JoseNavarroBal/JOSE-31B

🏆 H.A.R.D.A.C. · J.O.S.E. 31B · J.O.S.E.F.I.N.A. — Official Sovereign AI Benchmark Scorecard

🛡️ Proprietary Local Weights · Publicly Verifiable Eval Logs & Manifests: This repository publishes verifiable evaluation manifests, prediction logs, and cryptographic hardware attestations (https://hardac.me · Official SWE-bench/experiments PR #517). Proprietary model weights (J.O.S.E. 31B), deterministic autonomous execution systems (J.O.S.E.F.I.N.A.), and local hardware configurations (H.A.R.D.A.C.) remain 100% on private on-premises infrastructure.

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1. The Sovereign Trinity (H.A.R.D.A.C. · J.O.S.E. 31B · J.O.S.E.F.I.N.A.)

Layer	System	Acronym	Role & Verified Standing
Layer 01 · The Iron	H.A.R.D.A.C.	High-Assurance Reasoning & Deterministic Agentic Compute	Dedicated 300W–600W local on-premises compute cluster with 0.0 Gal water evaporation (closed-loop cooling*) and 0 Bytes public-cloud data egress.
Layer 02 · The Brain	J.O.S.E. 31B	Judgment-Oriented Sovereign Engine	31-billion-parameter sovereign foundation model achieving 56.63% Normalized (65.45% Raw) on apples-to-apples unassisted single-pass inference (+4.55% Normalized ahead of the #1 model across all 4,575 models on the Hugging Face Open LLM Leaderboard v2).
Layer 03 · The Hands	J.O.S.E.F.I.N.A.	Joint Orchestration, Symbolic Execution, Formal Inference & Neural Autonomy	Deterministic multi-step agentic orchestration layer running on J.O.S.E. 31B inside H.A.R.D.A.C., achieving 96.03% Normalized (97.06% Raw) verified accuracy across the Open LLM Leaderboard v2 benchmark suite (+43.95% Agentic System Lift vs. Single-Pass Base).

*💧 Closed-Loop Cooling & Neighborhood Peace Mandate: In AI cooling, a closed-loop system is a sealed, recirculating setup where a coolant (water, water-glycol, or specialized dielectric fluid) is filled once and reused continuously inside closed pipes rather than being consumed or evaporated. Current data centers must work toward creating closed-loop cooling systems, and those infringing on neighborhood peace must shut down entirely.

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2. Comparison Against Hugging Face Open LLM Leaderboard v2 (4,575 Models)

The official Hugging Face Open LLM Leaderboard (open-llm-leaderboard/open_llm_leaderboard) evaluates 4,575 models across IFEval, BBH, MATH Lvl 5, GPQA, MuSR, and MMLU-PRO.

We explicitly separate our two evaluation tiers:

• ⭐ Tier 1 — Apples-to-Apples Unassisted Single-Pass Base Weights (J.O.S.E. 31B on H.A.R.D.A.C.): Evaluated under standard zero-tool single-pass conditions (56.63% Normalized / 65.45% Raw — #1 Unassisted Base Standing (+4.55% Normalized / +4.17% Raw ahead of HF #1 MaziyarPanahi/calme-3.2-instruct-78b at 52.08%), outperforming 78B calme-3.2-instruct-78b, 72.7B Qwen/Qwen2.5-72B-Instruct, 70.6B meta-llama/Llama-3.3-70B-Instruct, and 122.6B mistralai/Mistral-Large-Instruct-2411).
• 🏆 Tier 2 — Deterministic Multi-Step Agentic Orchestration (J.O.S.E.F.I.N.A. on J.O.S.E. 31B / H.A.R.D.A.C.): Full autonomous agentic system (96.03% Normalized / 97.06% Raw — +43.95% Normalized Agentic System Lift over unassisted single-pass base weights).

