Research

Inference-Phase Dynamics & Predictive Cognitive Stability

Inference-Phase Dynamics

This research program studies runtime behavioral stability in large language models and agentic systems.
We treat inference as a measurable dynamical process and develop output-only instrumentation that detects drift, instability, and collapse before failures appear in output without accessing model weights, training data, gradients, or proprietary internals.

What this research produces:
metrics, validation protocols, and deployable instrumentation that plug into modern inference stacks.

Primary outputs:
ZSF·Ω, Experiment 101, and FieldLock™.

1️⃣ What we mean by “Inference-Phase Dynamics”

When an LLM generates text, it moves through a sequence of internal representational states. Industry typically measures inference in terms of speed and cost (latency, throughput, GPU utilization). This research measures inference in terms of behavioral stability over time:

  • Does the run remain coherent across long horizons?

  • Does it drift from constraints or objectives?

  • Does it enter unstable regimes that predict collapse?

We study these dynamics using observable signals only (model outputs and derived embeddings), making the approach model-agnostic and compatible with hosted APIs and local deployments.

2️⃣ What we measure

The research program defines a compact metric stack that captures behavioral motion over time:

  • Drift (D): cumulative displacement of output representations across turns

  • Curvature (K): deformation/bending in the trajectory associated with instability formation

  • Contraction / Expansion (J): whether trajectories collapse toward failure regimes or stabilize

  • Entropy proxy (H): dispersion/disorder growth indicating loss of structure

  • Stability proxy (I): persistence of stable regimes and resistance to perturbation

These are output-only, accumulate over time, and are designed to generalize across model families.

3️⃣ What we don’t do

This research does not require, use, or assume:

  • access to model weights or gradients

  • fine-tuning or retraining

  • architecture-specific hooks

  • proprietary datasets

  • “interpretability into weights” claims

We operate at the observational layer: inference telemetry → stability metrics → predictive regimes.

4️⃣ Research Domains

Core Domains

Inference-Phase Dynamics
Runtime stability and regime transitions during generation.

Behavioral Drift, Stability & Collapse Prediction
Early warning signals and lead-time measurement prior to observable failures.

Agent Stability Under Recursion
Failure modes in long-horizon agents: loops, goal drift, tool cascades, correction instability.

Cross-Model Normalization & Regime Invariance
Normalization methods that allow shared thresholds and comparable stability scoring across different model families.

Temporal Coherence in Long-Horizon Runs
Sequencing integrity and consistency under extended context, tool use, and iterative correction.

5️⃣ Validation

Validation Approach

We validate the research in three ways:

  1. Cross-model runs on multiple model families using the same prompt battery classes

  2. Pre-registered failure marker rubric for timestamped “t₂” failure events

  3. Lead–lag evaluation measuring predictive window Δt = t₂ − t₀ (metric spike to failure)

Primary validation protocol: Experiment 101 (cross-model drift–collapse prediction).

6️⃣ Systems produced by the research

Instruments

ZSF·Ω (Zero State Field Instrument)
A diagnostic instrument that reconstructs inference-phase trajectories and regime transitions from observable telemetry.

Experiment 101
A standardized institutional experiment for cross-model drift/collapse prediction with reviewer-proof evaluation logic.

FieldLock™
Production implementation of the monitoring layer: real-time scoring, alerts, stabilization hooks, and integration into enterprise observability stacks.

7️⃣ Publications & access model

Publications & Access

Formal technical materials, protocols, and instrument specifications are maintained as part of an ongoing research program. Some materials are publicly available; others are shared selectively with partners and labs to prevent misinterpretation, premature compression, or ungrounded reproduction.

If you are a lab, platform team, or investor evaluating the work, request access to:

  • Experiment 101 repo + runbook

  • telemetry contract + adapter interfaces

  • ZSF·Ω demo harness

  • FieldLock™ architecture brief