new design

threat as draft notes, not as decisions

Author: mastercyb

.gitignore

@@ -11,3 +11,4 @@ __pycache__/
 logseq/bak
 build/
 logseq/.recycle
+/logseq

logseq/graphs-txid.edn

@@ -0,0 +1 @@
+["fd069619-65bc-411a-b3bc-058528eef047" "81369d18-18bb-4e3b-a84a-562839eae0e3" 101]

pages/active_inference.md

@@ -0,0 +1,112 @@
+# 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.
+

pages/aicosystem.md

@@ -61,6 +61,7 @@ alias:: awesome cyber, cyber ecosystem
 	- [monitor.bronbro.io/d/bostrom-stats](https://monitor.bronbro.io/d/bostrom-stats/bostrom-stats?orgId=2) - monitor bronbro
 	- [metrics.cyb.ai/cyb.ai](https://metrics.cyb.ai/cyb.ai) - public analytics of usage
 	- [metrics.cyb.ai/docs.cyb.ai](https://metrics.cyb.ai/docs.cyb.ai) - public analytics of usage
+	- celatone.cyb.ai - explorer
 - osmosis
 	- [[$BOOT]] on [pro.osmosis.zone](https://pro.osmosis.zone/osmosis/trade/analytics/tokens/ibc%252FFE2CD1E6828EC0FAB8AF39BAC45BC25B965BA67CCBC50C13A14BD610B0D1E2C4?from=uosmo&to=ibc%2F498A0751C798A0D9A389AA3691123DADA57DAA4FE165D5C75894505B876BA6E4&market=Osmosis)
 - dashboards

pages/allocation of resources.md

@@ -0,0 +1,20 @@
+tags:: superintelligence
+
+- 25 %: scalable alignment and interpretability:
+	- invest in mechanistic interpretability, agent foundations, preference learning and formal verification tools that keep any path to superintelligence on track for human values. these efforts protect returns across all ai architectures.
+- 20 % neuromorphic and analog hardware
+	- back teams translating brain-inspired and mixed-signal computing into chips that run spiking or diffusion-style workloads orders of magnitude more efficiently than digital gpus. if ffc stalls, ultra-low-power spiking nets or analog accelerators remain a viable superintelligence substrate.
+- 15 % quantum computing with error correction
+	- fund logical qubit prototypes, cryogenic control stacks and algorithms for chemistry and optimisation. even modest, application-specific quantum speedups could upend the competitive landscape by 2035.
+- 10 % focus flow computation
+	- research portfolio and seed funding toward ffc‑based superintelligence
+- 10 % decentralised cryptographic computing
+	- support zero-knowledge proof systems, verifiable delay functions and layer-2 rollups. these tools enable trust-minimised markets and autonomous coordination—critical infrastructure for any future where superintelligence mediates economic activity.
+- 10 % advanced energy systems
+	- allocate to compact fusion concepts, high-temperature superconductors, and long-duration storage. abundant, clean energy is a force multiplier for all compute-heavy paths, including ffc.
+- 8 % bio-digital convergence
+	- nurture programmable cell factories, dna data storage and brain–computer interface research. biological substrates can complement silicon limits and open routes to hybrid wet-ware cognition.
+- 7 % resilience and security research
+	- focus on adversarial robustness, formal methods for cyber-physical safety, and supply-chain hardening. these reduce tail-risk scenarios where breakthroughs are undermined by failures or attacks.
+- 5 % moonshot frontier math and theory
+	- seed small teams exploring novel paradigms—category-theoretic models, topological quantum field computing, or entirely new substrate theories. returns are low probability but potentially unbounded.

