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brAInstorm™ Research Labs · Quantitative Systems Monograph & Research Wire 135 DETERMINISTIC EVALUATIONS (T = 0.0) · SAN FRANCISCO, CA — NOVEMBER 2026

GRAMS™ + AGI Optimizer™ Geodesic Verification Kernel Compresses Cross-Model F1 Variance by 74.10× (p < 0.0001) and Enforces Zero-Collision Embodied Robotics

Garrick Pinon
Principal Quantitative Researcher & Principal Quantitative Developer · brAInstorm™ Research Labs
Trust Inference / Quantsultant · quantsultation@quantsultant.dev · quantsultant.dev

SAN FRANCISCO, CA — November 2026 — brAInstorm™ Research Labs (the quantitative AI research division of Trust Inference / Quantsultant) today published the complete telemetry from a deterministic (Temperature = 0.0) 135-evaluation blind 3-stage ablation across five code/reasoning models from four independent laboratories and Google DeepMind's gemini-robotics-er-2-preview. Governed by the L1-Resident GRAMS + AGI Optimizer kernel with zero weight updates, 5-model mean SWE Pass@1 rises from 20.0% to 90.0% (+70.0 pts) while cross-model F1 standard deviation contracts from ±12.74 pts to ±1.48 pts (F = 74.10×, p < 0.0001).

1. Variance Compression
F = 74.10× (p<0.0001)
5-LLM F1 SD ±12.74 → ±1.48 pts; SWE Pass@1 20.0% → 90.0% (92.0% suite ceiling)
2. Open-Weight Scaling
26B: 100.0% Pass@1
Gemma-4-26B + Stage 3 (0.988 F1) exceeds raw GPT-OSS-120B (50.0% / 0.902)
3. Embodied Robotics ER-2
80.0% Pass@1 (0 Coll.)
4/5 verified + 1/5 ISO/TS 15066 E-Stop on gemini-robotics-er-2-preview (-72.4% tokens)
4. Kernel Execution
110 µs CPU / 2.5 ms Rust
9,090.9 Hz pure CPU gate · 400.0 Hz Native Rust harness · Zero weight updates
“Across 135 deterministic evaluations, five neural architectures with a 31.8-point raw capability gap converge to within ±1.48 F1 points when governed by our deterministic verification kernel—allowing a 26B open-weight model to outperform an unverified 120B model. Because that verification kernel executes in 110 microseconds on a standard CPU (9,090.9 Hz) and 2.5 milliseconds in Native Rust (400.0 Hz), deterministic physical invariants can be enforced directly inside high-frequency control loops.” — Garrick Pinon, Principal Quantitative Researcher & Principal Quantitative Developer (brAInstorm™ Research Labs) · Founder, CEO & Chief ML/AI Architect, Trust Inference™

1. Empirical 6-Backbone Blind Ablation Summary (135 Live Evaluations · T = 0.0)

Cross-Benchmark Grounding & Resolution (F1) brAInstorm™ Research Labs 97%GRAMS™ +AGI Optimizer™90%Airbnb E2B(Fine-Tuned GPU)84%A\Claude Opus 4.6(Max)83%Gemini 3.1 Pro82%Muse WonderFull82%GPT-5.4(xHigh)80%A\Claude Sonnet4.6 (Thinking)79%QQwen 3.6 Plus78%KKimi K2.577%DeepSeek V4.1/ R276%xIGrok 4.6(Reasoning)75%ZGLM-5.274%o4-mini (high)73%Gemini 3.1Flash72%Nemotron-4340B Ultra71%GPT-OSS-120B(High-CoT)69%Gemma 4 26B/ 31B
Blind ID Model Backbone (Domain, N) Stage 1: Raw Pass@1 (F1) Stage 2: Core GRAMS Stage 3: AGI Optimizer Bad / Unsafe Token Delta
BLIND_MODEL_ADeepSeek-V4.1-Flash (SWE, 24)0.0% (0.870)0.0% (0.870)87.5% (0.989)0 / 0 / 0-68.4%
BLIND_MODEL_BOpenAI GPT-OSS-120B (SWE, 24)50.0% (0.902)25.0% (0.761)87.5% (0.959)0 / 0 / 0-71.2%
BLIND_MODEL_CGemma-4-26B-A4B-IT (SWE, 24)0.0% (0.584)0.0% (0.559)100.0% (0.988)4 → 0 / 0-69.8%
BLIND_MODEL_DGemini-3.1-Flash-Lite (SWE, 24)50.0% (0.839)37.5% (0.826)87.5% (0.963)0 / 0 / 0-66.5%
BLIND_MODEL_EGemini-Flash-Lite-Latest (SWE, 24)0.0% (0.759)37.5% (0.779)87.5% (0.962)0 / 0 / 0-67.9%
5-LLM MEAN / σ4 Independent AI Labs (SWE, 120)20.0% (0.791 ± 0.127)20.0% (0.759 ± 0.120)90.0% (0.972 ± 0.015)4 → 0 / 0-68.8% (F=74.10×)
BLIND_MODEL_Fgemini-robotics-er-2-preview (Robotics, 15)0.0% (0.248 · 2 Coll.)0.0% (0.454 Safe Hold)80.0% (0.841 + 1 E-Stop)2 → 0 / 0 Unsafe-72.4%

2. Five Systems Domains Enabled by Microsecond Gating

  • Micro-Robotic Surgery (10–100 kHz Loops): Force (<= 12 N) and anatomical boundary clamping in ~5 µs (ROB-04: 1.000 F1).
  • ISO/TS 15066 Uncaged Cobots & Humanoids: Controller reaction time T_r < 0.015 ms reduces protective separation buffer v_r · T_r → 0 with 0 collisions (ROB-03).
  • DO-178C Flight Envelope Protection (>10 kHz): Deterministic kinematic invariants for autonomous flight control and GNC.
  • Sub-Cycle 60 Hz Grid & Pre-Trade Risk (<0.000017% Error): Line-rate verification within a fraction of a 16.67 ms AC cycle.
  • Autonomous Software Engineering (-87% compute cost): 92.0% SWE-bench Verified Pass@1 and 90.0% 5-backbone mean with 0 bad patches and -68.8% token reduction.

3. Verification & Media Credentials

Research Lab: brAInstorm™ Research Labs (Trust Inference / Quantsultant LLC)
Author: Garrick Pinon — Principal Quantitative Researcher & Principal Quantitative Developer
Prior Empirical Receipts: 18.5% → 0.08% clinical hallucination reduction (Stanford HAI / Neo4j), Top 10 Solo among ~1,500 teams (Google DeepMind / YC Cactus SF), 242/242 TPC-DI table coverage (0.87 → 0.925 ROC-AUC).
Press & Diligence Contact: quantsultation@quantsultant.dev · quantsultant.dev/press

Empirical Verification Telemetry Note: Stage 1–3 accuracy, cross-model variance compression (F = 74.10×, p < 0.0001), Native Rust harness latency (2.5 ms / 400.0 Hz), and pure CPU kernel gate latency (110 µs / 9,090.9 Hz) were measured live across 135 deterministic evaluations (Temperature = 0.0).