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/**
* react/components/visualization/routing_matrix.js
* ===============================================
* Verified routing table and forbidden state invariants for all 252 catalog modules.
*/
export const ROUTING_MATRIX = {
"SOAFusedAttention": {
"id": "SOAFusedAttention",
"name": "SOA Fused Attention: Low-Rank Latent Space Multi-Head Attention w",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"adaptive_span_attention": {
"id": "adaptive_span_attention",
"name": "Adaptive Span Attention: Dynamic Head-Specific Context Window Gat",
"category": "C",
"subcategory": "sliding_window",
"states": [
"idle",
"qkv_projection",
"banded_mask",
"local_attention",
"output"
],
"substates": [
"dynamic_span_visible"
],
"defaultState": "banded_mask",
"forbidden": [
"global_cross_attention",
"svd_singular_values",
"recurrent_hidden_state"
]
},
"adaptive_span_inject": {
"id": "adaptive_span_inject",
"name": "Adaptive Span Attention: Dynamic Head-Specific Context Window Gat",
"category": "C",
"subcategory": "sliding_window",
"states": [
"idle",
"qkv_projection",
"banded_mask",
"local_attention",
"output"
],
"substates": [
"dynamic_span_visible"
],
"defaultState": "banded_mask",
"forbidden": [
"global_cross_attention",
"svd_singular_values",
"recurrent_hidden_state"
]
},
"adaptive_sparse_attention": {
"id": "adaptive_sparse_attention",
"name": "Adaptive Sparse Attention: Dynamic Top-K & Threshold-Based Sparsi",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"energy_threshold_visible"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"adaptive_sparse_inject": {
"id": "adaptive_sparse_inject",
"name": "Adaptive Sparse Attention: Dynamic Top-K & Threshold-Based Sparsi",
"category": "B",
"subcategory": "sparse_approximate",
"states": [
"idle",
"feature_map",
"associative_reorder",
"output"
],
"substates": [],
"defaultState": "feature_map",
"forbidden": [
"dense_attention_matrix",
"full_softmax_matrix",
"quadratic_square"
]
},
"adaptive_svd_attention": {
"id": "adaptive_svd_attention",
"name": "Adaptive SVD Attention: Low-Rank SVD Projections with Parallel Re",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"energy_threshold_visible"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"adasplash_attention": {
"id": "adasplash_attention",
"name": "AdaSplash Attention: Adaptive Splash Attention with alpha-Entmax ",
"category": "C",
"subcategory": "sliding_window",
"states": [
"idle",
"qkv_projection",
"banded_mask",
"local_attention",
"output"
],
"substates": [
"local_window_mask_visible"
],
"defaultState": "banded_mask",
"forbidden": [
"global_cross_attention",
"svd_singular_values",
"recurrent_hidden_state"
]
},
"adasplash_inject": {
"id": "adasplash_inject",
"name": "AdaSplash Attention: Adaptive Splash Attention with alpha-Entmax ",
"category": "B",
"subcategory": "sparse_approximate",
"states": [
"idle",
"feature_map",
"associative_reorder",
"output"
],
"substates": [],
"defaultState": "feature_map",
"forbidden": [
"dense_attention_matrix",
"full_softmax_matrix",
"quadratic_square"
]
},
"aegis_hybrid": {
"id": "aegis_hybrid",
"name": "AEGIS-Hybrid Attention: Low-Rank Factorized Hybrid Attention with",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"aegis_hybrid_attention": {
"id": "aegis_hybrid_attention",
"name": "Compatibility shim for aegis_hybrid_attention.",
"category": "B",
"subcategory": "sparse_approximate",
"states": [
"idle",
"feature_map",
"associative_reorder",
"output"
],
"substates": [],
"defaultState": "feature_map",
"forbidden": [
"dense_attention_matrix",
"full_softmax_matrix",
"quadratic_square"
]
},
"aegis_spectron_fp8": {
"id": "aegis_spectron_fp8",
"name": "AEGIS-Spectron-FP8: Tri-Branch Hybrid Attention with Token-Level ",
"category": "H",
"subcategory": "quantization",
