snath-ai/Lar-JEPA: 0.2.0 — The Nervous System: Universal Model Routing for LLMs, JEPAs, and Every Cognitive Architecture That Follows.

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DOI

10.5281/zenodo.19646376

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Zenodo

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Aug 23, 2026

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Nov 6, 2026

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Apache License 2.0

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Nov 6, 2026

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v0.2.0 — The Nervous System Universal Model Routing for LLMs, JEPAs, and Every Cognitive Architecture That Follows Released: 18 April 2026 Author: Aadithya Vishnu Sajeev / Snath AI Repository: snath-ai/Lar-JEPA Prior Art Preprint: DMN v3.0 "The Dream Loop" (Zenodo, April 2026) What Changed This release reframes and extends the Lár-JEPA repository from a JEPA-specific orchestration showcase into its correct architectural identity: a universal cognitive routing nervous system capable of routing any model type — large language models (LLMs), JEPA world models, diffusion models, state-space models (SSMs), graph neural networks (GNNs), and any architecture that follows — as first-class, equally routable nodes within the same deterministic Lár execution spine. New: AbstractCognitiveNode — The Universal Node Interface File: core/interfaces.py Introduced AbstractCognitiveNode as the root interface for all cognitive components in the Lár routing graph. Any model type implements three methods: def encode(self, input_signal: Any) -> Any # Signal → internal context def forward(self, context: Any) -> Any # Core inference pass def decode(self, representation: Any) -> Any # Output → GraphState signal AbstractManifold (the JEPA-specific world model base) is now a subclass of AbstractCognitiveNode, placing JEPAs and LLMs at the same level of the type hierarchy. AbstractEntropicRouter is retained as the JEPA-specific routing spine component. New: AbstractContextBridge — Cross-Modal Signal Adapter File: core/interfaces.py Introduced AbstractContextBridge: a stateless signal conduit that adapts one AbstractCognitiveNode's output SignalType to another node's expected encode() input. This enables: LLMs attending to JEPA latent predictions — bridge converts LATENT_EMBEDDING → TEXT or prefix embedding for LLM context window. JEPAs conditioning on LLM semantic embeddings — bridge converts TEXT / GRAPH_STATE → manifold-compatible context vector. Any future cross-modal composition — new bridge declares source_signal_type and target_signal_type, implements bridge(). Bridges hold no model weights. They are pure format adapters — the synaptic connectors of the nervous system. New: ModelType, SignalType, CompositionPattern Enums File: core/types.py ModelType All model types are first-class citizens in the Lár routing graph: | Value | Description | |---|---| | LLM | Large Language Model (any provider via LiteLLM) | | JEPA | Joint-Embedding Predictive Architecture | | DIFFUSION | Diffusion / score-matching model | | SSM | State Space Model (Mamba, S4, RWKV, etc.) | | GNN | Graph Neural Network | | CLASSICAL | Deterministic non-neural function | | HYBRID | Heterogeneous composite | | FUTURE | Any architecture not yet named or invented | The FUTURE value is a formal architectural claim: any model type that does not yet exist will implement AbstractCognitiveNode and become routable without modifying the spine. SignalType Typed signal vocabulary for cross-node communication: TEXT, LATENT_EMBEDDING, LGSL_SPEC, GRAPH_STATE, STRUCTURED_DATA, TENSOR, IMAGE, AUDIO, GRAPH, DISTRIBUTION. CompositionPattern Eight named cross-modal composition design patterns: LLM_ROUTES_JEPA, JEPA_INFORMS_LLM, PARALLEL_HOMOGENEOUS, PARALLEL_HETEROGENEOUS, HIERARCHICAL, CROSS_ATTENTION, SEQUENTIAL_PIPELINE, RECURSIVE_SELF_IMPROVEMENT. New: JEPA_DMN_Consolidation_Node — Live JEPA→DMN Bridge File: dmn_integration/consolidation_node.py The consolidation stub has been replaced with a functional bridge