| # Mach-1-Ternary-Additive-35B — benchmark board |
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| Payload: integer L1-ball trellis expert codes + per-wavefront gamma scales + |
| continuous fp16 su/sv side-streams; 64-level integer-lattice spine; int5-g64 |
| head; int4 embed — every weight matmul is add/subtract-only. Ship gate: |
| decode.py-primitive reconstruction == served checkpoint (bf16 rounding, |
| layers 0/20/39). |
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| Protocol: same-harness EvalScope 1.9.1 + vLLM, PrismML App. B (thinking mode, |
| temp 1.0, top_p 0.95, top_k 20, PrismML token tiers, AIME mean-of-8, IFEval |
| prompt-strict, IFBench prompt-loose). Retention = 100 x score / BF16 teacher |
| (Qwen3.6-35B-A3B), same harness. tau2-bench = fixed external user-simulator |
| (Qwen3.6-35B BF16, greedy), single pass, identical for every model. |
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| ## Flagship payload (12/12) |
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| | benchmark | score | teacher | retention | |
| |---|---|---|---| |
| | AIME25 @8 | 87.50 | 88.33 | **99.1%** | |
| | AIME26 @8 | 89.58 | 90.00 | **99.5%** | |
| | MATH-500 | 98.00 | 98.60 | **99.4%** | |
| | GSM8K | 94.69 | 96.21 | 98.4% | |
| | MBPP+ | 94.44 | 96.03 | 98.3% | |
| | HumanEval+ | 92.68 | 95.12 | 97.4% | |
| | MMLU-Redux | 89.18 | 92.68 | 96.2% | |
| | IFEval | 83.75 (n=5) | 89.05 | 94.0% | |
| | MuSR | 61.77 | 66.66 | 92.7% | |
| | BFCL-v3 | 68.97 | 74.98 | 92.0% | |
| | tau2-bench | 71.58 | 79.51 | 90.0% | |
| | IFBench | 54.08 | 64.97 | 83.2% | |
| | **mean retention** | | | **95.0%** | |
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| ## Read-quality notes |
| - Multi-read cells quote the mean over all reads with n; single reads are n=1. |
| - IFEval strict single-read spread measured ~2-3 pts; tau2 complete-read |
| spread up to ~6 on some artifacts — sub-point deltas are ties. |
| - Expert payload 6.207 GB. |
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