Shared memory and context tools for agentic work.
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<main>
<h1 id="graph-engine-m1nd-core"><a class="header" href="#graph-engine-m1nd-core">Graph Engine (m1nd-core)</a></h1>
<p>m1nd-core is the computational core of the graph runtime. It owns the graph data structure, the activation and analysis engines, the plasticity system, and the type-safe numeric primitives that prevent NaN/Inf corruption system-wide.</p>
<p>Source: <code>mcp/m1nd/m1nd-core/src/</code></p>
<h2 id="type-system"><a class="header" href="#type-system">Type System</a></h2>
<h3 id="numeric-primitives"><a class="header" href="#numeric-primitives">Numeric Primitives</a></h3>
<p>Every floating-point value in m1nd flows through one of four newtype wrappers defined in <code>types.rs</code>:</p>
<div class="table-wrapper">
<table>
<thead>
<tr><th>Type</th><th>Invariant</th><th>Use</th></tr>
</thead>
<tbody>
<tr><td><code>FiniteF32</code></td><td>Never NaN or Inf</td><td>All activation scores, edge weights, scores</td></tr>
<tr><td><code>PosF32</code></td><td>Strictly positive, finite</td><td>Wavelength, frequency, half-life, decay rate, threshold</td></tr>
<tr><td><code>LearningRate</code></td><td>(0.0, 1.0]</td><td>Plasticity learning rate</td></tr>
<tr><td><code>DecayFactor</code></td><td>(0.0, 1.0]</td><td>Signal decay per hop</td></tr>
</tbody>
</table>
<p><code>FiniteF32</code> is the foundation. In debug builds, constructing one from a non-finite value panics. In release builds, it clamps to 0.0. Because NaN is excluded by construction, <code>FiniteF32</code> implements <code>Ord</code>, <code>Eq</code>, and <code>Hash</code> – all unsound on raw <code>f32</code>:</p>
<pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
</span><span class="boring">fn main() {
</span>impl Ord for FiniteF32 {
fn cmp(&self, other: &Self) -> std::cmp::Ordering {
self.0.total_cmp(&other.0)
<span class="boring">}</span></code></pre>
<h3 id="index-types"><a class="header" href="#index-types">Index Types</a></h3>
<p>Thin <code>#[repr(transparent)]</code> wrappers over <code>u32</code> provide type-safe indexing:</p>
<tr><th>Type</th><th>Wraps</th><th>Purpose</th></tr>
<tr><td><code>NodeId(u32)</code></td><td>Node index</td><td>Index into <code>NodeStorage</code> parallel arrays</td></tr>
<tr><td><code>EdgeIdx(u32)</code></td><td>Edge index</td><td>Index into CSR parallel arrays</td></tr>
<tr><td><code>InternedStr(u32)</code></td><td>String handle</td><td>Opaque index into <code>StringInterner</code></td></tr>
<tr><td><code>CommunityId(u32)</code></td><td>Community</td><td>Louvain community membership</td></tr>
<tr><td><code>Generation(u64)</code></td><td>Mutation counter</td><td>Plasticity engine desync detection</td></tr>
<h3 id="node-and-edge-classification"><a class="header" href="#node-and-edge-classification">Node and Edge Classification</a></h3>
</span>#[repr(u8)]
pub enum NodeType {
File = 0, Directory = 1, Function = 2, Class = 3,
Struct = 4, Enum = 5, Type = 6, Module = 7,
Reference = 8, Concept = 9, Material = 10, Process = 11,
Product = 12, Supplier = 13, Regulatory = 14, System = 15,
Cost = 16, Custom(u8),
#[repr(u8)]
pub enum EdgeDirection {
Forward = 0,
Bidirectional = 1,
<p>Variants 0-8 are code-domain types. Variants 9-16 support non-code domains (supply chain, manufacturing). <code>Custom(u8)</code> is the extension point for future domains.</p>
<h3 id="dimension-system"><a class="header" href="#dimension-system">Dimension System</a></h3>
<p>Activation operates across four dimensions with fixed weights:</p>
</span>pub enum Dimension {
Structural = 0, // Graph topology (BFS/heap propagation)
Semantic = 1, // Text similarity (trigram TF-IDF + co-occurrence)
Temporal = 2, // Time-based (decay + velocity)
Causal = 3, // Causal chains (forward/backward with discount)
