Merge remote-tracking branch 'origin/main'

chat/runtime.py

@@ -22,6 +22,7 @@ from core.physics.identity import (
 from field.state import FieldState
 from generate.articulation import ArticulationPlan, realize
 from generate.dialogue import DialogueRole, classify_dialogue_blade, propose_dialogue
+from generate.intent_bridge import articulate_with_intent
 from generate.proposition import FrameRegistry, Proposition, propose
 from generate.result import GenerationResult
 from generate.stream import generate
@@ -387,6 +388,22 @@ class ChatRuntime:
             salience_top_k=self.config.salience_top_k,
             inhibition_threshold=self.config.inhibition_threshold,
         )
+
+        # --- Articulation fidelity: replace bare S-P-O join with intent-aware surface ---
+        # articulate_with_intent() classifies the input intent, builds a proposition
+        # graph grounded on the generation result's recalled tokens, and calls the
+        # realize_semantic() path (13-construction realizer) that was previously
+        # implemented but never connected to the chat hot path.
+        # Falls back to the existing articulation.surface when bridge returns "".
+        if self.config.output_language == "en":
+            recalled_words = tuple(
+                tok for tok in (result.tokens or ()) if tok and tok.isalpha()
+            )
+            intent_surface = articulate_with_intent(text, articulation, recalled_words)
+            if intent_surface:
+                articulation = replace(articulation, surface=intent_surface)
+        # --- end articulation fidelity fix ---
+
         reasoning_trajectory = _make_trajectory_from_result(result, self._context.turn)
         identity_score = self._identity_check.check(reasoning_trajectory, self.identity_manifold)
         flagged = identity_score.flagged

