337 lines
13 KiB
Markdown
337 lines
13 KiB
Markdown
# ADR-016: RuVector Integration for Training Pipeline
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## Status
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Accepted
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## Context
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The `wifi-densepose-train` crate (ADR-015) was initially implemented using
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standard crates (`petgraph`, `ndarray`, custom signal processing). The ruvector
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ecosystem provides published Rust crates with subpolynomial algorithms that
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directly replace several components with superior implementations.
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All ruvector crates are published at v2.0.4 on crates.io (confirmed) and their
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source is available at https://github.com/ruvnet/ruvector.
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### Available ruvector crates (all at v2.0.4, published on crates.io)
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| Crate | Description | Default Features |
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|-------|-------------|-----------------|
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| `ruvector-mincut` | World's first subpolynomial dynamic min-cut | `exact`, `approximate` |
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| `ruvector-attn-mincut` | Min-cut gating attention (graph-based alternative to softmax) | all modules |
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| `ruvector-attention` | Geometric, graph, and sparse attention mechanisms | all modules |
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| `ruvector-temporal-tensor` | Temporal tensor compression with tiered quantization | all modules |
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| `ruvector-solver` | Sublinear-time sparse linear solvers O(log n) to O(√n) | `neumann`, `cg`, `forward-push` |
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| `ruvector-core` | HNSW-indexed vector database core | v2.0.5 |
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| `ruvector-math` | Optimal transport, information geometry | v2.0.4 |
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### Verified API Details (from source inspection of github.com/ruvnet/ruvector)
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#### ruvector-mincut
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```rust
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use ruvector_mincut::{MinCutBuilder, DynamicMinCut, MinCutResult, VertexId, Weight};
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// Build a dynamic min-cut structure
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let mut mincut = MinCutBuilder::new()
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.exact() // or .approximate(0.1)
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.with_edges(vec![(u: VertexId, v: VertexId, w: Weight)]) // (u32, u32, f64) tuples
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.build()
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.expect("Failed to build");
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// Subpolynomial O(n^{o(1)}) amortized dynamic updates
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mincut.insert_edge(u, v, weight) -> Result<f64> // new cut value
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mincut.delete_edge(u, v) -> Result<f64> // new cut value
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// Queries
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mincut.min_cut_value() -> f64
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mincut.min_cut() -> MinCutResult // includes partition
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mincut.partition() -> (Vec<VertexId>, Vec<VertexId>) // S and T sets
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mincut.cut_edges() -> Vec<Edge> // edges crossing the cut
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// Note: VertexId = u64 (not u32); Edge has fields { source: u64, target: u64, weight: f64 }
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```
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`MinCutResult` contains:
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- `value: f64` — minimum cut weight
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- `is_exact: bool`
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- `approximation_ratio: f64`
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- `partition: Option<(Vec<VertexId>, Vec<VertexId>)>` — S and T node sets
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#### ruvector-attn-mincut
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```rust
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use ruvector_attn_mincut::{attn_mincut, attn_softmax, AttentionOutput, MinCutConfig};
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// Min-cut gated attention (drop-in for softmax attention)
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// Q, K, V are all flat &[f32] with shape [seq_len, d]
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let output: AttentionOutput = attn_mincut(
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q: &[f32], // queries: flat [seq_len * d]
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k: &[f32], // keys: flat [seq_len * d]
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v: &[f32], // values: flat [seq_len * d]
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d: usize, // feature dimension
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seq_len: usize, // number of tokens / antenna paths
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lambda: f32, // min-cut threshold (larger = more pruning)
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tau: usize, // temporal hysteresis window
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eps: f32, // numerical epsilon
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) -> AttentionOutput;
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// AttentionOutput
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pub struct AttentionOutput {
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pub output: Vec<f32>, // attended values [seq_len * d]
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pub gating: GatingResult, // which edges were kept/pruned
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}
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// Baseline softmax attention for comparison
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let output: Vec<f32> = attn_softmax(q, k, v, d, seq_len);
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```
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**Use case in wifi-densepose-train**: In `ModalityTranslator`, treat the
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`T * n_tx * n_rx` antenna×time paths as `seq_len` tokens and the `n_sc`
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subcarriers as feature dimension `d`. Apply `attn_mincut` to gate irrelevant
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antenna-pair correlations before passing to FC layers.