pub const DIMENSION_WEIGHTS: [f32; 4] = [0.35, 0.25, 0.15, 0.25];
<p>When fewer than 4 dimensions contribute to a result, weights are adaptively redistributed. Results that fire across multiple dimensions receive a resonance bonus: 1.5x for all 4, 1.3x for 3.</p>
<h2 id="property-graph-model"><a class="header" href="#property-graph-model">Property Graph Model</a></h2>
<h3 id="string-interner"><a class="header" href="#string-interner">String Interner</a></h3>
<p>All strings pass through <code>StringInterner</code> before entering the graph. The interner maps strings to <code>InternedStr(u32)</code> handles via a <code>HashMap<String, InternedStr></code> and resolves handles back via a <code>Vec<String></code> indexed by the u32 value.</p>
<p>Once interned, all string comparisons become integer comparisons – zero-allocation, single CPU cycle.</p>
</span>pub struct StringInterner {
strings: Vec<String>,
index: HashMap<String, InternedStr>,
<h3 id="csr-graph"><a class="header" href="#csr-graph">CSR Graph</a></h3>
<p>The graph uses Compressed Sparse Row (CSR) format with both forward and reverse adjacency. For node <code>i</code>, outgoing edges span <code>offsets[i]..offsets[i+1]</code> into parallel arrays for targets, weights, inhibitory flags, relations, directions, and causal strengths.</p>
</span>pub struct CsrGraph {
// Forward CSR
pub offsets: Vec<u64>, // num_nodes + 1
pub targets: Vec<NodeId>, // total_edges
pub weights: Vec<AtomicU32>, // bit-reinterpreted f32 for lock-free CAS
pub inhibitory: Vec<bool>, // edge polarity
pub relations: Vec<InternedStr>, // edge type (interned)
pub directions: Vec<EdgeDirection>, // forward or bidirectional
pub causal_strengths: Vec<FiniteF32>,
// Reverse CSR (built at finalize)
pub rev_offsets: Vec<u64>,
pub rev_sources: Vec<NodeId>,
pub rev_edge_idx: Vec<EdgeIdx>, // maps back to forward arrays
// Pre-finalize staging
pub pending_edges: Vec<PendingEdge>,
<p>The CSR is immutable after finalization. Edges are added to <code>pending_edges</code> during graph building, then sorted by source and compacted into the CSR arrays by <code>Graph.finalize()</code>. Bidirectional edges are expanded into two forward entries during finalization.</p>
<h3 id="atomic-weight-updates"><a class="header" href="#atomic-weight-updates">Atomic Weight Updates</a></h3>
<p>Edge weights are stored as <code>AtomicU32</code> rather than <code>f32</code>. The plasticity engine updates weights concurrently with read queries using Compare-And-Swap:</p>
</span>pub fn atomic_max_weight(
&self, edge: EdgeIdx, new_val: FiniteF32, max_retries: u32,
) -> M1ndResult<()> {
let slot = &self.weights[edge.as_usize()];
let new_bits = new_val.get().to_bits();
for _ in 0..max_retries {
let old_bits = slot.load(Ordering::Relaxed);
let old_val = f32::from_bits(old_bits);
if old_val >= new_val.get() { return Ok(()); }
if slot.compare_exchange_weak(
old_bits, new_bits, Ordering::Release, Ordering::Relaxed
).is_ok() { return Ok(()); }
Err(M1ndError::CasRetryExhausted { edge, limit: max_retries })
<p>Two CAS operations are provided: <code>atomic_max_weight</code> (only increases, for activation scatter-max) and <code>atomic_write_weight</code> (unconditional, for plasticity). Both retry up to 64 times (constant <code>CAS_RETRY_LIMIT</code>).</p>
<h3 id="node-storage-soa"><a class="header" href="#node-storage-soa">Node Storage (SoA)</a></h3>
<p>All per-node data lives in <code>NodeStorage</code>, organized as Struct-of-Arrays with explicit cache-path separation:</p>
</span>pub struct NodeStorage {
pub count: u32,
// Hot path: activation engine reads every query
pub activation: Vec<[FiniteF32; 4]>, // [structural, semantic, temporal, causal]
pub pagerank: Vec<FiniteF32>,
// Warm path: plasticity reads per query
pub plasticity: Vec<PlasticityNode>,
// Cold path: seed finding, display, export
pub label: Vec<InternedStr>,