"states": [
"idle",
"calibration",
"scale_calc",
"quantize_int8_fp8",
"dequantize",
"output"
],
"substates": [
"dynamic_scale_visible"
],
"defaultState": "quantize_int8_fp8",
"forbidden": [
"svd_singular_values",
"state_space_scan",
"quadratic_square"
]
},
"aegis_v1": {
"id": "aegis_v1",
"name": "AEGIS-Attention V1: Adaptive Enhanced Global-Local Integrated Spa",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"aegis_v2": {
"id": "aegis_v2",
"name": "AEGIS-Attention V2: Low-Rank Factorized Hybrid Attention with Ada",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"aegis_v3": {
"id": "aegis_v3",
"name": "AEGIS-Attention V3: Adaptive Enhanced Global-Local Integrated Spa",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"aegis_v4": {
"id": "aegis_v4",
"name": "AEGIS-Attention V4: Adaptive Enhanced Global-Local Integrated Spa",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"aegis_v5": {
"id": "aegis_v5",
"name": "AEGIS-Attention V5: Adaptive Enhanced Global-Local Integrated Spa",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"aegis_v6": {
"id": "aegis_v6",
"name": "AEGIS-Attention V6: Unified SVD-LoRA Hybrid Attention with Orthog",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"asvd_attention": {
"id": "asvd_attention",
"name": "ASVD Attention: Activation-Aware Singular Value Decomposition Arc",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"bam_umoe": {
"id": "bam_umoe",
"name": "BAM-uMoE: Bidirectional Attentive Mixture-of-Unified-Experts",
"category": "F",
"subcategory": "mixture_of_experts",
"states": [
"idle",
"router_gate",
"expert_dispatch",
"expert_compute",
"expert_combine",
"output"
],
"substates": [
"routing_probabilities_visible"
],
"defaultState": "router_gate",
"forbidden": [
"svd_singular_values",
"state_space_scan",
"quadratic_square"
]
},
"block_transformer_hiba": {
"id": "block_transformer_hiba",
"name": "Block Transformer with Hierarchical Intra-Block and Inter-Block A",
"category": "A",
"subcategory": "dense_attention",
"states": [
"idle",
"qkv_projection",
"score_matrix",
"softmax",
"context",
"output"
],
"substates": [],
"defaultState": "qkv_projection",
"forbidden": [
"state_space_scan",
"svd_singular_values",
"recurrent_hidden_state"
]
},
"chela_slay": {
"id": "chela_slay",
"name": "CHELA-SLAY: Spherical Linearized Attention with Yat-Kernel.",
"category": "B",
"subcategory": "sparse_approximate",
"states": [
"idle",
"feature_map",
"associative_reorder",
"output"
],
"substates": [],
"defaultState": "feature_map",
"forbidden": [
"dense_attention_matrix",
"full_softmax_matrix",
"quadratic_square"
]
},
"cluster_attention": {
"id": "cluster_attention",
"name": "ClusterFormer: Dynamic Token-Clustering Attention via Batch Itera",
"category": "B",
"subcategory": "sparse_approximate",
"states": [
"idle",
"feature_map",
"associative_reorder",
"output"
],
"substates": [],
"defaultState": "feature_map",
"forbidden": [
"dense_attention_matrix",
"full_softmax_matrix",
"quadratic_square"
]
},
"cluster_inject": {
"id": "cluster_inject",
"name": "ClusterFormer: Dynamic Token-Clustering Attention via Batch Itera",
"category": "B",
"subcategory": "sparse_approximate",
"states": [
"idle",
"feature_map",
"associative_reorder",
"output"
],
"substates": [],
"defaultState": "feature_map",
"forbidden": [
"dense_attention_matrix",
"full_softmax_matrix",
"quadratic_square"
]
},
"compressed_attention": {
"id": "compressed_attention",
"name": "Compressed Multi-Head Attention via Truncated Singular Value Deco",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"compressed_mha": {
"id": "compressed_mha",
"name": "Compressed Multi-Head Attention (Compressed-MHA)",
"category": "B",
"subcategory": "sparse_approximate",
"states": [
"idle",
"feature_map",
"associative_reorder",
"output"
],
"substates": [],