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#### ruvector-solver (NeumannSolver)
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```rust
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use ruvector_solver::neumann::NeumannSolver;
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use ruvector_solver::types::CsrMatrix;
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use ruvector_solver::traits::SolverEngine;
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// Build sparse matrix from COO entries
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let matrix = CsrMatrix::<f32>::from_coo(rows, cols, vec![
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(row: usize, col: usize, val: f32), ...
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]);
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// Solve Ax = b in O(√n) for sparse systems
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let solver = NeumannSolver::new(tolerance: f64, max_iterations: usize);
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let result = solver.solve(&matrix, rhs: &[f32]) -> Result<SolverResult, SolverError>;
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// SolverResult
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result.solution: Vec<f32> // solution vector x
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result.residual_norm: f64 // ||b - Ax||
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result.iterations: usize // number of iterations used
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```
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**Use case in wifi-densepose-train**: In `subcarrier.rs`, model the 114→56
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subcarrier resampling as a sparse regularized least-squares problem `A·x ≈ b`
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where `A` is a sparse basis-function matrix (physically motivated by multipath
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propagation model: each target subcarrier is a sparse combination of adjacent
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source subcarriers). Gives O(√n) vs O(n) for n=114 subcarriers.
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#### ruvector-temporal-tensor
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```rust
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use ruvector_temporal_tensor::{TemporalTensorCompressor, TierPolicy};
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use ruvector_temporal_tensor::segment;
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// Create compressor for `element_count` f32 elements per frame
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let mut comp = TemporalTensorCompressor::new(
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TierPolicy::default(), // configures hot/warm/cold thresholds
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element_count: usize, // n_tx * n_rx * n_sc (elements per CSI frame)
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id: u64, // tensor identity (0 for amplitude, 1 for phase)
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);
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// Mark access recency (drives tier selection):
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// hot = accessed within last few timestamps → 8-bit (~4x compression)
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// warm = moderately recent → 5 or 7-bit (~4.6–6.4x)
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// cold = rarely accessed → 3-bit (~10.67x)
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comp.set_access(timestamp: u64, tensor_id: u64);
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// Compress frames into a byte segment
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let mut segment_buf: Vec<u8> = Vec::new();
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comp.push_frame(frame: &[f32], timestamp: u64, &mut segment_buf);
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comp.flush(&mut segment_buf); // flush current partial segment
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// Decompress
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let mut decoded: Vec<f32> = Vec::new();
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segment::decode(&segment_buf, &mut decoded); // all frames
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segment::decode_single_frame(&segment_buf, frame_index: usize) -> Option<Vec<f32>>;
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segment::compression_ratio(&segment_buf) -> f64;
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```
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**Use case in wifi-densepose-train**: In `dataset.rs`, buffer CSI frames in
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`TemporalTensorCompressor` to reduce memory footprint by 50–75%. The CSI window
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contains `window_frames` (default 100) frames per sample; hot frames (recent)
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stay at f32 fidelity, cold frames (older) are aggressively quantized.
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#### ruvector-attention
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```rust
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use ruvector_attention::{
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attention::ScaledDotProductAttention,
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traits::Attention,
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};
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let attention = ScaledDotProductAttention::new(d: usize); // feature dim
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// Compute attention: q is [d], keys and values are Vec<&[f32]>
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let output: Vec<f32> = attention.compute(
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query: &[f32], // [d]
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keys: &[&[f32]], // n_nodes × [d]
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values: &[&[f32]], // n_nodes × [d]
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) -> Result<Vec<f32>>;
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```
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**Use case in wifi-densepose-train**: In `model.rs` spatial decoder, replace the
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standard Conv2D upsampling pass with graph-based spatial attention among spatial
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locations, where nodes represent spatial grid points and edges connect neighboring
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antenna footprints.