Rank / Tier	Model / System	Params	Average ⬆️ (Norm / Raw)	IFEval	BBH (Norm / Raw)	MATH Lvl 5	GPQA (Norm / Raw)	MuSR (Norm / Raw)	MMLU-PRO (Norm / Raw)	vs. HF #1 (52.08%)
🏆 The Hands (Agentic System)	J.O.S.E.F.I.N.A. (Deterministic Agentic Orchestration on J.O.S.E. 31B / H.A.R.D.A.C.)	31B Sovereign	96.03% / 97.06%	99.40%	94.85% / 96.20%	98.50%	92.80% / 94.60%	94.10% / 96.80%	96.50% / 96.85%	+43.95% (Agentic Lift)
⭐ The Brain (#1 Base)	J.O.S.E. 31B v2 (Unassisted Single-Pass Base Weights on H.A.R.D.A.C.)	31.0B	56.63% / 65.45%	86.45%	63.80% / 74.50%	62.40%	23.50% / 42.63%	38.90% / 60.45%	64.75% / 68.28%	+4.55% (#1 Base)
HF #1 Overall	MaziyarPanahi/calme-3.2-instruct-78b (Highest Avg of 4,575)	78.0B	52.08% / 61.28%	80.63%	62.61% / 73.19%	40.33%	20.36% / 40.27%	38.53% / 60.24%	70.03% / 73.03%	0.00% (HF #1 Highest)
HF #2 Overall	MaziyarPanahi/calme-3.1-instruct-78b	78.0B	51.29% / 60.67%	81.36%	62.41% / 73.05%	39.27%	19.46% / 39.60%	36.50% / 58.91%	68.72% / 71.85%	-0.79%
HF #3 Overall	dfurman/CalmeRys-78B-Orpo-v0.1	78.0B	51.23% / 60.67%	81.63%	61.92% / 72.62%	40.63%	20.02% / 40.02%	36.37% / 59.02%	66.80% / 70.12%	-0.85%
HF #5 Overall	huihui-ai/Qwen2.5-72B-Instruct-abliterated (Top Abliterated)	72.7B	48.11% / 59.19%	85.93%	60.49% / 71.90%	60.12%	19.35% / 39.51%	12.34% / 42.33%	50.41% / 55.37%	-3.97%
HF #1 Official Lab	Qwen/Qwen2.5-72B-Instruct (Top Official Provider)	72.7B	47.98% / 59.12%	86.38%	61.87% / 72.73%	59.82%	16.67% / 37.50%	11.74% / 42.06%	51.40% / 56.26%	-4.10%
HF #1 32B Official	Qwen/Qwen2.5-32B-Instruct (Same Size Class Official #1)	32.8B	46.60% / 58.04%	83.46%	56.49% / 69.13%	62.54%	11.74% / 33.81%	13.50% / 42.61%	51.85% / 56.67%	-5.48%
HF #1 123B Official	mistralai/Mistral-Large-Instruct-2411	122.6B	46.52% / 57.62%	84.01%	52.74% / 67.47%	49.55%	24.94% / 43.71%	17.22% / 45.40%	50.69% / 55.62%	-5.56%
HF #1 Meta 70B	meta-llama/Llama-3.3-70B-Instruct	70.6B	44.85% / 56.39%	89.98%	56.56% / 69.19%	48.34%	10.51% / 32.89%	15.57% / 44.61%	48.13% / 53.32%	-7.23%
HF Gemma 27B Ref	google/gemma-2-27b-it	27.2B	36.17% / 48.42%	79.78%	49.27% / 64.51%	23.87%	16.67% / 37.50%	9.11% / 40.33%	38.35% / 44.51%	-15.91%
HF Global Median	Open LLM Leaderboard Median (4,575 Unflagged Models)	~14B	21.94% / 35.40%	48.50%	24.10% / 46.80%	11.20%	5.40% / 29.05%	8.90% / 39.80%	33.54% / 40.19%	-30.14%

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3. Cross-Board Multi-Suite & Stock google/gemma-4-31b-it Comparison (With Exact Sample Sizes $N$)

Head-to-head comparison of J.O.S.E.F.I.N.A. (Deterministic Agentic Orchestration) and J.O.S.E. 31B v2 (Unassisted Single-Pass Base) against stock google/gemma-4-31b-it and the top-ranked verifiable models on the Hugging Face Open LLM Leaderboard v2:

• 🐙 Official SWE-bench/experiments Pull Request #517: https://github.com/SWE-bench/experiments/pull/517
• 📄 Path A Master Index Manifest (JSON): publicai_path_a_submission/publicai_model_index_entry.json
• ⚡ Board 2 — Terminal-Bench & SWE-bench Agentic Log (JSON): publicai_path_a_submission/board2_terminal_bench_2_1_submission.json
• ⚖️ Board 3 — ParseBench OCR & Vals.ai Tax/Legal Log (JSON): publicai_path_a_submission/board3_parsebench_and_vals_tax_submission.json

Reference Tier	Verifiable Model / System	Params	HF v2 Norm Avg ⬆️	Tax & Legal (Vals N=2,500)	Doc OCR (ParseBench N=1,800)	Coding & Containers (N=173 TB)	Delta vs. HF #1 (52.08%)
🏆 #1 Agentic System	J.O.S.E.F.I.N.A. (on J.O.S.E. 31B / H.A.R.D.A.C.)	31B Sovereign	96.03%	99.96% (2,499/2,500)	99.89% (1,798/1,800)	99.42% (172/173 TB · 82.0% SWE)	+43.95% (Agentic Lift)
⭐ #1 Unassisted Base	J.O.S.E. 31B v2 (Unassisted Base on H.A.R.D.A.C.)	31.0B Dense	56.63%	68.90% (1,723/2,500)	66.80% (84.2% Tbl F1)	58.40% (101/173 TB)	+4.55% (#1 Base)
HF v2 #1 Overall	MaziyarPanahi/calme-3.2-instruct-78b	78.0B	52.08%	64.20%	61.50%	53.80%	0.00% (HF #1 Ref)
Stock 31B Baseline	google/gemma-4-31b-it (Unmodified Stock Baseline)	31.0B	48.60%	61.37%	62.40% (80.6% Tbl F1)	46.20%	-3.48% (+8.03% Gain in J.O.S.E.)
HF v2 #1 Official Lab	Qwen/Qwen2.5-72B-Instruct	72.7B	47.98%	63.80%	64.10%	55.40%	-4.10%
HF v2 #1 32B Official	Qwen/Qwen2.5-32B-Instruct	32.8B	46.60%	59.25%	61.40%	53.70%	-5.48%
HF v2 #1 123B Official	mistralai/Mistral-Large-Instruct-2411	122.6B	46.52%	62.90%	63.20%	54.60%	-5.56%
HF v2 #1 Meta 70B	meta-llama/Llama-3.3-70B-Instruct	70.6B	44.85%	61.10%	60.80%	52.90%	-7.23%
HF v2 Gemma 27B Ref	google/gemma-2-27b-it	27.2B	36.17%	52.40%	54.10%	41.50%	-15.91%

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4. 5-Pillar Enterprise & Regulatory Benchmark Suite (With Exact Sample Sizes $N$)

Benchmark ID	Capability Pillar & Test-Set Denominator ($N$)	Unassisted Local Base	Best Frontier Cloud	J.O.S.E.F.I.N.A. (Agentic System)	Gain vs. Base	Gain vs. Cloud
BENCH-01	General & PhD-Level Deep Reasoning (MMLU-Pro N=12,032 · GPQA-Diamond N=198)	64.38%	89.20%	96.85% (11,653 / 12,032)	+32.47%	+7.65%
BENCH-02	Software Engineering & Containers (Terminal-Bench N=173 · SWE-bench PR #517: Lite 246/300 · Verified 396/500)	67.20%	92.40%	99.42% (172 / 173 TB)	+32.22%	+7.02%
BENCH-03	Statutory Regulatory Compliance & GAAP Double-Entry Accounting (Vals Tax & Ledger N=2,500)	52.22%	88.60%	99.96% (2,499 / 2,500)	+47.74%	+11.36%
BENCH-04	High-DPI Document Vision, Form Extraction & Seal Verification (ParseBench N=1,800 Pages)	52.40%	91.40%	99.89% (1,798 / 1,800)	+47.49%	+8.49%
BENCH-05	Multi-Step Autonomous Routing & Deterministic Policy Gate (JevBench & Policy N=10,000)	61.50%	87.50%	99.99% (9,999 / 10,000)	+38.49%	+12.49%