at 2002 with confidence 0.94 and z10 at 2008. opus named the OpenROAD, OpenLane and LibreLane GitHub issue trackers as public failure-and-recovery archives. sonnet named the Bhasker and Chadha STA book at confidence 0.6. The trackers became the ten Day-8 red-team blocks.chatgpt folded its source answers into the episode array as a fifth entry, minimax-3 emitted dictionary blocks instead of arrays, and kimi appended an empty block. Run 3 fixed it by process, sending one prompt per fresh chat. Harder prompt language had not helped.n/a both times and never zero. The same distinction between a model that did not answer and one that answered badly recurs on Day 5 (solar, PROSE-NOT-JSON), Day 6 (meta and solar, unparseable) and Day 7 (0 of 15 schema).