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---
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## Decision
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Integrate ruvector crates into `wifi-densepose-train` at five integration points:
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### 1. `ruvector-mincut` → `metrics.rs` (replaces petgraph Hungarian for multi-frame)
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**Before:** O(n³) Kuhn-Munkres via DFS augmenting paths using `petgraph::DiGraph`,
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single-frame only (no state across frames).
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**After:** `DynamicPersonMatcher` struct wrapping `ruvector_mincut::DynamicMinCut`.
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Maintains the bipartite assignment graph across frames using subpolynomial updates:
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- `insert_edge(pred_id, gt_id, oks_cost)` when new person detected
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- `delete_edge(pred_id, gt_id)` when person leaves scene
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- `partition()` returns S/T split → `cut_edges()` returns the matched pred→gt pairs
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**Performance:** O(n^{1.5} log n) amortized update vs O(n³) rebuild per frame.
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Critical for >3 person scenarios and video tracking (frame-to-frame updates).
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The original `hungarian_assignment` function is **kept** for single-frame static
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matching (used in proof verification for determinism).
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### 2. `ruvector-attn-mincut` → `model.rs` (replaces flat MLP fusion in ModalityTranslator)
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**Before:** Amplitude/phase FC encoders → concatenate [B, 512] → fuse Linear → ReLU.
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**After:** Treat the `n_ant = T * n_tx * n_rx` antenna×time paths as `seq_len`
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tokens and `n_sc` subcarriers as feature dimension `d`. Apply `attn_mincut` to
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gate irrelevant antenna-pair correlations:
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```rust
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// In ModalityTranslator::forward_t:
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// amp/ph tensors: [B, n_ant, n_sc] → convert to Vec<f32>
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// Apply attn_mincut with seq_len=n_ant, d=n_sc, lambda=0.3
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// → attended output [B, n_ant, n_sc] → flatten → FC layers
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```
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**Benefit:** Automatic antenna-path selection without explicit learned masks;
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min-cut gating is more computationally principled than learned gates.
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### 3. `ruvector-temporal-tensor` → `dataset.rs` (CSI temporal compression)
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**Before:** Raw CSI windows stored as full f32 `Array4<f32>` in memory.
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**After:** `CompressedCsiBuffer` struct backed by `TemporalTensorCompressor`.
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Tiered quantization based on frame access recency:
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- Hot frames (last 10): f32 equivalent (8-bit quant ≈ 4× smaller than f32)
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- Warm frames (11–50): 5/7-bit quantization
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- Cold frames (>50): 3-bit (10.67× smaller)
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Encode on `push_frame`, decode on `get(idx)` for transparent access.
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**Benefit:** 50–75% memory reduction for the default 100-frame temporal window;
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allows 2–4× larger batch sizes on constrained hardware.
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### 4. `ruvector-solver` → `subcarrier.rs` (phase sanitization)
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**Before:** Linear interpolation across subcarriers using precomputed (i0, i1, frac) tuples.
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**After:** `NeumannSolver` for sparse regularized least-squares subcarrier
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interpolation. The CSI spectrum is modeled as a sparse combination of Fourier
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basis functions (physically motivated by multipath propagation):
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```rust
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// A = sparse basis matrix [target_sc, src_sc] (Gaussian or sinc basis)
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// b = source CSI values [src_sc]
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// Solve: A·x ≈ b via NeumannSolver(tolerance=1e-5, max_iter=500)
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// x = interpolated values at target subcarrier positions
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```
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**Benefit:** O(√n) vs O(n) for n=114 source subcarriers; more accurate at
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subcarrier boundaries than linear interpolation.