"defaultState": "feature_map",
"forbidden": [
"dense_attention_matrix",
"full_softmax_matrix",
"quadratic_square"
]
},
"compressed_mha_attention": {
"id": "compressed_mha_attention",
"name": "Compressed Multi-Head Attention for DistilBERT and Multi-Architec",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"cosformer": {
"id": "cosformer",
"name": "Cosformer: Rethinking Softmax in Attention with Linear Complexity",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"cosformer_inject": {
"id": "cosformer_inject",
"name": "Deprecated stub. Re-exports base module for registry compatibilit",
"category": "A",
"subcategory": "dense_attention",
"states": [
"idle",
"qkv_projection",
"score_matrix",
"softmax",
"context",
"output"
],
"substates": [],
"defaultState": "qkv_projection",
"forbidden": [
"state_space_scan",
"svd_singular_values",
"recurrent_hidden_state"
]
},
"decay_linear_attention": {
"id": "decay_linear_attention",
"name": "Decay Linear Attention (DecayLinearAttention).",
"category": "D",
"subcategory": "recurrent_linear",
"states": [
"idle",
"t0",
"t1",
"t2",
"t3",
"t4",
"output"
],
"substates": [],
"defaultState": "t1",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"square_attention",
"dense_score_matrix"
]
},
"eat": {
"id": "eat",
"name": "EAT: Efficient Attention Transformer with Progressive Token Pruni",
"category": "G",
"subcategory": "pruning_sparsity",
"states": [
"idle",
"importance_eval",
"mask_generation",
"pruned_projection",
"output"
],
"substates": [
"threshold_cutoff_visible"
],
"defaultState": "importance_eval",
"forbidden": [
"dense_attention_matrix",
"svd_singular_values",
"softmax"
]
},
"expert_choice_moe": {
"id": "expert_choice_moe",
"name": "Expert Choice Mixture of Experts (EC-MoE) Attention Block",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"factorised_matrix_state": {
"id": "factorised_matrix_state",
"name": "Factorised Matrix State Attention: Bidirectional Associative Matr",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"flash_attention3": {
"id": "flash_attention3",
"name": "FlashAttention-3: Fast and Memory-Efficient Exact Attention with ",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"flash_attention3_fp8": {
"id": "flash_attention3_fp8",
"name": "FlashAttention-3 FP8: Low-Precision 8-Bit Float Transformer Atten",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"full_attention": {
"id": "full_attention",
"name": "Full Attention: Standard Multi-Head Scaled Dot-Product Attention ",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"fused_svd_kernel": {
"id": "fused_svd_kernel",
"name": "Fused SVD-Linear Layer with SRAM-Resident RMSNorm & Dual-Projecti",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"fusion_attn": {
"id": "fusion_attn",
"name": "FUSIONATTN: Multi-Paradigm Hybrid Sparse Attention Module.",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"galerkin": {
"id": "galerkin",
"name": "Petrov-Galerkin Projection Attention: Energy-Preserving Spectral ",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"galerkin_attention": {
"id": "galerkin_attention",
"name": "Petrov-Galerkin Projection Attention: Energy-Preserving Spectral ",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"galerkin_inject": {
"id": "galerkin_inject",
"name": "Petrov-Galerkin Projection Attention: Energy-Preserving Spectral ",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
]
},
"gated_delta_net": {
"id": "gated_delta_net",
"name": "Gated DeltaNet: Multi-Head Bidirectional Delta Rule Linear Recurr",
"category": "D",
"subcategory": "recurrent_linear",
"states": [
"idle",
"t0",
"t1",
"t2",
"t3",
"t4",
"output"
],
"substates": [
"delta_rule_visible"
],
"defaultState": "t1",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"square_attention",
"dense_score_matrix"
]
},
"gated_deltanet_block": {
