Cyber

active inference

Updated Nov 8, 20254 min read

active inference x cft: summary and integration plan

executive summary

  • active inference gives a single principle for agents to perceive, learn, and act by minimising variational free energy.
  • cft models collective attention as token-weighted random walks converging to a stationary distribution (collective focus).
  • fusing them yields a self-configuring network where each neuron updates beliefs and adjusts links to lower expected free energy, improving stability, curiosity, and robustness.
  • precision (confidence) becomes an on-chain economic signal that prices prediction errors and filters noise.

key mappings between active inference and cft

  • hidden states ↔ latent attributes of particles and edges in the cybergraph
  • observations ↔ measured traffic of random walks, link arrivals, weight changes
  • generative model ↔ each neuron’s local probabilistic model of link dynamics and token flows
  • prediction error ↔ divergence between expected focus distribution and realised traffic
  • precision (confidence) ↔ adaptive token staking and edge weights that amplify trusted signals
  • free energy ↔ upper bound on global uncertainty over graph states; minimised at focus convergence

minimal algorithmic spec

  • belief representation: variational posterior q_θ(z) over latent graph states z per neuron; parameters θ stored locally.
  • free energy: F = Eq_θ[−log p(s, z)] + H[q_θ], with s the local observations (traffic, link events). goal is to reduce F.
  • expected free energy for planning: G(π) = risk + ambiguity ≈ Eq[−log p(preferred s | z)] + Eq[H[p(s | z)]], guiding policy π over link edits and sampling actions.
  • precision control: learn/logit-scale precisions λ for different error channels; use soft attention to weight updates.
  • hierarchical markov blankets: discover clusters (modules) with dense internal edges; perform message passing within and between blankets for scalability.

reference update loop (pseudocode)

for epoch in epochs:
  for neuron i in graph:
    s_i ← observe(local traffic, link arrivals, token flows)
    \hat{s}_i ← predict via generative model
    ε_i ← s_i − \hat{s}_i                      # prediction error
    θ_i ← θ_i − η_θ * ∇_θ F(s_i; θ_i, λ_i)     # perception / learning
    λ_i ← λ_i − η_λ * ∇_λ F                    # precision tuning
    a_i ← argmin_π G_i(π; θ_i, λ_i)            # choose action policy
    execute(a_i)                               # edit edges, stake, sample

integration roadmap

  • modelling
    • define a neurally inspired generative model p(s, z) for link dynamics conditioned on local focus, trending content cues, and governance events.
    • specify preference distributions over observations (e.g., high-quality citations, low spam entropy) to ground goal-directed behaviour.
  • protocol layer
    • add a lightweight variational message-passing step to the existing compute kernel so neurons exchange sufficient statistics before committing writes.
    • implement precision-weighted staking where tokens back the reliability of subgraphs and price prediction-error channels.
  • scalability
    • form markov-blanket modules via community detection; schedule intra-module updates at high frequency and inter-module updates at lower frequency.
    • use sparse, low-rank approximations for θ and amortised inference for q_θ(z) to keep costs bounded.
  • evaluation
    • run ablations on the test-net comparing baseline cft vs cft + active inference on convergence speed, adversarial resilience, retrieval accuracy, and compute cost.
    • track free-energy and precision maps as primary diagnostics.

expected benefits and risks

  • benefits
    • faster, more stable convergence under uncertainty and drift
    • intrinsic curiosity drives exploration without central control
    • robustness: anomalous regions get down-weighted via precision control
    • interpretability: free-energy heatmaps show why attention moves
  • risks / mitigations
    • overfitting preferences: adopt plural preference priors and rotate committees
    • precision gaming: require skin-in-the-game with slashing on bad forecasts; diversify error channels
    • compute overhead: amortise inference, cache sufficient statistics, schedule updates asynchronously

open research questions

  • what precision-staking regime best aligns epistemic efficiency with token economics under real traffic?
  • where are phase transitions in emergent intelligence when adding hierarchical markov blankets to cft?
  • how to calibrate preference distributions without central authority while avoiding sybil manipulation?
  • which approximate-inference methods (e.g., natural gradients, lo-fi variational families) give the best performance-compute tradeoff on very large graphs?

immediate next actions

  • formalise a concrete free-energy objective for the current cyberrank kernel and derive local gradients.
  • prototype the message-passing layer on a small subgraph and measure free-energy descent and retrieval quality.
  • design and test precision-weighted staking rules with simulated adversaries before on-chain trials.
  • prepare ablation metrics, dashboards, and free-energy map visualisations for the next test-net cycle.

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