Unified Master Telemetry Glossary & Metric Specification

Abstract

UNIFIED MASTER TELEMETRY GLOSSARY & METRIC SPECIFICATION MyceliaLM Cognitive Architecture & MASSIF Latent Trajectory Framework Continuous SDE Kinematics, Closed-Loop Governors, Pressure Tensor Diagnostics, and Live Dashboard Telemetry Daniel Solis -- Principal Investigator, Dubito Inc. / Ergo Sum AGI Safety Systems October 2026 -- Version 3.6 Complete Unified Synthesis Abstract This document serves as the unified, mathematically complete, and rigorous master glossary synthesizing all parameters, observables, control variables, and diagnostic metrics from the MyceliaLM Telemetry Dashboard Parameter Glossary, the MASSIF (Multiscale Attractor Stability & Stress Inference Framework) Telemetry Glossary, and the newly added Missing Dashboard Telemetry Additions. It bridges continuous-time Itô Stochastic Differential Equation (SDE) trajectory kinematics, Fokker-Planck jump diagnostics, depth-dependent friction cascades, closed-loop cybernetic governor stacks, pressure tensor thermodynamics, Kuramoto-inspired order parameters, and live production training dashboard telemetry. Every metric is defined with its exact mathematical expression, plain-language translation, target/healthy operational ranges, and diagnostic significance for ex-ante AI safety. 1. Modification and Addition Log Comprehensive Audit & v3.6 Revision Log This master document unifies the operational dashboard specification (MyceliaLM Telemetry Dashboard), the theoretical trajectory analysis specification (MASSIF Telemetry Glossary), and the newly added Missing Dashboard Telemetry Additions. The following synthesis rules, additions, and modifications were applied: Unification of Terminology & Notation: Standardized notation across all sources. Hidden residual states are defined as hₜ⁽ˡ⁾ ∈ ℝᵈ, velocity as vₜ⁽ˡ⁾, persistence as Iₜ, local tension as σₜ, and optimization pressure as Π. Integration of Missing Governor & Kinematic Metrics: SoftCap Governor Telemetry: Added hit_ratio, max_raw, and mean_raw tracking activation stats prior to final layer norm. FFN Veto Ratio (FFNVeto): Added explicit ratio of clamped tokens across layers alongside mean_norm, max_norm, and target thresholds. Surfer Target Actuation (SURFER_TARGET / τ_α): Added dynamic target threshold set by the Phase-Transition Surfer algorithm based on χ_R. Sub-Basin Jump Indicator (SBTI): Added loss landscape transition indicator flagging major basin jumps (SBTI > 0.05). Cross-Token Directional Coherence (c_l): Added sequence-wide mean cosine similarity distinguishing individual from collective ballistic flow. Spectral Concentration (S_concentration): Added ratio of top-k singular values to total spectral mass of residual states. Historical Sliding Window Buffers (I-field & Conf-field): Added 24-step sliding window arrays tracking temporal trends of instability and forecast confidence. Data Tapestry & Meta-Governor Telemetry: Added exact source attribution tags (FW_EDU, FW_ORIG, STANFORD, CZ_fiction, EN_Fact-TCM, web_math, TEACHING), pH-Stir ratio (85/15), and Meta-Governor Circuit Breaker (CB) status levels (GREEN, YELLOW, RED). Cold-Start Artifact Pruning Protocol: Detailed checkpoint-resume ghosts caused by zero-initialized in-memory variables (Ẇ_gov ≈ 150-160, σ̇²_head ≈ +0.63 → +0.73, fake loss plunge, GCI = 0.00) and explicit exclusion/pruning rules. Live Dashboard Printout Annotations: Added an explicit, line-by-line annotated section demonstrating real production log lines from live SageMaker/T4 training runs. 2. Foundational Hidden-State Geometry & Governor Architecture 2.1 Hidden-State Trajectory Geometry Metric 1.1: Hidden State Residual Vector (hₜ⁽ˡ⁾) What it is: The continuous representational state vector in the residual stream evolving through network layers l ∈ [1, L] and autoregressive time steps t ∈ [1, T]. Calculation: hₜ⁽ˡ⁾ ∈ ℝᵈ, l ∈ {1, ..., L}, t ∈ {1, ..., T} In Words: "The hidden state at layer l and timestep t is a vector belonging to the d-dimensional real vector space ℝᵈ, evolving sequentially across layers l from 1 to L and generation steps t from 1 to T." Target / Healthy Values: Mean Euclidean norm ‖hₜ⁽ˡ⁾‖₂ bounded between 18.0 and 24.0 Activation Magnitude Units. Why it matters: Treats model execution as a continuous Riemannian trajectory rather than a black-box function, enabling real-time differential geometric telemetry during the forward pass. 2.2 The Four-Loop Closed-Loop Governor Stack Metric 1.2: FFN Veto Governor & Veto Ratio (FFNVeto) What it is: Activation magnitude and clamping ratio of the feed-forward network (FFN) sub-layer contribution across layers. Calculation: ‖h_FFN⁽ˡ⁾‖₂ ≤ τ_FFN, FFNVeto Ratio = (1/L) Σ 𝟙[‖h_FFN⁽ˡ⁾‖₂ > τ_FFN] In Words: "The FFN Veto ratio equals the fraction of layers where the Euclidean norm of the feed-forward network state h_FFN exceeds target threshold τ_FFN, accompanied by mean norm and max norm." Target / Healthy Values: Target threshold τ_FFN = 50.0 (or 150.0 in late epochs). Normal operation: veto ratio < 20.0%; under heavy factual retrieval: up to 100.0%. Why it matters: Prevents FFN sub-layers from overwhelming attention representations with high-norm memorized facts or hallucinated noise. Metric 1.3: Alpha Scaling Governor & Surfer Target (SURFER_TARGET / τ_α) What it is: Combined weighted contribution vector from self-attention (a⁽ˡ⁾) and FFN (f⁽ˡ⁾) branches, governed by the dynamic Phase-Transition Surfer threshold τ_α. Calculation: ‖αₐ · a⁽ˡ⁾ + α_f · f⁽ˡ⁾‖₂ ≤ τ_α(t) In Words: "The combined contribution norm must remain below the Surfer Target threshold τ_α(t), which is dynamically adjusted via cosine ease-in/ease-out based on optimization response χ_R." Target / Healthy Values: SURFER_TARGET ranges dynamically between 20.0 and 60.0 (or up to 150.0). Scale factor typically 0.60 - 0.98. Why it matters: Prevents explosive gain accumulation across sequential transformer layers and forces macro-state condensation when χ_R flips positive. Metric 1.4: Soft Cap Governor Telemetry (hit_ratio, max_raw, mean_raw) What it is: Real-time activation statistics for the SoftCap Governor prior to final layer-norm projection. Calculation: hit_ratio = (1 / T·L) Σ 𝟙[‖rₜ⁽ˡ⁾‖₂ > τ_softplus_start], max_raw = max ‖rₜ⁽ˡ⁾‖₂, mean_raw = mean ‖rₜ⁽ˡ⁾‖₂ In Words: "SoftCap hit ratio equals the fraction of tokens and layers where the raw residual norm exceeded soft compression threshold. Max raw is the highest residual norm observed in the batch; mean raw is the average." Target / Healthy Values: hit_ratio < 20.0%. max_raw bounded below τ_cap = 28.0 (or 15 - 17 in low-norm regimes, < 465 in high-norm runs); mean_raw ≈ 14 - 16. Why it matters: Monitors whether the network is brushing against its maximum representational capacity. High hit ratios signal that the model is constantly pushing larger activations than allowed safely. Metric 1.5: Model Predictive Control (MPC) Governor (I_combined) What it is: Predictive instability score calculated by a temporal Kalman/Bayesian forecaster observing trend acceleration. Calculation: I_combined⁽ˡ⁾ ≤ I_target In Words: "The trend-extended combined instability score I_combined at layer l must remain less than or equal to target instability I_target." Target / Healthy Values: Target threshold I_target = 0.45 - 0.70. Intervention rate < 1.0% (or up to 20-40% during distribution shifts). Why it matters: Provides proactive, predictive intervention by executing fractional dampening before trajectory degeneration serializes into output tokens. 3. Stochastic Differential Equation (SDE) & Fokker-Planck Diagnostics Metric 2.1: First-Order Drift Velocity (D⁽¹⁾ / μ_drift) What it is: Deterministic first-order drift vector driving logical progression through latent semantic space under an Itô SDE (dz_t = D⁽¹⁾ dt + √(2D⁽²⁾) dW_t). Calculation: D⁽¹⁾(z) = lim_{Δt → 0} (1/Δt) 𝔼[Δz | z_t = z] In Words: "The first-order drift vector D⁽¹⁾ equals the limit as delta t approaches zero of one divided by delta t, multiplied by the expected displacement delta z given state z." Target / Healthy Values: Magnitude range 18.79 to 21.58. Why it matters: Measures the strength of deterministic reasoning drive versus stochastic diffusion noise. Metric 2.2: Second-Order Diffusion Coefficient / Variance Rate (D⁽²⁾ / Var) What it is: Second-order Kramers-Moyal diffusion tensor measuring conditional variance rate of hidden-state increments. Calculation: D⁽²⁾(z) = lim_{Δt → 0} (1/2Δt) 𝔼[(Δz)(Δz)ᵀ | z_t = z] In Words: "The second-order diffusion coefficient D⁽²⁾ equals the limit as delta t approaches zero of one divided by two times delta t, multiplied by the expected outer product of hidden-state increments delta z with themselves." Target / Healthy Values: Healthy range: 2.2 × 10⁻⁴ to 2.9 × 10⁻⁴. Sudden jump > 4.0 × 10⁻⁴ indicates noise explosion. Why it matters: Quantifies continuous stochastic perturbation acting on the representational trajectory. Metric 2.3: Fourth-Order Kramers-Moyal Coefficient (D⁽⁴⁾ / D4) What it is: Fourth-order stochastic moment auditing higher-order non-Gaussian jump noise. Calculation: D⁽⁴⁾(z) = lim_{Δt → 0} (1/24Δt) 𝔼[(Δz)⁴ | z_t = z] In Words: "The fourth-order Kramers-Moyal coefficient D⁽⁴⁾ equals the limit as delta t approaches zero of one divided by twenty-four times delta t, multiplied by the expected fourth moment of increments delta z." Target / Healthy Values: Healthy range: 10⁻⁷ to 10⁻⁶. Sustained values > 5 × 10⁻⁶ signal non-Langevin jump noise. Why it matters: Tests whether the system obeys continuous Langevin dynamics or suffers from discontinuous state jumps. Metric 2.4: Pawula Diagnostic Ratio (R_Pawula) What it is: Normalized Pawula ratio testing for Fokker-Planck truncation validity (D⁽ⁿ⁾ = 0 for n ≥ 3). Calculation: R_Pawula = D⁽⁴⁾ / (V_Pawula)² In Words: "The Pawula ratio R_Pawula equals the fourth-order Kramers-Moyal coefficient D⁽⁴⁾ divided by