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### 5. `ruvector-attention` → `model.rs` (spatial decoder)
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**Before:** Standard ConvTranspose2D upsampling in `KeypointHead` and `DensePoseHead`.
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**After:** `ScaledDotProductAttention` applied to spatial feature nodes.
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Each spatial location [H×W] becomes a token; attention captures long-range
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spatial dependencies between antenna footprint regions:
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```rust
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// feature map: [B, C, H, W] → flatten to [B, H*W, C]
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// For each batch: compute attention among H*W spatial nodes
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// → reshape back to [B, C, H, W]
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```
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**Benefit:** Captures long-range spatial dependencies missed by local convolutions;
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important for multi-person scenarios.
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---
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## Implementation Plan
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### Files modified
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| File | Change |
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|------|--------|
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| `Cargo.toml` (workspace + crate) | Add ruvector-mincut, ruvector-attn-mincut, ruvector-temporal-tensor, ruvector-solver, ruvector-attention = "2.0.4" |
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| `metrics.rs` | Add `DynamicPersonMatcher` wrapping `ruvector_mincut::DynamicMinCut`; keep `hungarian_assignment` for deterministic proof |
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| `model.rs` | Add `attn_mincut` bridge in `ModalityTranslator::forward_t`; add `ScaledDotProductAttention` in spatial heads |
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| `dataset.rs` | Add `CompressedCsiBuffer` backed by `TemporalTensorCompressor`; `MmFiDataset` uses it |
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| `subcarrier.rs` | Add `interpolate_subcarriers_sparse` using `NeumannSolver`; keep `interpolate_subcarriers` as fallback |
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### Files unchanged
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`config.rs`, `losses.rs`, `trainer.rs`, `proof.rs`, `error.rs` — no change needed.
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### Feature gating
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All ruvector integrations are **always-on** (not feature-gated). The ruvector
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crates are pure Rust with no C FFI, so they add no platform constraints.
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---
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## Implementation Status
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| Phase | Status |
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|-------|--------|
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| Cargo.toml (workspace + crate) | **Complete** |
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| ADR-016 documentation | **Complete** |
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| ruvector-mincut in metrics.rs | **Complete** |
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| ruvector-attn-mincut in model.rs | **Complete** |
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| ruvector-temporal-tensor in dataset.rs | **Complete** |
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| ruvector-solver in subcarrier.rs | **Complete** |
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| ruvector-attention in model.rs spatial decoder | **Complete** |
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---
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## Consequences
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**Positive:**
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- Subpolynomial O(n^{1.5} log n) dynamic min-cut for multi-person tracking
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- Min-cut gated attention is physically motivated for CSI antenna arrays
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- 50–75% memory reduction from temporal quantization
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- Sparse least-squares interpolation is physically principled vs linear
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- All ruvector crates are pure Rust (no C FFI, no platform restrictions)
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**Negative:**
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- Additional compile-time dependencies (ruvector crates)
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- `attn_mincut` requires tensor↔Vec<f32> conversion overhead per batch element
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- `TemporalTensorCompressor` adds compression/decompression latency on dataset load
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- `NeumannSolver` requires diagonally dominant matrices; a sparse Tikhonov
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regularization term (λI) is added to ensure convergence
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## References
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- ADR-015: Public Dataset Training Strategy
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- ADR-014: SOTA Signal Processing Algorithms
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- github.com/ruvnet/ruvector (source: crates at v2.0.4)
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- ruvector-mincut: https://crates.io/crates/ruvector-mincut
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- ruvector-attn-mincut: https://crates.io/crates/ruvector-attn-mincut
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- ruvector-temporal-tensor: https://crates.io/crates/ruvector-temporal-tensor
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- ruvector-solver: https://crates.io/crates/ruvector-solver
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- ruvector-attention: https://crates.io/crates/ruvector-attention
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