RoamingPigs AI — Independent Systems Physics & Empirical AI Architecture

BAY 01

TRCS Acoustic Channel Analyzer

ONLINE // 8 kHz VAD GATED

Characterizing vocoder parameter distortion, fricative collapse, and autoregressive hallucination loops across operational narrowband channels.

Channel Profile:

Specification8 kHz, 8-bit log PCM (64 kbps)

Word Error Rate18.2% ± 1.1% WER

Fricative Attenuation-14.2 dB (/s/ sibilance collapse)

SQUELCH NOISE GATE:

ACTIVE / BLOCKED

ITU-T G.712 Filter Specifications & Tactical Sample Counts (n=1,050)

Channel Parameter	Standard Specification	Measured Impact	Sample Fixtures	Verification Digest
G.712 Bandpass	300 Hz – 3400 Hz (4th-order Butterworth)	Collapse of /s/ causing "Sector" to register as "Hector"	n = 1,050	SHA-256 VERIFIED
AMR-NB MR122	3GPP TS 26.071 ACELP (12.2 kbps)	Smearing of nasal vowel anti-formants under 8 kHz	n = 1,050	SHA-256 VERIFIED
GSM 06.10 FR	ETSI GSM 06.10 RPE-LTP (13.0 kbps)	Regular pulse excitation distortion on plosive bursts	n = 1,050	SHA-256 VERIFIED
Squelch Tail	FM Discriminator Unmuting (50–150 ms)	Ungrounded hallucinations (24 to 160 WPM) without energy gate	n = 1,050	SHA-256 VERIFIED

Hugging Face Hermetic Replication

from datasets import load_dataset

# Load 1,050 tactical radio fixtures with cryptographic SHA-256 manifest

ds = load_dataset("RoamingPigs/trcs-tactical-audio")

print(f"Loaded {len(ds['test'])} clips across 30 narrowband conditions")

BAY 02

VEC-SCR Verifier Covariance Scope

CALIBRATED // N=504 TASKS

2D decision error scope characterizing positive verifier error dependence (ρ = 0.42 ± 0.04) and asymmetric risk penalty dynamics in test-time compute.

Decision Risk Model:

Error Correlationρ = 0.42 ± 0.04

Penalty RatioC_FA >> C_FR (10:1 Ratio)

Abstention Rate95.7% Bayes Risk Reduction (τ = 0.70)

Log-Odds StatusCorrected: 59.4% Posterior Log-Odds

Sequential Stopping Horizon (Optimal K* = 3):

Stage 1 (τ₁*)

Stage 2 (τ₂*)

Stage 3 (τ₃* → Stop)

Stage 4 (—)

Neyman-Pearson Formulations & Dynamic Programming Calibration

Theoretical Formulation	Analytical Expression	Empirical Calibration Result
Theorem 1: Total Error Covariance	σ_AB = (1−π)σ_FA + πσ_FR + π(1−π)(α_A−β_A)(α_B−β_B)	Σ_AB = 0.42 ± 0.04 over 504 calibration tasks
Lemma 1: Coupling Saturation	κ_FA = σ_FA / √(α_A(1−α_A)α_B(1−α_B))	89.53% saturation of Fréchet-Hoeffding upper bound
Theorem 2: Bayesian Log-Odds Distortion	ΔI = ln(1 + σ_FA / (α_A α_B))	+2.675 nats uncalibrated distortion under independent voting
Theorem 3: Stopping Monotonicity	τ₁* > τ₂* > … > τ_K* under C_FA >> C_FR	Optimal stopping terminates at stage K* = 3

BAY 03

Echo Swarm PRG Gate Array

10 CHANNELS SYNCED

Concurrency logic analyzer monitoring 10 autonomous agents across 12,400 advisory file holds, verifying zero lock collisions and publication gate criteria.

CH 01 // AGT-01

HOLD LEASED (0.00ms)

CH 02 // AGT-02

HEARTBEAT OK

CH 03 // AGT-03

VERIFYING CODEC

CH 04 // AGT-04

TASK DISPATCH

CH 05 // AGT-05

ATOMIC RELEASE

CH 06 // AGT-06

HOLD LEASED

CH 07 // AGT-07

HEARTBEAT OK

CH 08 // AGT-08

HOLD LEASED

CH 09 // AGT-09

SYNC BARRIER

CH 10 // AGT-10

GATE VERIFIED

10

Active Agents

12,400

Advisory File Holds

0.00%

Lock Collisions

84.7% ± 1.6%

Pass^5 Consistency

Publication Readiness Gate Array (PRG 01–06 Verification Checklist)

