Dweve Loom System Card
Technical specification for Dweve Loom: A sparse mixture-of-experts system with 456 production expert modules (528 total with 72 in training), built primarily on binary constraint networks with a small neural coherence validator. Complete transparency in capabilities, architecture, limitations, and safety.
System Overview
Loom is a sparse mixture-of-experts system built on binary constraint satisfaction rather than traditional neural networks. Every inference is deterministic, traceable, and delivers a 25× energy efficiency gain over floating-point implementations.
Active Development: 72 New Experts in Training
Loom is actively expanding with 72 additional expert modules currently undergoing the 57-layer training pipeline. These new experts will bring the total catalog to 528 modules, expanding coverage across engineering, social sciences, humanities, applied domains, and AI governance. Training timelines vary significantly by domain complexity and available constraint specifications.
Training Progress: All 72 experts proceeding through the 57-layer evolutionary pipeline. Upon completion, Loom will offer the most comprehensive constraint-based expert coverage across academic, professional, and applied domains.
Binary-Probabilistic Computing Substrate
Loom performs all inference using binary constraints. No floating-point arithmetic occurs during inference. Only bitwise operations (XNOR, AND, OR, popcount) and constraint satisfaction solving. This architectural choice provides determinism, auditability, and 25× better energy efficiency than floating-point operations.
Stochastic Computing
- 1024-bit stochastic bitstreams encode probability distributions
- Population coding: value = popcount(bitstream) / 1024
- XNOR + popcount implements probabilistic inference operations
- Three parallel LFSRs generate phase-differential encoding
Constraint Networks
- Finite constraint sets (1024 bytes each) replace parameter spaces
- MaxSAT and Bit-DP solvers for constraint satisfaction (with timeout handling for intractable cases)
- Complete provenance: every constraint activation traceable
- Built on Dweve Core: 1,930 hardware-optimized algorithms
Architecture Deep Dive
Four-Level Constraint Hierarchy
BitTrue, Hamming, PAP (Permuted Agreement Popcount), XOR, Provenance tracking primitives
Logical combinations (AND, OR, NOT) of atomic constraints forming patterns
Task-specific sets for reasoning, language, code, mathematics, vision
Expert selection, routing logic, ensemble voting, cross-expert coordination
Expert Organization: 10 Specialized Clusters
456 expert modules organized into 10 functional clusters. Each expert contains constraint sets (2-3.5 million constraints), gate subsets (50-200 filters), hypervector signatures (65,536-dimensional), and explicit failure modes.
Sparse Expert Routing: Sublinear Selection
Achieves O(log N) expert selection through three-tier filtering hierarchy using PAP (Permuted Agreement Popcount) similarity metric, a binary Hamming-like measure detecting structural patterns in bit agreement.
- • HNSW: O(log N) hierarchical navigation
- • LSH: 32 hash tables with cosine similarity
- • Inverted Bit Index: Roaring bitmap compression
- • Result: Candidate pool of ~50 experts
- • 50-200 rapid binary filters per expert
- • Required gates: must all pass
- • Optional gates: weighted scoring
- • Result: ~10-15 qualified experts
- • 2-3.5M constraints per expert
- • Early termination on satisfaction
- • Negative evidence immediate fail
- • Result: Top 4-8 experts activated (up to 20 for complex queries)
57-Layer Evolutionary Training
Loom's 456 experts are produced through a 57-layer evolutionary pipeline that integrates multiple optimization strategies: population initialization, energy-based search, structural refinement, parallel exploration, adaptive randomness, network stabilization, and final consolidation across seven defined phases.
Initialization (Layers 1–8)
Generates diverse initial constraint populations through entropy-guided seeding and diversity injection, maximizing search-space coverage for evolutionary optimization.
Energy-Based Search (Layers 9–16)
Uses simulated annealing with controlled temperature schedules to minimize system energy. Randomness decreases gradually, allowing the model to escape local minima while moving toward global optima.
Structural Refinement (Layers 17–28)
Optimizes the internal topology of constraint networks. Beneficial structural patterns are reinforced, while redundant or unstable links are pruned to improve efficiency and coherence.
Parallel Exploration (Layers 29–40)
Conducts simultaneous evaluation of multiple candidate solutions through probabilistic search. Maintains high population diversity and cross-validation among constraint sets.
Exploration Control (Layers 41–48)
Regulates the balance between randomness and convergence. Adaptive noise modulation keeps exploration active without destabilizing progress toward optimal configurations.
Network Stabilization (Layers 49–54)
Establishes consistent constraint interactions through dependency analysis and feedback correction. Ensures the formation of coherent, stable expert networks.
Final Consolidation (Layers 55–57)
Finalizes expert specialization through regularization and controlled variance retention, producing 456 robust and generalizable modules.
Key Insight: This multi-phase approach sustains exploration while preserving structural coherence. Each phase targets a distinct optimization trade-off: exploration vs. exploitation, local vs. global search, structure vs. efficiency, and adaptability vs. stability.
