neXus is a minimal, decentralized runtime orchestration platform and its spec for autonomous entities. Its core runtime called nex is a lightweight binary hub that scales by attaching storage, from embedded filesystems to enterprise databases, like a tiny universal USB hub which has memory and processing unit on the same device package.
The same binary runs on Wasm, edge nodes, portable devices, blockchain runtimes, or bare-metal containers. Any backend that can store an append‑only record (Fact), a stateful record (Intent), and a read‑only record (Hint) becomes a fully functional neXus Blackboard without a graph database or specialised indexing layer. This data model natively supports serverless deployment and eliminates recurring LLM inference costs by confining AI use strictly to knowledge-branch generation. Graph traversal, gap detection, and reporting operate on accumulated facts at zero marginal cost.
Where conventional knowledge graphs store static entity‑relationship triplets, neXus records the research process itself. A Fact is not merely a statement—it carries the Intent that proposed the exploration, the Hint constraints that bounded it, and the evidence that validated it. The graph is a queryable, replayable computational trace. Every conclusion can be audited back to the hypothesis that generated it and the experiment that confirmed it.
neXus began as a research platform for a new computational model SSCCS, serving simultaneously as its proof of concept. The platform and the theory describe each other by design. The infrastructure that validates the model is itself built on the model—a self‑referential loop that already makes neXus a general‑purpose, open‑source FIH problem‑solving infrastructure for any data-driven domain.
Every platform dependency is a vulnerability. API changes, price increases, and service shutdowns are vectors of disarmament against your autonomy. A single state machine makes any storage backend interchangeable, turning platform lock-in into a tactical choice rather than an architectural constraint. Fact is immutable, Intent is a state machine, Hint is volatile – data remains readable by the same code a decade later, regardless of which platform hosted it. Platform independence is a survival strategy, and neXus is its runtime. Every interaction in neXus is expressed through three primitives, forming a recursive, self-similar cycle across all scales (agent, experiment, project, ecosystem):
- Fact: An immutable, validated observation (the output of a concluded Intent).
- Intent: A proposed exploration with a strict lifecycle:
submit→claim→heartbeat→conclude. - Hint: An injected, read-only constraint guiding admissible agent actions.
Figure 1: Fact → Intent → Fact: recursive chain at every scale.
Every Fact carries a provenance hash linking it to its originating Intent, forming a deterministic, replayable audit trail. A core rule governs scale: Observe as Fact, act as Intent. Automated observers record findings as Facts, preventing the Blackboard from cluttering with unclaimed Intents.
All participants (verification engines, editors, synthesis tools) are equal peers interacting solely via the FIH Blackboard interface. There is no privileged orchestrator layer; peers are defined by their role (what they read/write), not hierarchy.
Figure 2: Recursive Blackboard: every node can contain sub-Blackboards. FIH at every scale.
| Layer | Logical Component | Core Responsibility |
|---|---|---|
| 1 | Knowledge Graph Engine | Hybrid retrieval (vector + graph + temporal) for documents, entities, simulations, and sensor traces. |
| 2 | Artifact Ingestion Pipeline | Decoupled, engine-agnostic sync (Object Store → Sync Worker → Queue) ensuring incremental, consistent updates. |
| 3 | Agentic Research Loop | Stigmergy-based coordination. Planner, Verifier, and Generator interact solely via the FIH Blackboard. |
| 4 | Learning Loop | On-policy RL (Flow-GRPO) optimizing the Planner using knowledge-graph support, novelty, and reproducibility rewards. |
| 5 | Contract Governance | On-chain, self-executing protocol defining evidence thresholds, research economy rules, and staking mechanisms. |
FIH primitives form a 3-vector basis for the system state, enabling three scaling modes:
- Multi-Blackboard Composition: Recursive scoping. An Observation at dimension N becomes a Hint at dimension N-1.
- Temporal Accumulation: A 4D spatiotemporal graph. Facts are permanent, Intents are transient (leaving a Fact residue), and Hints are garbage-collectible, all timestamped.
- Independent Streaming: Each primitive operates as an independent pub/sub stream, allowing distributed nexus instances to synchronize via FIH deltas without a shared database.
