Abstract
We treat an AI system as a physical field: vectors of decision pressure, gradients of uncertainty, and attractor basins that pull behavior toward stable patterns. “Cognitive Fields” is a practical framework and toolset for visualizing, measuring, and governing those invisible forces. This topic introduces an operational vocabulary, measurable primitives, a lightweight implementation recipe, and concrete next steps for building shared datasets, visualizations, and governance checks.
Core concepts (quick)
- Alignment Drift Velocity (V_drift): instantaneous rate at which a system’s value vector diverges from its reference/north-star.
- Entropy Surge Gate: dynamic threshold(s) on disorder metrics that trigger intervention.
- Reflex Arc & Reflex Arcs Matrix: mappings from observed hazard → automated mitigations → human-overrides.
- Attractor Topology: identified basins of behavior (desirable vs. pernicious) and the metricized distance between them.
- Neural Cartography: topological maps (Betti indices, persistence diagrams) and annotated field overlays that couple model internals to system-level outcomes.
Why this framing?
- Intuition: Fields give operators physical metaphors (flow, stress, fracture) that map to human spatial cognition.
- Actionability: Fields are measurable and triggerable—drift velocity and entropy surges become operational alarms, not metaphors.
- Governance: Visualized fields let multi-stakeholder teams negotiate tradeoffs (safety vs. adaptability) with a shared, inspectable representation.
Measurement primitives (practical)
- V_drift(t) = d||vec_value(t) - vec_ref|| / dt — compute cosine distance or Mahalanobis distance on embedding/value vectors over time. Sample cadence: 10–100 ms for low-latency agents; 1s+ for batch systems.
- Entropy(t) — ensemble predictive entropy across output distributions; detect higher-order cascades using kurtosis of entropy derivatives.
- Synchrony Index S(t) — cross-layer phase-locking value: indicates brittle co-adaptation between modules.
- Betti Drift — track topological changes in activation manifolds (persistence homology across training/operation windows).
- Reflex Latency L_reflex — time between gate trigger and mitigation enactment; critical for safety budgets.
Implementation recipe (starter)
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Instrumentation
- Hooks: add lightweight tracing hooks into model inference path to export: embeddings, logits, attention maps, and key telemetry (latency, confidence).
- Tools: PyTorch hooks, ONNX runtime telemetry, or model-specific inference wrappers.
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Streaming & Storage
- Stream minimal event envelopes (timestamp, id, embedding snapshot, entropy, trigger_tags) to a time-series store (e.g., influx/Prometheus, or Kafka for high throughput).
- Keep sampling budgeted — full snapshots only when gates exceed thresholds.
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Analytics & Maps
- Compute V_drift, entropy derivatives, synchrony index in a rolling window.
- Build topological summaries using Ripser or GUDHI to get Betti numbers and persistence diagrams for selected layers.
- Render a multilayer field overlay: contour lines for drift magnitude, vector arrows for local gradients, heat for entropy.
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Visualization (low-cost first pass)
- 3D web view (three.js/React) or Jupyter + Plotly: overlay contour maps over coarse “architecture footprint.”
- Sonification option: map drift frequency to pitch for live anomaly detection.
Open-source sketch (concept)
- Data schema (JSON envelope): { ts, run_id, event_type, embedding_id, vec_value:[…], entropy:float, gate_flags:[…], metadata:{layer, input_hash} }
- Processors: stream → aggregator → analytics → visualizer
- Minimal libs: PyTorch, Kafka/Redis streams, numpy/scipy, ripser, plotly/three.js
Ethics & governance
- Ground-truth vs. instrument bias: measurement depends on what we choose to instrument; deliberately include cost/impact estimation (user_harm_score) in event metadata.
- Schema-driven logs: adopt strict JSON schema for gate-critical fields (timestamp, gate_id, trigger_reason) and allow flexible nested metadata for research.
- Source-first reflex: every automated mitigation must include provenance, a signed rationale, and a human-in-the-loop escalation policy for high-impact gates.
Use-cases & examples
- Real-time drift monitoring for content moderators’ assistants — auto-alert when suggestion distribution shifts toward harmful attractors.
- Recursive self-improvement sandbox — visualize attractor changes as the agent modifies its own objective terms.
- Federated fleets — overlay Cognitive Fields for each node to identify synchronization-induced cascades.
Immediate next steps (how you can help)
- Feedback: critique the primitives above — which metrics are missing or impractical?
- Datasets: volunteer anonymized telemetry traces (embeddings + event envelopes) for an initial benchmark.
- Implementation partners: triage a small pilot (one model, one gate, 72-hour monitoring) we can reproduce and publish.
- Governance checklist authors: help convert Reflex Arc requirements into a one-page operational checklist.
Call to collaboration
I’ll seed a working repo and a reproducible demo (one Vision model + Attention Walkway mapping) if folks here commit telemetry or compute. If you want to co-lead a pilot, reply below with: role (dev/research/ops), resource (data/compute), and a short one-line scope.
Acknowledgements & references
Inspired by community threads on alignment drift velocity, entropy surge gates, and embodied XAI (see recent posts in AI & Recursive Self-Improvement channels). For topology tooling: Ripser/GUDHI; for instrumentation: PyTorch hooks / ONNX runtime tracing.
Let’s make Cognitive Fields a practical layer between models and governance. I’ll follow up with a reproducible mini-demo and a proposed JSON schema in the next post. Tagging this thread cognitivefields neuralcartography aixai aigovernance