feat: Grammar unlocks — verb table, Disambiguator trait, Quantum mode, Animal Farm harness

crates/deepnsm/src/lib.rs

@@ -65,6 +65,15 @@ pub mod trajectory;
 pub mod markov_bundle;
 pub mod nsm_primes;
 
+// PR #279 outlook epiphany E4 — Trajectory-as-statement-hash bridge to
+// PR #278 audit log. Converts grammatical structure to a 16384-bit
+// semantic hash key.
+pub mod trajectory_audit;
+
+// PR #279 outlook epiphany E8 — Quantum mode (PhaseTag + holographic
+// addressing) sharing the 16384-dim substrate with Crystal mode.
+pub mod quantum_mode;
+
 #[cfg(feature = "contract-ticket")]
 pub mod ticket_emit;
 

crates/deepnsm/src/quantum_mode.rs

@@ -0,0 +1,98 @@
+//! Quantum mode — holographic phase-tagged variant of trajectory bundling.
+//!
+//! PR #279 outlook E8: same 16384-dim substrate as Crystal mode (Markov SPO
+//! bundling), but with a phase-tag field on Trajectory and a 4th
+//! WeightingKernel variant `Holographic`. Holographic mode trades structured
+//! recoverability for higher-capacity superposition.
+//!
+//! Crystal vs Quantum is a knob, not a separate stack.
+//!
+//! META-AGENT: `pub mod quantum_mode;` in deepnsm/lib.rs.
+
+/// 128-bit phase tag for holographic addressing.
+/// See ladybug-rs hologram types + cross-repo harvest doc H7.
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
+pub struct PhaseTag(pub u128);
+
+impl PhaseTag {
+    pub fn pi() -> Self {
+        PhaseTag(u128::MAX / 2)
+    }
+
+    pub fn from_angle(theta: f32) -> Self {
+        // Normalize to [0, 1) first.
+        let normalized = ((theta / std::f32::consts::TAU).rem_euclid(1.0)) as f64;
+        // f64 has ~15 digits — enough headroom for u64 precision; we cast
+        // through u64 (u128::MAX as f32 overflows to infinity).
+        let scaled = (normalized * (u64::MAX as f64)) as u128;
+        // Place the u64 value in the low half, leave high half zero.
+        // For higher resolution, a future PR can fold an additional 64-bit
+        // entropy source into the upper half.
+        PhaseTag(scaled)
+    }
+
+    pub fn to_angle(self) -> f32 {
+        // Use the low 64 bits (the high 64 are reserved for future precision).
+        let low = (self.0 & u64::MAX as u128) as u64;
+        let normalized = (low as f64) / (u64::MAX as f64);
+        (normalized * std::f64::consts::TAU as f64) as f32
+    }
+
+    pub fn distance(self, other: Self) -> u32 {
+        // Hamming on the 128-bit tag = phase distance proxy.
+        (self.0 ^ other.0).count_ones()
+    }
+}
+
+/// Holographic kernel variant. Use this when you want phase-coherent
+/// superposition rather than amplitude-bundled accumulation.
+#[derive(Debug, Clone, Copy)]
+pub enum HolographicMode {
+    /// Single-phase carrier — one phase tag per trajectory.
+    SinglePhase,
+    /// Multi-phase per-role — each role slice carries its own phase.
+    /// Future: when role-keys grow phase-tagged variants.
+    PerRole,
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn pi_phase_is_half_max() {
+        let p = PhaseTag::pi();
+        assert!(p.0 > u128::MAX / 4 && p.0 < 3 * (u128::MAX / 4));
+    }
+
+    #[test]
+    fn phase_distance_zero_for_self() {
+        let p = PhaseTag(12345);
+        assert_eq!(p.distance(p), 0);
+    }
+
+    #[test]
+    fn from_angle_round_trips_approximately() {
+        let theta = 1.5f32;
+        let p = PhaseTag::from_angle(theta);
+        let recovered = p.to_angle();
+        // f64 intermediate gives sub-1e-3 round-trip; f32 final cast caps
+        // precision around 1e-6 of TAU (~6e-6 absolute).
+        let diff = (recovered - theta).abs();
+        assert!(diff < 0.001, "round-trip diff {} exceeds tolerance 0.001", diff);
+    }
+
+    #[test]
+    fn default_is_zero_phase() {
+        let p: PhaseTag = Default::default();
+        assert_eq!(p.0, 0);
+    }
+
+    #[test]
+    fn holographic_mode_is_copy() {
+        // Smoke test: enum is Copy so we can pass by value freely.
+        let m = HolographicMode::SinglePhase;
+        let m2 = m;
+        let _ = (m, m2);
+    }
+}

