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feat(support): battle-tested benchmark prompt set (GSM8K/MATH) + thinking-lift analysis
Replace the 6 trivial prompts with 12 problems adapted from public
battle-tested benchmarks, machine-verified answers:
- Easy (3): GSM8K-style 2-step word problems — pass in both modes
- Medium (4): GSM8K multi-step + MATH L3-style (heads/legs) —
non-thinking misses some
- Hard (5): MATH L4-5 style — inclusion-exclusion counting, stars and
bars, coin probability, exact-value log equation, recurrence
Report gains:
- 'Test set & sources' section (GSM8K openai/grade-school-math, MATH
hendrycks/math attribution + tier expectations)
- auto Notes: thinking lift (fails at off, passes on) and persistent
failures (fail at all levels = capability gap, not level effect)
- intermediate /tmp saves now include fidelity + wire_params
First results with the new set (12 problems, 19/19 wire fidelity):
- Qwen3.8-27B: off 10/12 -> minimal/low/medium 12/12 (thinking rescues
math_log_eq + math_coin_prob); high 11/12 (sampling variance)
- Qwen3.6-35B-A3B-MTP: 11/12 at every level; math_log_eq fails at all
levels (capability gap) — thinking gives no lift on this model set
"A gardener plants 4 rows of 6 tomato plants, then adds 3 more rows of 6 each. How many total?"),
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("logic_easy", "easy",
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"All dogs can swim. Max is a dog. Can Max swim? Answer yes or no."),
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("math_med", "medium",
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"Solve: number * 3 + 5 = 320 - (number * 4). What is x? Show the equation."),
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("logic_med", "medium",
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"Alice Bob Charlie each have number 1 2 or 3. Alice not largest/smallest. Bob > Charlie. Charlie != 3."),
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("math_hard", "hard",
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"A farmer has sheep. If he sells 140, half as many left as if he didn't sell any. Then buys 2 more. How many?"),
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("causal_med", "medium",
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"It is 95F outside. You leave a scoop of vanilla ice cream on the sidewalk for 30 minutes. What happens and why?"),
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# ── Easy (GSM8K-style, 2 steps — expect pass in both modes) ──
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("gsm_clips", "easy",
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"Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether?"),
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("gsm_babysit", "easy",
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"Weng earns $12 an hour for babysitting. Yesterday she just worked 50 minutes. How much did she earn?"),
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("gsm_letter", "easy",
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"James writes a 3-page letter to 2 different friends twice a week. How many pages does he write a year?"),
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# ── Medium (GSM8K multi-step / MATH L3 — expect non-thinking to miss some) ──
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("gsm_wallet", "medium",
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"Betty is saving money for a new wallet which costs $100. Betty has only half of the money she needs. Her parents decided to give her $15 for that purpose, and her grandparents twice as much. How much more money does Betty need?"),
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("gsm_book", "medium",
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"Julie is reading a 120-page book. Yesterday she was able to read 12 pages and today she read twice as many pages as yesterday. If she wants to read half of the remaining pages tomorrow, how many pages should she read?"),
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("gsm_flowers", "medium",
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"Mark planted 10 yellow flowers. He planted 80% more purple flowers than yellow flowers. He also planted 25% as many green flowers as the total of yellow and purple flowers combined. How many flowers did Mark plant in total?"),
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("math_heads_legs", "medium",
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"A farmer has chickens and rabbits in a field. There are 40 heads and 100 legs in total. How many rabbits does the farmer have?"),
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# ── Hard (MATH L4-5 style — expect only thinking to pass) ──
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("math_div_or", "hard",
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"How many positive integers less than 1000 are divisible by 7 or 11?"),
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("math_balls_boxes", "hard",
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"In how many ways can 3 indistinguishable balls be placed into 4 distinguishable boxes?"),
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("math_coin_prob", "hard",
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"A fair coin is flipped 5 times. What is the probability of getting exactly 3 heads? Express your answer as a fraction in lowest terms."),
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("math_log_eq", "hard",
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"Solve for x: log base 2 of x plus log base 2 of (x minus 2) equals 4. Give the exact value of x."),
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("math_sequence", "hard",
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"A sequence starts 2, 3, 5, 9, 17 and each term after the first is one less than twice the previous term. What is the sixth term of the sequence?"),
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}
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# Expected answer keywords — used for automated correctness checking.
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# Numeric keywords are matched with digit-boundary guards (no substring
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# false-positives like "10" inside "100"). Sources: GSM8K (openai/grade-school-math)
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# and MATH (hendrycks/math) style problems, adapted for single-shot scoring.
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