Why 57 Layers Stays Fast
A 57-layer pipeline sounds like a performance disaster. In Loom, it's not because most layers never execute, and those that do run on hardware-native binary operations.
Binary Operations = Hardware Speed
Every layer runs on bit-level primitives: XNOR, AND, OR, popcount. These map directly to single CPU instructions with SIMD vectorization (AVX-512 processes 512 bits in one cycle). No floating-point multipliers, no matrix operations dragging through memory hierarchies.
Early Termination Everywhere
Loom's constraint hierarchy enables aggressive early termination. Atomic constraints fail fast (single bit test). Composite constraints short-circuit on first failure. Domain constraints only evaluate if gates pass. Most queries terminate after 4-10 layers, not 57.
Sparse Expert Routing
Only 4-8 of 456 experts activate per query (98.2% idle). PAP similarity routing is O(log N), so adding more experts or deeper layers doesn't linearly increase latency. HNSW index narrows to ~50 candidates, gates filter to ~15, full evaluation picks top-K.
Stochastic Computing Efficiency
Continuous values become 1024-bit streams where multiplication = AND, addition = XOR. Operations that require floating-point units and take 10-100 cycles collapse into single-cycle bitwise ops. LFSR generation is deterministic and parallelizable.
Example Inference Paths
Scenario 1: Simple Factual Query
- • Layers 1-3: Atomic constraints (bit tests)
- • Layer 12: Composite validation
- • Expert: Knowledge Retrieval (1 of 456)
- • Remaining 53 layers: skipped
Scenario 2: Mathematical Reasoning
- • Layers 1-4: Atomic (symbol parsing)
- • Layers 18-20: Composite (equation structure)
- • Layer 29: Domain (Algebraic Manipulator)
- • Experts: 2 active (Algebra + Verification)
Scenario 3: Cross-Domain Analogy
- • Layers 1-5: Atomic feature extraction
- • Layers 17-22: Structural (domain bridging)
- • Layers 35-38: Parallel search (analogical)
- • Experts: 5 active (Biology, Analogical, Meta)
Key Insight: The 57 layers are not a sequential pipeline but a training framework that generates specialized experts. During inference, sparse routing and early termination mean the effective depth is 4-15 layers, not 57. Each active layer costs nanoseconds, not milliseconds.
Capabilities & Limitations
Core Strengths
- Structured reasoning: Logic, planning, constraint satisfaction, formal verification, proof systems
- Code generation & analysis: Program synthesis, type checking, optimization, debugging, constraint propagation
- Deterministic systems: Regulatory compliance, safety-critical applications, complete auditability
- Energy efficiency: Edge devices, battery-powered systems, sustainable AI deployment
- Ensemble reasoning: Multi-expert voting, perspective aggregation, uncertainty quantification
Known Limitations
- Constraint bootstrapping: Initial constraint specification requires human domain experts or external learning systems to seed knowledge; the system cannot bootstrap expertise in completely novel domains without prior constraints
- Precision bounds: 1024-bit stochastic computing provides ~0.1% precision; applications requiring ultra-high-precision continuous function approximation (beyond 64-bit float equivalence) need traditional floating-point systems
- Hybrid architecture for creative tasks: Creative generation uses a neural network (20.4M parameters: 20M embedding model + 394K classifier) for coherence validation. Constraint networks excel at discrete logical reasoning; neural networks handle continuous aesthetic judgments. This hybrid approach combines the strengths of both architectures.
Safety & Transparency
Complete Decision Provenance
Every Loom inference is fully traceable: which experts activated, what constraints triggered, which solver paths executed, how the answer was derived. Unlike neural networks with billions of opaque parameters, Loom enables comprehensive auditing for regulatory compliance and accountability.
Deterministic Reproducibility
100% reproducibility: identical input always produces identical output, bit-for-bit. No temperature sampling, no stochastic dropout, no non-determinism. Critical for safety applications (medical, financial, industrial) where unpredictable behavior is unacceptable.
Explicit Failure Documentation
Each expert documents failure modes: UNSAT (constraint unsatisfiability), timeout, ambiguous inputs, out-of-distribution patterns. Rather than hallucinating confidently, Loom reports when it encounters scenarios outside its training distribution. Honesty about limitations builds trust.
Sustainable AI
Binary operations require ~4% of floating-point energy. No GPU clusters, no industrial cooling, no massive electricity consumption. Runs on standard CPUs with air cooling, enabling deployment in energy-constrained environments.
Privacy by Design
On-premise deployment with no external data transfer. GDPR Article 25 compliance built-in.
Audit Trails
Constraint-level decision logs. Every activation, evaluation, and output fully documented.
Certified Determinism
Mathematical guarantee of reproducibility. Essential for regulated industries.
Technical Specifications
System Architecture
Core Operations
Storage & Memory
Implementation
Rust with SIMD-optimized binary operations (x86: SSE2, AVX2, AVX-512 | ARM: NEON, SVE2), zero-copy tensor sharing, lock-free concurrent constraint solving. Built on Dweve Core (1,930 algorithms). Includes native modality adapters (text, image, audio, continuous values), creative generation system (Meta-Expert Conductor with 20.4M-parameter neural coherence validator), and continuous optimization engine (hybrid binary-gradient methods).