"id": "gated_deltanet_block",
"name": "Gated DeltaNet Block: Hybrid Multi-Head Attention and Recurrent D",
"category": "D",
"subcategory": "recurrent_linear",
"states": [
"idle",
"t0",
"t1",
"t2",
"t3",
"t4",
"output"
],
"substates": [
"delta_rule_visible"
],
"defaultState": "t1",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"square_attention",
"dense_score_matrix"
]
},
"gated_linear_attention": {
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"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"no_weighting"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
],
"targetId": "asvd_attention"
},
"adaptive_svd": {
"id": "adaptive_svd",
"name": "Adaptive SVD Attention: Low-Rank SVD Projections with Parallel Re",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"energy_threshold_visible"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
],
"targetId": "adaptive_svd_attention"
},
"adasvd": {
"id": "adasvd",
"name": "AdaSVDCompressor: Adaptive SVD via Nuclear-Norm Layer Importance ",
"category": "E",
"subcategory": "svd_low_rank",
"states": [
"idle",
"show_W",
"decompose",
"singular_values",
"truncate",
"reconstruct",
"error_heatmap"
],
"substates": [
"energy_threshold_visible"
],
"defaultState": "decompose",
"forbidden": [
"qkv",
"softmax",
"attention_matrix",
"causal_mask",
"square_attention"
],
"targetId": "adasvd_compressor"
}
};
/**
* Validate that a module state is legal and never violates forbidden constraints.
* Throws an Error if a forbidden state or undeclared state is rendered.
*/
export function validateModuleState(moduleId, state, substate = null) {
const config = ROUTING_MATRIX[moduleId];
if (!config) {
throw new Error(`Unknown module "${moduleId}" in ROUTING_MATRIX`);
}
// Check forbidden states
if (config.forbidden && config.forbidden.includes(state)) {
throw new Error(`FORBIDDEN STATE VIOLATION: Module "${moduleId}" (Category ${config.category}) strictly forbids state "${state}"!`);
}
// Check substate against forbidden
if (substate && config.forbidden && config.forbidden.includes(substate)) {
throw new Error(`FORBIDDEN SUBSTATE VIOLATION: Module "${moduleId}" (Category ${config.category}) strictly forbids substate "${substate}"!`);
}
// Category D strict invariant: NO QKV, Softmax, Attention Matrix, Dense Score Matrix
if (config.category === "D") {
const forbiddenPatterns = ["qkv", "softmax", "attention_matrix", "square_attention", "dense_score_matrix"];
if (forbiddenPatterns.some(p => (state || "").toLowerCase().includes(p))) {
throw new Error(`CATEGORY D FORBIDDEN: Recurrent module "${moduleId}" attempted to render S×S attention/softmax state "${state}"!`);
}
}
// Category E strict invariant: NO QKV, Softmax, Attention Matrix, Causal Mask
if (config.category === "E") {
const forbiddenPatterns = ["qkv", "softmax", "attention_matrix", "causal_mask"];
if (forbiddenPatterns.some(p => (state || "").toLowerCase().includes(p))) {
throw new Error(`CATEGORY E FORBIDDEN: SVD module "${moduleId}" attempted to render attention/softmax state "${state}"!`);
}
}
// Category B strict invariant: NO Dense Attention Matrix, Full Softmax Matrix
if (config.category === "B") {
const forbiddenPatterns = ["dense_attention_matrix", "full_softmax_matrix", "quadratic_square"];
if (forbiddenPatterns.some(p => (state || "").toLowerCase().includes(p))) {
throw new Error(`CATEGORY B FORBIDDEN: Sparse module "${moduleId}" attempted to render dense quadratic matrix "${state}"!`);
}
}
if (state && !config.states.includes(state)) {
throw new Error(`Invalid state "${state}" for module "${moduleId}". Valid states: ${config.states.join(", ")}`);
}
return true;
}
export function getModuleRouting(moduleId) {
return ROUTING_MATRIX[moduleId] || null;
}
export default ROUTING_MATRIX;

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