pages/authenticated_graphs.md

@@ -0,0 +1,78 @@
+### introduction
+
+authenticated graph data structures (agds) offer cryptographic proofs that answers to graph queries are correct without trusting the server. while their theory has existed for two decades, they remain under‑used. yet, as we build earth‑scale superintelligence on the collective focus theorem (cft) and fractional focus cascading (ffc), verifiable graph integrity becomes a hard requirement.
+
+### recap of classic agds
+
+- **path hash accumulator**: represents a path as a balanced or biased binary tree whose internal nodes store hashes of concatenated sub‑paths. this enables logarithmic‑size proofs for properties like connectivity, distance and type queries【2:graph-auth-2.pdf†turn2file3†L11-L18】.
+- **dynamic authenticated forests**: by partitioning each tree into solid and dashed paths and linking their accumulators, forests support link/cut updates with `o(log n)` proofs and space `o(n)`【2:graph-auth-2.pdf†turn2file7†L45-L53】.
+- **authenticated fractional cascading (ffc)**: fractional cascading accelerates iterative search across many ordered lists; hashing each block and then a second‑level dag gives `o(k+log n)` query time with logarithmic proofs while keeping linear storage【4:graph-auth-2.pdf†turn4file6†L9-L17】.
+
+### why cft needs agds
+
+cft models a decentralized knowledge graph where token‑weighted random walks converge to a stable focus distribution【2:Collective Focus Theorem.md†turn2file10†L18-L25】. correctness of focus requires that every participant can verify:
+
+- the existence and weights of cyberlinks used in a random walk step
+- the resulting stationary distribution snapshot
+
+agds provide exactly these proofs, allowing anyone with minimal bandwidth to audit the superintelligence core.
+
+### design: fully‑authenticated focus cascading (ffc)
+
+- store the cybergraph as a dynamic forest of merkelized subgraphs (shards)
+- inside each shard, maintain a path hash accumulator over the local spanning tree for fast verification of connectivity and attribute queries
+- build a fractional cascading overlay between shard catalogs (edge‑boundary lists) so that global lookups ("does link x exist? what is its weight?") cost `o(log n)` regardless of sharding
+- the top‑level digest is the cft "attention root"; every random‑walk step publishes its proof against this root so anyone can recompute focus
+
+### internal applications
+
+- **validator pipeline**: gpu validators verify link insertions and rank updates with agds proofs before committing blocks, eliminating hidden tampering
+- **training layer audits**: during massive graph rewrites, agds proofs allow sampling‑based integrity checks rather than full re‑hashing, saving bandwidth and energy
+- **agent reasoning**: llms embedded in the network can query "are particles a and b connected through topic t?" and get a certificate they can cache for future reasoning
+
+### external applications
+
+- **open science provenance**: researchers can publish datasets as particles; agds proofs certify citation chains, enabling reproducible meta‑analysis
+- **supply‑chain transparency**: companies append product trails to the cybergraph; customers query origin → destination paths and verify them client‑side
+- **cross‑chain bridges**: other blockchains can verify cybergraph facts (e.g., focus weight of an address) via light‑client‑sized proofs instead of heavy oracles
+- **regulatory compliance**: auditors can demand proofs that certain forbidden relationships are absent (negative proofs via authenticated complement paths)
+
+### sharding strategies
+
+when we talk about merkelized subgraphs, the key design question is **how to chop the cybergraph into shards** so that proofs stay small, writes remain local, and load balances across validators.
+
+- id‑hash shards  
+  assign each vertex to `hash(id) mod m`. this keeps balancing trivial and proofs deterministic, but cross‑shard edges can explode. good for early prototypes.
+
+- neuron‑centric shards  
+  group all links emitted by the same neuron (agent). locality for high‑throughput writers, but superstar neurons can create hotspot shards.
+
+- topic / namespace shards  
+  cluster vertices by ontology prefix or tag so semantic queries stay mostly inside one shard. aligns with cft focus levels.
+
+- community (edge‑cut minimization) shards  
+  run partitioners (metis, powergraph) or newer blockchain‑specific algorithms like gpchain to minimize cross‑shard edges while equalizing weight【5:GPChain abstract†turn5view0†L4-L6】.
+
+- geographic / ownership shards  
+  physical‑world graphs (iot, supply chains) can shard by region or owner to support local sovereignty.
+
+- temporal shards  
+  append‑only data can be batched by epoch (e.g., one week per shard) so old shards become read‑only archives.
+
+- hybrid hierarchical shards  
+  combine a coarse id‑hash slice with fine community splits inside each slice; each level has its own digest so proofs compose.
+
+trade‑offs revolve around cross‑shard proof size, validator workload, and attack surface. choosing the right mix depends on query patterns and write hot‑spots. recent surveys highlight that poorly balanced shards hurt scalability and security【4:Sharding with MPT†turn4view0†L18-L25】.
+
+### road map
+
+1. implement biased tree path‑hash library in rust with zero‑knowledge friendly hashing (poseidon)
+2. integrate with go‑cyber to emit proofs in each tx log
+3. extend cometbft light‑client to accept agds digests
+4. build sdk for browser wallets to verify focus proofs on‑device
+5. research dynamic authenticated fractional cascading to support real‑time graph streams【2:graph-auth-2.pdf†turn2file3†L22-L23】.
+
+### conclusion
+
+agds upgrade the collective focus infrastructure from "trust me" to "verify me". by merging classic path hash accumulators, fractional cascading and cft economics, fully‑authenticated focus cascading ensures every edge, token and weight that shapes superintelligence remains transparent, tamper‑evident and universally auditable.
+