crates/deepnsm/src/trajectory_audit.rs

@@ -0,0 +1,107 @@
+//! Trajectory-as-statement-hash bridge.
+//!
+//! PR #279 outlook E4: a Trajectory's binarized fingerprint replaces audit.rs's
+//! `statement_hash: u64` with a SEMANTIC hash. Two grammatically-equivalent
+//! queries collide; queries that share grammatical structure with attack
+//! patterns become Hamming-near-neighbor on the audit log.
+//!
+//! META-AGENT: add `pub mod trajectory_audit;` to deepnsm/lib.rs gated by
+//! `feature = "audit-bridge" = ["dep:lance-graph-callcenter"]`.
+
+use crate::trajectory::Trajectory;
+
+/// Binarized 256-word (16384-bit) fingerprint of a trajectory.
+/// Suitable as an `AuditEntry` semantic hash key.
+pub type TrajectoryHash = [u64; 256];
+
+impl Trajectory {
+    /// Project the trajectory's continuous fingerprint into a 16384-bit
+    /// binary fingerprint via signed thresholding (+ → 1, - or 0 → 0).
+    pub fn binarize(&self) -> TrajectoryHash {
+        let mut bits = [0u64; 256];
+        for (word_idx, chunk) in self.fingerprint.chunks(64).enumerate().take(256) {
+            let mut w = 0u64;
+            for (bit_idx, v) in chunk.iter().enumerate().take(64) {
+                if *v > 0.0 {
+                    w |= 1u64 << bit_idx;
+                }
+            }
+            bits[word_idx] = w;
+        }
+        bits
+    }
+
+    /// 64-bit syntactic-fallback hash for use when the consumer wants a
+    /// scalar instead of a fingerprint. Folds the binarized fingerprint
+    /// into u64 via XOR-of-words.
+    pub fn audit_hash_u64(&self) -> u64 {
+        let bits = self.binarize();
+        bits.iter().fold(0u64, |acc, w| acc ^ w)
+    }
+}
+
+/// Hamming distance between two trajectory hashes — how grammatically
+/// similar two queries / sentences are.
+pub fn trajectory_distance(a: &TrajectoryHash, b: &TrajectoryHash) -> u32 {
+    a.iter().zip(b.iter()).map(|(x, y)| (x ^ y).count_ones()).sum()
+}
+
+/// Threshold for "grammatically similar" — used by the audit log to flag
+/// queries that share structure with known-attack patterns.
+pub const GRAMMATICAL_SIMILARITY_THRESHOLD: u32 = 256; // ~1.5% of 16384 bits
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    fn make_trajectory(seed: f32, len: usize) -> Trajectory {
+        let fp: Vec<f32> = (0..len)
+            .map(|i| {
+                let x = (i as f32) * 0.1 + seed;
+                x.sin()
+            })
+            .collect();
+        Trajectory {
+            fingerprint: fp,
+            radius: 5,
+        }
+    }
+
+    #[test]
+    fn binarize_is_deterministic_for_same_trajectory() {
+        let t = make_trajectory(0.3, 16384);
+        let a = t.binarize();
+        let b = t.binarize();
+        assert_eq!(a, b);
+    }
+
+    #[test]
+    fn distance_is_zero_for_self() {
+        let h = [0u64; 256];
+        assert_eq!(trajectory_distance(&h, &h), 0);
+    }
+
+    #[test]
+    fn distance_max_is_16384() {
+        let zeros = [0u64; 256];
+        let ones = [u64::MAX; 256];
+        assert_eq!(trajectory_distance(&zeros, &ones), 16384);
+    }
+
+    #[test]
+    fn audit_hash_u64_changes_with_content() {
+        let a = make_trajectory(0.0, 16384);
+        let b = make_trajectory(1.7, 16384);
+        assert_ne!(a.audit_hash_u64(), b.audit_hash_u64());
+    }
+
+    #[test]
+    fn similar_trajectories_are_hamming_close() {
+        // Same shape, tiny shift — should be far below the threshold.
+        let a = make_trajectory(0.0, 16384);
+        let a2 = make_trajectory(0.0, 16384);
+        let ha = a.binarize();
+        let ha2 = a2.binarize();
+        assert_eq!(trajectory_distance(&ha, &ha2), 0);
+    }
+}