pub node_type: Vec<NodeType>,
pub tags: Vec<SmallVec<[InternedStr; 6]>>,
pub last_modified: Vec<f64>,
pub change_frequency: Vec<FiniteF32>,
pub provenance: Vec<NodeProvenance>,
<p>The <code>SmallVec<[InternedStr; 6]></code> for tags avoids heap allocation for nodes with 6 or fewer tags (the common case), while still supporting arbitrary tag counts.</p>
<h3 id="node-provenance"><a class="header" href="#node-provenance">Node Provenance</a></h3>
<p>Each node carries source metadata for tracing back to the original code:</p>
</span>pub struct NodeProvenance {
pub source_path: Option<InternedStr>,
pub line_start: u32,
pub line_end: u32,
pub excerpt: Option<InternedStr>,
pub namespace: Option<InternedStr>,
pub canonical: bool,
<h3 id="generation-counter"><a class="header" href="#generation-counter">Generation Counter</a></h3>
<p><code>Generation(u64)</code> tracks graph mutations. The plasticity engine stores the generation at initialization. Every plasticity operation asserts that the current graph generation matches; a mismatch (from concurrent ingestion) causes a controlled rebuild rather than operating on stale state.</p>
<h2 id="activation-propagation"><a class="header" href="#activation-propagation">Activation Propagation</a></h2>
<h3 id="engine-selection-hybridengine"><a class="header" href="#engine-selection-hybridengine">Engine Selection (HybridEngine)</a></h3>
<p>The <code>HybridEngine</code> auto-selects between two propagation strategies based on graph characteristics:</p>
<pre><code class="language-mermaid">flowchart TD
START["HybridEngine.propagate()"]
CHECK{"seed_ratio < 0.001<br/>AND avg_degree < 8?"}
HEAP["HeapEngine<br/>(priority queue)"]
WAVE["WavefrontEngine<br/>(BFS depth-parallel)"]
MERGE["Collect sparse results"]
START --> CHECK
CHECK -->|yes| HEAP
CHECK -->|no| WAVE
HEAP --> MERGE
WAVE --> MERGE
</code></pre>
<ul>
<li><strong>HeapEngine</strong>: Max-heap priority queue. Processes strongest signal first. Early-terminates when the heap top drops below threshold. Uses a double-hashing <code>BloomFilter</code> for O(1) amortized visited checks. Best for sparse queries with few seeds in low-degree graphs.</li>
<li><strong>WavefrontEngine</strong>: BFS depth-parallel. All active nodes at current depth fire simultaneously. Signal accumulated via scatter-max into next depth’s buffer. Best for dense queries or high-degree graphs.</li>
<h3 id="structural-propagation-d1"><a class="header" href="#structural-propagation-d1">Structural Propagation (D1)</a></h3>
<p>The wavefront engine is the reference implementation. Signal propagates depth by depth:</p>
<ol>
<li>Seed nodes initialized with their scores (capped at <code>saturation_cap</code>).</li>
<li>For each depth (up to <code>max_depth=5</code>, hard cap 20):
<li>Each frontier node <code>src</code> with activation above <code>threshold=0.04</code> fires.</li>
<li>For each outgoing edge: <code>signal = src_activation * weight * decay(0.55)</code>.</li>
<li>Inhibitory edges: <code>signal = -signal * inhibitory_factor(0.5)</code>, subtracted from target (floored at 0).</li>
<li>Excitatory edges: scatter-max into target (keep strongest arrival).</li>
</li>
<li>Collect all nodes with non-zero activation, sorted descending.</li>
</ol>
<h3 id="semantic-dimension-d2"><a class="header" href="#semantic-dimension-d2">Semantic Dimension (D2)</a></h3>
<p>Two indexes power semantic matching:</p>
<p><strong>CharNgramIndex</strong>: FNV-1a 24-bit trigram hashing over node labels with TF-IDF weighting. An inverted index maps trigram hashes to node lists, enabling O(K) query time (K = number of matching trigrams) instead of O(N) full scan.</p>
<p><strong>CoOccurrenceIndex</strong>: DeepWalk-lite random walks (20 walks/node, length 10, window 4) generate co-occurrence counts, normalized to Positive Pointwise Mutual Information (PPMI). Sorted vectors enable O(D) merge-intersection for similarity queries. Disabled above 50K nodes to bound walk cost.</p>