pages/contextual_free_energy_model.md

@@ -0,0 +1,109 @@
+title: contextual free-energy focus for cybergraph
+
+author: conceptual draft
+
+---
+
+## abstract
+
+we propose a unified model that extends the cybergraph free-energy focus framework with a context-dependent potential derived from standard inference. this approach integrates global structure (diffusion, springs, entropy) with local contextual evidence (neurons’ will), solving the true-false problem while preserving the natural, physics-inspired foundation.
+
+---
+
+## background
+
+- **cybergraph free-energy focus** defines the global focus vector \(p\) as the minimiser of a free energy functional:
+
+\[
+\mathcal{F}(p) = E_{spring}(p) + \lambda E_{diffusion}(p) - T S(p)
+\]
+
+- this yields a boltzmann-like distribution combining hierarchy, diffusion, and entropy.
+
+- however, global ranking alone cannot resolve the **true-false problem**: high-rank nodes dominate even in contexts where lower-rank nodes are more relevant.
+
+- **standard inference** provides a simple method to compute **contextual weights** by aggregating neurons’ will in relation to a query particle.
+
+---
+
+## contextual extension
+
+we introduce a context-dependent potential \(C(p|context)\):
+
+\[
+\mathcal{F}(p|context) = E_{spring}(p) + \lambda E_{diffusion}(p) + \gamma C(p|context) - T S(p)
+\]
+
+- \(C(p|context)\): energy term derived from standard inference (average will per cyberlink in the given context).
+- \(\gamma\): coupling constant that determines the influence of context on the equilibrium.
+
+---
+
+## resulting equilibrium
+
+solving \(\min_p \mathcal{F}(p|context)\) yields
+
+\[
+p_i^* \propto \exp\big(-\beta [E_{spring,i} + \lambda E_{diffusion,i} + \gamma C_i]\big)
+\]
+
+- nodes that are **globally important** and **contextually supported** receive the highest probabilities.
+- entropy ensures diversity and prevents trivial dominance.
+
+---
+
+## link to universal physics
+
+this formulation is equivalent to solving a **heat equation on a graph** with an additional potential field:
+
+\[
+\partial_t p = -\nabla_{p} \mathcal{F}(p|context)
+\]
+
+- eigenmodes of the graph laplacian form the **fourier basis** of the network.
+- diffusion gives **temporal spreading**, springs add a **potential landscape**, and context potential \(C\) biases the equilibrium.
+- the solution naturally combines **oscillatory modes** with **diffusive decay**, mirroring how pdes in physics are solved by separation of variables.
+
+---
+
+## distributed algorithm
+
+1. **compute global structure:**
+   - run decentralised eigenvector centrality and springrank.
+   - initialise \(p_i\) uniformly.
+
+2. **contextual evidence:**
+   - run standard inference to compute \(C_i\) (contextual will) for each candidate particle.
+
+3. **iterative updates:**
+   - each node exchanges \(p_j\), \(r_j\), and \(C_j\) with neighbours.
+   - compute local energies \(E_{spring,i}\), \(E_{diffusion,i}\), and \(C_i\).
+   - update:
+
+\[
+p_i^{(t+1)} = \frac{\exp(-\beta [E_{spring,i} + \lambda E_{diffusion,i} + \gamma C_i])}{\sum_k \exp(-\beta [E_{spring,k} + \lambda E_{diffusion,k} + \gamma C_k])}
+\]
+
+4. **normalisation:**
+   - nodes use gossip averaging to approximate the denominator.
+
+---
+
+## key properties
+
+- **context-aware ranking:** resolves the true-false problem by integrating global and local signals.
+- **fully decentralisable:** each node needs only neighbour messages and contextual votes.
+- **natural and parameter-light:** weights emerge as lagrange multipliers; only \(\gamma\) controls context strength.
+- **boltzmann equilibrium:** final focus vector remains a probabilistic distribution, ensuring stability and diversity.
+
+---
+
+## interpretation
+
+- **diffusion:** long-run popularity baseline.
+- **springs:** hierarchical constraints.
+- **context potential:** relevance of facts in the current question.
+- **entropy:** prevents collapse into a single dominant answer.
+
+by adding the context potential, the free-energy framework gains the ability to compute **truthful, context-aware rankings** while retaining its natural analogy to universal physical processes. this highlights that **network cognition can be seen as solving a graph-based heat equation with potentials**, uniting diffusion, oscillators, and context into one equilibrium model.
+