Gate Identifier	Standard Requirement	Telemetry Verification Method	Status
PRG-GATE-01	Cryptographic Provenance	SHA-256 manifests across 1,050 audio fixtures and weights	PASSED (0 missing)
PRG-GATE-02	Dependency Isolation	Hermetic clean-room execution in standard Python without host paths	PASSED (Hermetic)
PRG-GATE-03	Negative Controls	Ground truth WER=0.0; Silence deletion WER=1.0; Squelch test	PASSED (Invariants hold)
PRG-GATE-04	Schema Validity	Strict JSON schema validation; zero undefined or NaN variables	PASSED (Valid)
PRG-GATE-05	Statistical Rigor	95% confidence intervals via 1,000 stratified bootstrap resamples	PASSED (Powered N)
PRG-GATE-06	Public Surface Boundaries	Bio-canon compliance: zero vendor model names in public copy	PASSED (Clean)

BAY 04

Silicon Thermodynamics & Memory Roofline

HWMON5 TELEMETRY ACTIVE

Calibrated hardware telemetry monitoring wall power draw, thermodynamic prefill-decode asymmetry, and sustained 256-bit APU memory bus saturation.

WALL POWER: 108 W

THERMODYNAMIC EFFICIENCY

WALL JOULES / TOKEN

1.66 J

256-bit Unified APU Memory Bus87.0%

222.7 GB/s sustained256.0 GB/s theoretical

Physical Power Telemetry & Prefill-Decode Thermodynamic Breakdown

Operating Phase	Wall Power Draw	Silicon Package (PPT)	Joules / Token	Thermodynamic Ratio
System Idle (Baseline)	15.0 W (AC Wall)	4.2 W (SoC hwmon5)	—	Baseline tare
Prefill Phase (Compute-Bound)	42.0 W (AC Wall)	28.5 W (SoC hwmon5)	0.1305 J / token	1.0× (Reference)
Decode Phase (Memory-Bound)	108.4 W (AC Wall)	78.2 W (SoC hwmon5)	2.8220 J / token	21.6× Prefill Asymmetry
Sustained APU Bus Peak	108.4 W (AC Wall)	80.0 W (Full Package)	1.66 J / token (30B MoE tier)	87.0% Saturation Efficiency

BAY 05

Master Calibration Bay (Hardware Sizing Simulator)

REACTIVE TELEMETRY ENGINE

Interactive silicon calibration engine. Adjust model architecture, quantization format, and context length to dynamically drive Bay 04 wattmeter deflection and full TCO economics.

Model Parameter ClassARCHITECTURE

Quantization SchemeBITS / WEIGHT

Context Window Length16,384 tokens

Hardware Target ArchitectureSYSTEM CLASS

Grid Electricity TariffUSD / KWH

Total RAM Allocation
-- GB

KV-Cache Allocation

-- GB

Sustained Bandwidth

-- GB/s

Decode Speed

-- tok/s

Wall Power Draw

-- W

Energy per Token

-- J/tok

Electricity Cost / 1M Tokens

--

Amortized 3-Yr TCO / 1M Tokens
--

Comparative Hardware Architecture Physics & Memory Roofline Formulations

Hardware Architecture Class	Bus Interface Width	Peak Memory Bandwidth	Sustained Saturation	Idle / Active System Power	Capital Cost / Mtok
Sovereign Unified APU	256-bit LPDDR5X-8000	256.0 GB/s	87.0% sustained	15 W / 108 W	$0.65 / Mtok
Desktop Dual-Channel DDR5	128-bit DDR5-6000	96.0 GB/s	78.0% sustained	45 W / 175 W	$1.20 / Mtok
Server 8-Channel DDR5	512-bit DDR5-4800	307.2 GB/s	81.0% sustained	95 W / 380 W	$2.10 / Mtok
Datacenter HBM Accelerator	5,120-bit HBM3	3,350.0 GB/s	72.0% sustained	250 W / 1,050 W	$9.50 / Mtok

Roofline Formula & Analytical Equations

Generation Speed (tok/s) = (Peak_BW * Efficiency) / (Active_Weights_GB + 0.05 * KV_Cache_GB)

Energy (J/token) = Wall_Power_Watts / Generation_Speed_tok_s

Amortized TCO ($/Mtok) = Electricity_Cost + Capital_Depreciation_Rate

RoamingPigs AI MODULAR BENCH