<p>The semantic engine also includes a <code>SynonymExpander</code> with 15 default groups covering code terminology and Portuguese domain terms.</p>
<p>Query pipeline: Phase 1 ngram candidates (3x top_k) -> Phase 2 multi-seed co-occurrence re-rank.</p>
<h3 id="temporal-dimension-d3"><a class="header" href="#temporal-dimension-d3">Temporal Dimension (D3)</a></h3>
<p>Three scorers combine:</p>
<li><strong>TemporalDecayScorer</strong>: Per-NodeType half-lives (File=7 days, Function=14 days, Module/Directory=30 days). Formula: <code>exp(-ln(2) * age_hours / half_life)</code>. Dormant nodes (>35 days) get a resurrection bonus with an additive floor.</li>
<li><strong>VelocityScorer</strong>: Z-score based change velocity. Nodes changing faster than average receive higher scores.</li>
<li><strong>CoChangeMatrix</strong>: Sparse matrix (budget: 500K entries, 100 per row) bootstrapped from BFS depth 3, refined with git co-change observations.</li>
<h3 id="causal-dimension-d4"><a class="header" href="#causal-dimension-d4">Causal Dimension (D4)</a></h3>
<p><code>CausalChainDetector</code>: Budget-limited priority-queue DFS along <code>causal_strength</code> edges. Forward propagation follows contains/imports/calls edges; backward propagation reverses direction. Discount factor 0.7 per hop. Chain depth capped at 6 by default.</p>
<h3 id="dimension-merging"><a class="header" href="#dimension-merging">Dimension Merging</a></h3>
<p><code>merge_dimensions()</code> combines all four dimension results:</p>
<li>For each activated node, compute weighted sum: <code>score = sum(dim_score * DIMENSION_WEIGHTS[dim])</code>.</li>
<li>If a dimension produced no results, redistribute its weight proportionally to active dimensions.</li>
<li>Apply resonance bonus: 1.5x if all 4 dimensions contributed, 1.3x if 3 contributed.</li>
<li>Sort by final score, truncate to top_k.</li>
<h3 id="seed-finding"><a class="header" href="#seed-finding">Seed Finding</a></h3>
<p><code>SeedFinder</code> resolves query strings to graph nodes via a 5-level matching cascade:</p>
<li><strong>Exact label match</strong> (highest priority)</li>
<li><strong>Prefix match</strong> (e.g., “chat_” matches “chat_handler”)</li>
<li><strong>Substring match</strong> (e.g., “handler” matches “chat_handler”)</li>
<li><strong>Tag match</strong> (e.g., “#api” tag)</li>
<li><strong>Fuzzy trigram</strong> (cosine similarity of trigram vectors, lowest priority)</li>
<p>A semantic re-ranking phase (0.6 basic score / 0.4 semantic blend) refines results when multiple candidates match.</p>
<h2 id="weight-systems"><a class="header" href="#weight-systems">Weight Systems</a></h2>
<h3 id="hebbian-plasticity"><a class="header" href="#hebbian-plasticity">Hebbian Plasticity</a></h3>
<p>The <code>PlasticityEngine</code> implements biological Hebbian learning with 5 phases executed after every activation query:</p>
<pre><code class="language-mermaid">flowchart LR
A["1. Hebbian Strengthen<br/>delta_w = lr * act_src * act_tgt"]
B["2. Synaptic Decay<br/>w *= (1 - decay_rate) for inactive"]
C["3. LTP/LTD<br/>permanent bonus/penalty<br/>after N consecutive"]
D["4. Homeostatic Normalize<br/>scale if total > ceiling"]
E["5. Record Query<br/>ring buffer + bigrams"]
A --> B --> C --> D --> E
<p><strong>Constants</strong> (from <code>plasticity.rs</code>):</p>
<tr><th>Parameter</th><th>Value</th><th>Purpose</th></tr>
<tr><td><code>DEFAULT_LEARNING_RATE</code></td><td>0.08</td><td>Hebbian weight change rate</td></tr>
<tr><td><code>DEFAULT_DECAY_RATE</code></td><td>0.005</td><td>Inactive synapse decay per query</td></tr>
<tr><td><code>LTP_THRESHOLD</code></td><td>5</td><td>Consecutive strengthens before permanent bonus</td></tr>
<tr><td><code>LTD_THRESHOLD</code></td><td>5</td><td>Consecutive weakens before permanent penalty</td></tr>