Training Data
Data Sources & Composition
Constraint Sources
- Human expert-specified domain constraints (10-50 foundational rules per domain)
- Automated constraint discovery through genetic programming on validation sets
- Pattern mining from successful inference traces
- Transfer learning from related domains
Training Modalities
- Text: Multi-domain corpora for language understanding and code generation
- Structured data: Databases, knowledge graphs, formal specifications
- Visual: Image datasets for object recognition and scene understanding
- Audio: Speech and sound classification datasets
Training Methodology
Loom's training differs from gradient-based neural networks. Instead of optimizing billions of floating-point parameters, the system generates discrete constraint sets through a 57-layer pipeline combining multiple optimization strategies. Each expert is trained independently, then validated through shadow deployment before production integration.
Intended Use Cases
Primary Applications
- Safety-Critical Systems: Medical diagnosis, financial compliance, industrial control where deterministic, auditable decisions are mandatory
- Code Generation & Analysis: Program synthesis, type checking, optimization, debugging with constraint propagation
- Structured Reasoning: Logic puzzles, planning, constraint satisfaction, formal verification
- Edge Deployment: Battery-powered edge devices (8GB+ RAM), sustainable AI where energy efficiency is critical (10-1000× less energy than neural networks)
Out-of-Scope Use Cases
- Domains Without Prior Constraints: Loom requires initial constraint specification; cannot bootstrap expertise in completely novel domains from scratch
- Ultra-High-Precision Numerics: Applications requiring precision beyond ~0.1% (1024-bit stochastic computing limit) need traditional floating-point
- Real-Time Guarantees: While fast, inference time varies with query complexity; not suitable for hard real-time systems with microsecond deadlines
Example Applications
Medical AI
Diagnostic reasoning with complete audit trails for regulatory compliance. Every diagnosis traceable through explicit constraint activations.
Edge Intelligence
Edge devices and distributed systems running constraint-based inference on battery power with minimal energy consumption.
Software Development
Automated code review, bug detection, optimization suggestions with explainable reasoning about code quality.
Safety & Ethical Considerations
Adversarial Robustness
Loom's constraint-based architecture provides inherent resistance to certain attack vectors:
- No gradient attacks: Binary operations eliminate gradient-based adversarial examples
- Explicit constraints: Violations of domain rules detectable through constraint checking
- Deterministic behavior: Attacks cannot exploit stochastic sampling variations
- Constraint manipulation: Adversaries targeting constraint specification process remain a concern
Bias & Fairness
Bias analysis differs from neural networks due to constraint-based reasoning:
- Auditable decisions: Every decision traceable to specific constraints, enabling bias identification
- Constraint review: Domain experts can review and modify problematic constraints directly
- Expert specification bias: Human-specified constraints may encode societal biases
- Data distribution bias: Automatically-discovered constraints reflect training data biases
Privacy & Data Protection
Built-in Privacy
- • On-premise deployment: No data leaves customer infrastructure
- • Deterministic execution: No telemetry required for debugging
- • Minimal data retention: Only constraint activations logged
- • GDPR Article 25 compliance: Privacy by design and default
Data Handling
- • Input data processed locally, never transmitted externally
- • Constraint logs contain decision traces, not raw data
- • User control over retention policies and audit trails
- • Compatible with air-gapped and high-security environments
Responsible Deployment Practices
We recommend the following practices for responsible Loom deployment:
- Regular constraint audits by domain experts to identify and remove problematic rules
- Testing on diverse datasets to uncover bias before production deployment
- Human oversight for high-stakes decisions (medical, financial, legal applications)
- Transparent communication about system capabilities and limitations to end users
- Monitoring of system behavior in production to detect distributional shift
- Incident response procedures for constraint violations or unexpected behavior
Deployment Flexibility
Scalable Architecture
Loom supports flexible deployment from resource-constrained edge devices to large-scale cloud infrastructure. Expert catalog size scales with available memory, from lightweight specialized deployments to comprehensive full catalogs.
Edge Devices
Optimized expert subsets for edge devices (8GB+ RAM) with efficient power usage
Enterprise Servers
Comprehensive expert catalogs for on-premise deployment with full auditability and data sovereignty
Cloud Infrastructure
Massive-scale deployments with distributed expert routing and high-throughput parallel inference
System Requirements
Hardware
- • CPU with SIMD support (x86: SSE2 minimum, AVX-512 optimal | ARM: NEON, SVE2)
- • Memory scales with expert catalog size (configurable based on deployment needs)
- • GPU acceleration supported but not required (CPU-only operation reduces infrastructure costs)
- • Standard storage for expert persistence and constraint data
Software
- • Cross-platform: Linux, Windows, macOS support
- • Self-contained binary with embedded Rust runtime
- • Multiple API interfaces: REST, gRPC, WebSocket
- • Client SDKs: Python, JavaScript, Rust, Go
Documentation & Resources
Comprehensive technical documentation, integration guides, and developer resources