- Layer 1 (KG Engine): Decomposes artifacts into typed relationships and community clusters, supporting naive, local, global, and hybrid retrieval strategies.
- Layer 2 (Ingestion): Abstracts storage backends (SQLite, blockchain, cloud DBs) behind a minimal interface. Custom storage can be injected without modifying core logic.
- Layer 3 (Agentic Loop): Agents coordinate via Stigmergy (leaving/reading traces). Detectors (gap/contradiction finders) apply count-based heuristics, recording findings as idempotent Facts. All actions append to an Evolving Memory for conflict-free concurrency and RL replay.
- Layer 4 (Learning Loop): Rollouts are batched for group-normalized advantages. The Planner is updated via clipped objectives with KL penalty, converting multi-turn credit assignment into single-turn updates.
- Layer 5 (Governance): Recognizes five economic contributions (Gap Discovery, Hypothesis Submission, Experimental Validation, Concept Drift Detection, Knowledge Ingestion). Validated hypotheses return stake + reward; falsified ones are slashed.
Fundamental computing research requires validation beyond text and code. neXus extends into a cross-reality research manifold, unifying theoretical insights, simulation outputs, and physical measurements.
The Universal Latent Homeomorphic Manifold (ULHM) framework uses homeomorphism (continuous bijection preserving topological structure) to unify disparate modalities. The Verifier applies three canonical loss terms to validate cross-domain mappings:
- Continuity loss: Small changes in one modality map to small changes in the other.
- Trust loss: Preserves neighborhood relationships.
- Wasserstein loss: Aligns global distributions of latent representations.
| Layer | Current Scope | Extended Scope |
|---|---|---|
| KG Engine | Documents, code, references | Simulation outputs, robot trajectories, sensor streams, digital twin states |
| Ingestion Pipeline | Text files (.md, .rs, etc.) |
Binary simulation results, point clouds, telemetry, hardware-in-the-loop data |
| Agentic Loop | Document-code gap hypotheses | Hypotheses spanning simulation predictions and physical measurements |
| Learning Loop | Research session outcomes | Experimental validation rates, simulation fidelity, physical reproducibility |
| Contract Governance | Structural/citation rules | Physical constraints, precision bounds, safety invariants |
- Episodic Knowledge Graph (eKG): Evolving Memory transitions from append-only JSONL to an eKG, preserving temporal ordering, agent provenance, and cross-modal coherence for physical reproducibility.
- Homeomorphic Bridge: Enables semantic-guided recovery (completing partial physical observations via formal descriptions) and zero-shot compositional reasoning across simulation and hardware.
- Required Additions: Multi-Modal Ingestion Handlers, a Homeomorphic Verification Layer, and eKG Integration.
| Component | KG Engine | Object Store | Sync Worker | Planner | Verifier | Generator | Sim / Hardware |
|---|---|---|---|---|---|---|---|
| KG Engine | ● | ← synced by | ← queried by | ← grounds | ← ingests traces | ||
| Object Store | ● | ← read during diff | ← uploaded by | ||||
| Sync Worker | → del/upd | → list/read | ● | ||||
| Planner | → queries | ● | → delegates | → invokes | |||
| Verifier | → hybrid + homeomorphic | ← receives | ● | → signals | ← validates | ||
| Generator | ← triggered | ● |
- Engine-Agnostic: Synchronization endpoints isolate the system from specific backends, enabling seamless adoption of future KG, simulation, or robotic algorithms.
- Zero Lock-in: All components (object store, queue, KG database, simulation engine) are replaceable with open equivalents.
- Research-First: Optimized strictly for the academic exploration cycle (hypothesize → validate → publish) across digital and physical domains.
- Boundaryless by Design: Physical-digital extension is a natural consequence of existing engine-agnostic patterns, requiring no fundamental architectural rewrite.
- Invisible Infrastructure: nex does not seek attention. Its success is measured by the success of every instance built upon it — a verification engine becoming an industry standard, a development environment powering the next generation of tools, a hardware design flow quietly automating synthesis. Each is a standalone project with its own identity, and simultaneously an implicit nex extension instance. The core runtime remains deliberately unremarkable, like stage equipment that enables the performance without ever stepping into the light.