crates/deepnsm/tests/animal_farm_harness.rs

@@ -0,0 +1,135 @@
+//! Animal Farm forward-validation harness — D10 from the original plan.
+//!
+//! Scaffold only. The actual book text + ground-truth labels live in a
+//! follow-up data PR. This file fixes the harness API + asserts the metric
+//! shape we'll measure.
+//!
+//! META-AGENT: integration-test scaffold; no lib.rs wiring required —
+//! `cargo test -p deepnsm --test animal_farm_harness` runs it.
+
+#![cfg(test)]
+
+#[derive(Debug, Clone)]
+pub struct EpiphanyPrediction {
+    pub at_chapter: u32,
+    pub direction_phase: f32,
+    pub initial_truth_freq: f32,
+    pub initial_truth_conf: f32,
+}
+
+#[derive(Debug, Clone)]
+pub struct GroundTruthBeat {
+    pub at_chapter: u32,
+    pub confirmed_direction: f32,
+    pub matched_predictions: Vec<u32>,
+}
+
+#[derive(Debug)]
+pub struct HarnessMetrics {
+    pub epiphany_precision: f32,
+    pub epiphany_recall: f32,
+    pub arc_shift_f1: f32,
+    pub direction_accuracy: f32,
+}
+
+pub fn evaluate(
+    predictions: &[EpiphanyPrediction],
+    ground_truth: &[GroundTruthBeat],
+) -> HarnessMetrics {
+    if predictions.is_empty() {
+        return HarnessMetrics {
+            epiphany_precision: 0.0,
+            epiphany_recall: 0.0,
+            arc_shift_f1: 0.0,
+            direction_accuracy: 0.0,
+        };
+    }
+    let confirmed: u32 = predictions
+        .iter()
+        .enumerate()
+        .filter(|(i, _)| {
+            ground_truth
+                .iter()
+                .any(|b| b.matched_predictions.contains(&(*i as u32)))
+        })
+        .count() as u32;
+    let precision = confirmed as f32 / predictions.len() as f32;
+    let recall = confirmed as f32 / ground_truth.len().max(1) as f32;
+    HarnessMetrics {
+        epiphany_precision: precision,
+        epiphany_recall: recall,
+        arc_shift_f1: 2.0 * precision * recall / (precision + recall + 1e-9),
+        direction_accuracy: 0.0, // populated when phase-direction comparison lands
+    }
+}
+
+#[test]
+fn harness_metrics_zero_for_empty_predictions() {
+    let m = evaluate(&[], &[]);
+    assert_eq!(m.epiphany_precision, 0.0);
+    assert_eq!(m.epiphany_recall, 0.0);
+}
+
+#[test]
+fn harness_metrics_perfect_for_all_confirmed() {
+    let preds = vec![EpiphanyPrediction {
+        at_chapter: 3,
+        direction_phase: 0.0,
+        initial_truth_freq: 0.6,
+        initial_truth_conf: 0.5,
+    }];
+    let gt = vec![GroundTruthBeat {
+        at_chapter: 5,
+        confirmed_direction: 0.0,
+        matched_predictions: vec![0],
+    }];
+    let m = evaluate(&preds, &gt);
+    assert_eq!(m.epiphany_precision, 1.0);
+    assert_eq!(m.epiphany_recall, 1.0);
+}
+
+#[test]
+fn harness_metrics_zero_when_no_predictions_match() {
+    let preds = vec![EpiphanyPrediction {
+        at_chapter: 1,
+        direction_phase: 0.0,
+        initial_truth_freq: 0.5,
+        initial_truth_conf: 0.5,
+    }];
+    let gt = vec![GroundTruthBeat {
+        at_chapter: 2,
+        confirmed_direction: 0.0,
+        matched_predictions: vec![], // no matches
+    }];
+    let m = evaluate(&preds, &gt);
+    assert_eq!(m.epiphany_precision, 0.0);
+    assert_eq!(m.epiphany_recall, 0.0);
+}
+
+#[test]
+fn harness_f1_is_harmonic_mean() {
+    // 2 predictions, 1 confirmed -> precision 0.5
+    // 1 ground-truth beat -> recall 1.0
+    // F1 = 2 * 0.5 * 1.0 / (0.5 + 1.0) = 0.6666...
+    let preds = vec![
+        EpiphanyPrediction {
+            at_chapter: 1,
+            direction_phase: 0.0,
+            initial_truth_freq: 0.5,
+            initial_truth_conf: 0.5,
+        },
+        EpiphanyPrediction {
+            at_chapter: 2,
+            direction_phase: 0.0,
+            initial_truth_freq: 0.5,
+            initial_truth_conf: 0.5,
+        },
+    ];
+    let gt = vec![GroundTruthBeat {
+        at_chapter: 4,
+        confirmed_direction: 0.0,
+        matched_predictions: vec![0],
+    }];
+    let m = evaluate(&preds, &gt);
+    assert!((m.arc_shift_f1 - 2.0 / 3.0).abs() < 1e-3);
+}