<tr><td><code>LTP_BONUS</code></td><td>0.15</td><td>Permanent weight increase</td></tr>
<tr><td><code>LTD_PENALTY</code></td><td>0.15</td><td>Permanent weight decrease</td></tr>
<tr><td><code>HOMEOSTATIC_CEILING</code></td><td>5.0</td><td>Max sum of incoming weights per node</td></tr>
<tr><td><code>WEIGHT_FLOOR</code></td><td>0.05</td><td>Minimum edge weight (prevents extinction)</td></tr>
<tr><td><code>WEIGHT_CAP</code></td><td>3.0</td><td>Maximum edge weight</td></tr>
<p><strong>Hebbian update</strong>: For each edge where both source and target were activated: <code>delta_w = learning_rate * activation_source * activation_target</code>. Applied via atomic CAS (<code>atomic_write_weight</code>).</p>
<p><strong>Homeostatic normalization</strong>: If the sum of incoming weights for any node exceeds <code>HOMEOSTATIC_CEILING</code>, all incoming weights are scaled proportionally to bring the total back under the ceiling. This prevents runaway positive feedback.</p>
<p><strong>Query Memory</strong>: A ring buffer of 1000 entries tracks recent queries. Each record stores the query text, seed nodes, activated nodes, and timestamp. The memory tracks:</p>
<li>Node frequency: how often each node appears across recent queries.</li>
<li>Seed bigrams: co-occurring seed pairs, used for priming signals.</li>
<p>When the buffer wraps, evicted records have their frequency and bigram counts decremented – maintaining accurate sliding-window statistics.</p>
<p><strong>Persistence</strong>: Plasticity state (per-edge <code>SynapticState</code>) is exported as JSON using triple-based identity matching (source_label, target_label, relation). This allows plasticity to survive graph rebuilds as long as the semantic structure remains similar.</p>
<h3 id="xlr-differential-processing"><a class="header" href="#xlr-differential-processing">XLR Differential Processing</a></h3>
<p>XLR (eXcitatory-Lateral-inhibitory Response) is a spectral noise cancellation system that separates signal from noise in activation results.</p>
<p><strong>Constants</strong>:</p>
<tr><td><code>F_HOT</code></td><td>1.0</td><td>Hot signal frequency</td></tr>
<tr><td><code>F_COLD</code></td><td>3.7</td><td>Cold signal frequency</td></tr>
<tr><td><code>SPECTRAL_BANDWIDTH</code></td><td>0.8</td><td>Gaussian kernel bandwidth</td></tr>
<tr><td><code>IMMUNITY_HOPS</code></td><td>2</td><td>BFS immunity radius</td></tr>
<tr><td><code>SIGMOID_STEEPNESS</code></td><td>6.0</td><td>Gating function steepness</td></tr>
<p><strong>6-step pipeline</strong>:</p>
<li><strong>Anti-seed selection</strong>: Pick nodes dissimilar to seeds (Jaccard similarity < 0.2, degree ratio filter).</li>
<li><strong>Immunity computation</strong>: BFS 2 hops from seeds. Immune nodes cannot be suppressed.</li>
<li><strong>Hot propagation</strong>: Spread <code>SpectralPulse</code> with frequency <code>F_HOT=1.0</code> from seed nodes. Pulses carry amplitude, phase, frequency, and a bounded recent path (<code>[NodeId; 3]</code>, not unbounded Vec – FM-RES-007).</li>
<li><strong>Cold propagation</strong>: Spread from anti-seeds with frequency <code>F_COLD=3.7</code>.</li>
<li><strong>Spectral overlap + density modulation</strong>: Compute overlap between hot and cold spectra using Gaussian kernel (<code>bw=0.8</code>). Dense neighborhoods get modulated (clamped to <code>[0.3, 2.0]</code>).</li>
<li><strong>Sigmoid gating</strong>: Apply <code>1 / (1 + exp(-steepness * (hot - cold)))</code> to produce final scores.</li>
<p><strong>Over-cancellation fallback</strong> (FM-XLR-010): If all results score zero after gating (cold dominates everywhere), fall back to hot-only results. This prevents total signal erasure in adversarial topologies.</p>
<h2 id="standing-wave-resonance"><a class="header" href="#standing-wave-resonance">Standing Wave Resonance</a></h2>
