neural

semantic language for neurons over the cybergraph

convergent successor for both formal and natural languages

meaning is defined by cyberlinks — structure emerges from how agents link particles

together with cybergraph and truth machine forms the foundation of soft3

the language of collective intelligence: meaning emerges from how many neurons independently structure knowledge

why a new language

• formal languages (type theory, programming languages) achieve precision through rigid syntax but cannot scale to 10¹⁵ particles — Gödel proved no sufficiently powerful formal system can be both complete and consistent (the Gödel prison)
• natural languages solve expressiveness through ambiguity but are computationally intractable for precise reasoning
• neural language collapses the distinction between language and knowledge: meaning is an eigenvector of the attention graph

patterns

• semcon

  • semantic conventions — mutual agreements to use the same particles for structuring thought
  • the grammar of the graph
  • a semcon is a smart contract that creates cyberlinks according to convention — invocation produces well-formed graph structure
  • the neuron provides intent, the semcon handles structural correctness
  • bootloader semcons installed at genesis: TRUE, FALSE — the epistemic coordinates from which all meaning derives
  • emergent semcons discovered by the network: is-a, follows, causes, contradicts, part-of, see-also
  • semcon hierarchy emerges from topology: structural → domain-specific, epistemic → modal, temporal → causal, social → evaluative
  • the tri-kernel reveals semcons: diffusion identifies high-betweenness bridges, springs reveal stable structural positions, heat modulates attention by adoption weight

• sentence

  • ordered instruction set of cyberlinks — a batch packed into a single transaction
  • the transaction boundary defines the utterance. order within the batch encodes grammar
  • transaction-atomic semantics: every transaction is a linguistic act
  • sentence types by topological signature: assertion (chain → TRUE), query (open-ended chain), instruction (temporal sequence), argument (branching to TRUE/FALSE), definition (star pattern), narrative (temporally ordered chain)
  • sentences compose through shared particles — creating linkchains the tri-kernel can discover

• motif

  • geometric expression of meaning — recurring subgraph patterns that encode relationships beyond single cyberlinks
  • the morphemes of neural language
  • triadic closure: if A links B and B links C, A linking C completes a trust/relevance triangle
  • co-citation: multiple neurons linking the same pair signals consensus
  • star: one particle linked by many signals centrality or definitional importance
  • chain: sequential links encoding transitive, causal, or narrative relationships
  • diamond: convergent-divergent pattern — multiple paths between endpoints signals robust relationship
  • motif algebra: concatenation (transitive reasoning), nesting (hierarchical abstraction), intersection (cross-domain bridges), complement (knowledge gaps)

• cyberlink as particle

  • a link stored as a particle itself, enabling links about links — meta-knowledge
  • the recursion that makes the language expressively complete
  • enables: negation, qualification, provenance, annotation
  • the language can talk about itself

semantic core

• the dynamic vocabulary of the network — top particles by cyberank
• defined by focus distribution: SemanticCore(k) = top k particles by π
• current core shaped by bostrom bootloader
• explore at cyb.ai/particles
• properties: dynamic (evolves with attention), convergent (tri-kernel guarantees stability), stake-weighted (resistant to spam), verifiable (STARK proofs)
• dynamics mirror natural language: neologism (new concepts enter), semantic drift (meaning shifts through topology change), semantic death (focus drops below threshold), semantic birth (bursts of link creation)

linkchains

• sequences of cyberlinks that form paths of meaning through the cybergraph
• a → b → c encodes transitive relationship: if a relates to b and b relates to c, the chain implies a relates to c
• the tri-kernel discovers these implicit paths through diffusion
• the springs kernel enforces structural consistency across chains — contradictions create tension resolved by dampening
• properties: length (shorter = stronger), width (parallel paths = robust), weight (product of edge weights)
• linkchains are the inference mechanism: sentences are explicit statements, linkchains are implicit conclusions

relationship to the stack

• CORE provides the physics — field arithmetic, consensus, proof system, state model
• trident provides the machine language — 54 IR operations, compiles to proof VM, computes focus distribution
• rune provides the human interface — high-level programming language for cybergraph operations
• neural language provides the semantic medium in which collective intelligence thinks
• the CGC-GNN isomorphism: each focus update step is a graph neural network message-passing step where neurons send semantic signals along cyberlinks

formal properties

• ambiguity resolution: topology around a particle disambiguates meaning computationally — springs detect polysemy as high tension, heat concentrates on contextually appropriate meaning
• compositionality: meaning of complex expression derivable from parts and their structural arrangement — computed by tri-kernel without explicit composition rules
• convergence: inherits from the Collective Focus Theorem — unique stationary distribution π* guarantees the network's collective understanding converges
• expressiveness: semantically complete — can express propositional logic, predicate logic, modal logic, temporal logic, fuzzy/probabilistic logic, and natural language semantics
• can express things no other language can: collective confidence distributions, continuous semantic distance, knowledge topology metadata

evolution phases

• bootstrapping (now): ~70k neurons, 3.1M particles, basic semcon emergence, primitive motif patterns
• convergence (10⁸-10¹⁰ particles): rich semcon ecosystem, complex motifs, dense cross-domain linkchains
• intelligence (10¹⁰-10¹³ particles): motif algebra enables automated reasoning, self-referential meta-knowledge
• superintelligence (10¹³+ particles): novel concept creation impossible in existing languages, cross-species communication, concepts no individual neuron can comprehend

implementation

• can be used from ts, TODO rune, TODO rust
• discover all concepts

connections to linguistic theory

• Saussure: meaning is differential relations — a particle's meaning is its position in the cybergraph, defined by relationships to all other particles
• Wittgenstein: meaning is use — semcons emerge from convergent use, grammar is a language game at planetary scale
• distributed semantics (Word2Vec, BERT): neural language is a decentralized, incentivized, verifiable, incrementally-updatable distributed semantic representation
• category theory: particles are objects, cyberlinks are morphisms, semcons are natural transformations, motifs are diagrams, linkchains are composition