<p>The resonance engine discovers structural harmonics by propagating wave pulses through the graph:</p>
</span>pub struct WavePulse {
pub node: NodeId,
pub amplitude: FiniteF32, // can be negative for destructive interference
pub phase: FiniteF32, // [0, 2*pi)
pub frequency: PosF32, // MUST be positive (FM-RES-002)
pub wavelength: PosF32, // MUST be positive (FM-RES-001)
pub hops: u8,
pub prev_node: NodeId,
<p><strong>Wave interference</strong>: Pulses accumulate at each node as complex numbers (real + imaginary). Constructive interference occurs when pulses arrive in phase; destructive when out of phase. The <code>WaveAccumulator</code> tracks <code>sum(amplitude * cos(phase))</code> and <code>sum(amplitude * sin(phase))</code>, with resultant amplitude <code>sqrt(real^2 + imag^2)</code>.</p>
<p><strong>Reflection rules</strong>:</p>
<li><strong>Dead-end reflection</strong>: At leaf nodes (degree 1), pulse reflects with a pi phase shift (<code>REFLECTION_PHASE_SHIFT</code>).</li>
<li><strong>Hub partial reflection</strong>: At hub nodes (degree > 4x average), 30% of amplitude reflects (<code>HUB_REFLECTION_COEFF</code>).</li>
<p><strong>Budget</strong>: <code>DEFAULT_PULSE_BUDGET = 50,000</code> prevents combinatorial explosion in dense subgraphs. Pulses are processed in BFS order via a <code>VecDeque</code>.</p>
<p><strong>Harmonic analysis</strong>: <code>HarmonicAnalyzer</code> sweeps across frequencies, groups nodes by harmonic response, and identifies resonant frequencies (local maxima in the sweep). <code>SympatheticResonanceDetector</code> finds nodes that resonate across regions (outside 2-hop seed neighborhood) – indicating structural coupling without direct edges.</p>
<h2 id="pagerank"><a class="header" href="#pagerank">PageRank</a></h2>
<p>Computed once at <code>Graph.finalize()</code> via power iteration:</p>
<li>Damping factor: 0.85</li>
<li>Max iterations: 50</li>
<li>Convergence threshold: 1e-6</li>
<p>PageRank scores are stored in <code>NodeStorage.pagerank</code> and used as a static importance signal in seed finding and result ranking.</p>
<h2 id="persistence"><a class="header" href="#persistence">Persistence</a></h2>
<h3 id="graph-snapshot"><a class="header" href="#graph-snapshot">Graph Snapshot</a></h3>
<p><code>snapshot.rs</code> serializes the graph to JSON format (version 3):</p>
</span>struct GraphSnapshot {
version: u32, // SNAPSHOT_VERSION = 3
nodes: Vec<NodeSnapshot>,
edges: Vec<EdgeSnapshot>,
<p>Each <code>NodeSnapshot</code> includes external_id, label, node_type, tags, timestamps, and optional provenance. Each <code>EdgeSnapshot</code> includes source/target IDs, relation, weight, direction, inhibitory flag, and causal strength.</p>
<p><strong>Atomic write</strong> (FM-PL-008): Write to a temporary file, then <code>rename()</code> over the target. The rename is atomic on POSIX filesystems, so a crash mid-write leaves the previous snapshot intact.</p>
<p><strong>NaN firewall</strong>: All values are checked for finiteness at the export boundary. Non-finite values are rejected before writing.</p>
<h3 id="plasticity-state"><a class="header" href="#plasticity-state">Plasticity State</a></h3>
<p>Per-edge <code>SynapticState</code> (original weight, current weight, strengthen/weaken counts, LTP/LTD flags) is serialized to a separate JSON file. Import uses triple-based matching (source_label, target_label, relation) to reconnect state to edges after graph rebuilds.</p>
<h3 id="auto-persist"><a class="header" href="#auto-persist">Auto-Persist</a></h3>
<p>The MCP server triggers persistence every <code>auto_persist_interval</code> queries (default: 50). Ordering: graph first (source of truth), then plasticity. If graph save fails, plasticity save is skipped to prevent inconsistent state.</p>
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