generate/intent_bridge.py

@@ -0,0 +1,129 @@
+"""generate/intent_bridge.py — connects intent classification to the realizer.
+
+Bridges the gap between chat/runtime.py's articulation path (which resolves
+Proposition slot-versors into raw word tokens) and the intent-aware realizer
+pipeline (realize_semantic / realize_target in realizer.py, which are fully
+implemented but were never called from the chat hot path).
+
+Design constraints:
+  - Deterministic: same input text + same field state → same surface
+  - No LLM fallback
+  - Falls back cleanly to the existing ArticulationPlan when the realizer
+    cannot produce a non-empty surface (OOV-heavy input, UNKNOWN intent
+    with no grounded obj slots)
+  - Does not alter the ArticulationPlan dataclass or ChatResponse structure;
+    only the .surface field is replaced when the bridge succeeds
+"""
+
+from __future__ import annotations
+
+from generate.articulation import ArticulationPlan
+from generate.graph_planner import (
+    GraphEdge,
+    GraphNode,
+    PropositionGraph,
+    Relation,
+    ground_graph,
+    plan_articulation,
+)
+from generate.intent import DialogueIntent, IntentTag, classify_intent
+from generate.realizer import RealizedPlan, realize_semantic
+
+_PENDING = "<pending>"
+_PRIOR = "<prior>"
+_EMPTY_INDICATORS = frozenset({_PENDING, _PRIOR, "...", ""})
+
+
+def classify_intent_from_input(text: str) -> DialogueIntent:
+    """Run the rule-based intent classifier against raw input text."""
+    return classify_intent(text)
+
+
+def _build_graph_from_intent(intent: DialogueIntent, plan: ArticulationPlan) -> PropositionGraph:
+    """Build a minimal PropositionGraph from a classified intent and an ArticulationPlan.
+
+    Uses the resolved slot words from ArticulationPlan (subject, predicate, object)
+    as the concrete node content, with the intent tag selecting the predicate.
+    """
+    from generate.graph_planner import _INTENT_PREDICATES  # noqa: PLC0415
+
+    predicate = _INTENT_PREDICATES.get(intent.tag, "addresses")
+    subject = intent.subject or plan.subject or ""
+    obj = plan.object or plan.predicate or _PENDING
+
+    graph = PropositionGraph()
+
+    if intent.tag is IntentTag.COMPARISON:
+        secondary = intent.secondary_subject or plan.object or plan.predicate or obj
+        left = GraphNode(
+            node_id="p0",
+            subject=subject,
+            predicate=predicate,
+            obj=secondary,
+            source_intent=intent.tag,
+        )
+        right = GraphNode(
+            node_id="p1",
+            subject=secondary,
+            predicate=predicate,
+            obj=subject,
+            source_intent=intent.tag,
+        )
+        edge = GraphEdge(source="p0", target="p1", relation=Relation.CONTRAST)
+        return graph.add_node(left).add_node(right).add_edge(edge)
+
+    root = GraphNode(
+        node_id="p0",
+        subject=subject,
+        predicate=predicate,
+        obj=obj,
+        source_intent=intent.tag,
+    )
+    return graph.add_node(root)
+
+
+def _is_useful_surface(surface: str) -> bool:
+    """Return True when the realized surface is non-empty and fully grounded."""
+    if not surface or not surface.strip():
+        return False
+    for indicator in _EMPTY_INDICATORS:
+        if indicator and indicator in surface:
+            return False
+    return True
+
+
+def articulate_with_intent(
+    text: str,
+    plan: ArticulationPlan,
+    recalled_words: tuple[str, ...] = (),
+) -> str:
+    """Return an intent-aware surface string for *plan*, or "" if none can be produced.
+
+    Steps:
+      1. Classify intent from raw input *text*
+      2. Build a PropositionGraph from the intent + ArticulationPlan slot words
+      3. Ground <pending> obj slots with *recalled_words* from generation result
+      4. Plan articulation (topological walk)
+      5. Realize via realize_semantic() for intent-specific templates
+      6. Return the surface, or "" if the result is empty / ungrounded
+
+    The caller (chat/runtime.py) should fall back to the existing
+    ArticulationPlan.surface when this returns "".
+    """
+    intent = classify_intent_from_input(text)
+
+    graph = _build_graph_from_intent(intent, plan)
+    if recalled_words:
+        graph = ground_graph(graph, recalled_words)
+
+    articulation_target = plan_articulation(graph)
+    realized: RealizedPlan = realize_semantic(articulation_target, graph)
+
+    if not realized.surface or not realized.fragments:
+        return ""
+
+    surface = realized.surface
+    if not _is_useful_surface(surface):
+        return ""
+
+    return surface

generate/stream.py

@@ -127,21 +127,49 @@ def _close_final_state(state: FieldState) -> FieldState:
     )
 
 
+def _softmax(scores: list[float]) -> list[float]:
+    """Numerically stable softmax over a list of floats."""
+    if not scores:
+        return []
+    arr = np.asarray(scores, dtype=np.float64)
+    arr -= arr.max()
+    exp = np.exp(arr)
+    total = float(exp.sum())
+    if total < 1e-12:
+        return [1.0 / len(scores)] * len(scores)
+    return (exp / total).tolist()
+
+
 def _recall_state(state: FieldState, vault, top_k: int) -> tuple[FieldState, int]:
     if vault is None or top_k <= 0:
         return state, 0
 
+    hits = vault.recall(state.F, top_k=top_k)
+    if not hits:
+        return state, 0
+
+    # Drift fix 2: score-weighted vault recall transitions.
+    #
+    # Previously every recalled versor was applied as a full rotor transition
+    # regardless of its recall score, giving a stale turn-3 hit the same
+    # influence as a high-confidence recent hit.
+    #
+    # Now each rotor is scaled by its softmax-normalised score weight, so the
+    # field moves proportionally to how strongly each hit was recalled.
+    # Hits with infinite score (exact self-matches) receive full weight 1.0
+    # and short-circuit the softmax path.
+    finite_hits = [h for h in hits if h["score"] != float("inf")]
+    exact_hits = [h for h in hits if h["score"] == float("inf")]
+
     current = state
     hits_applied = 0
-    for hit in vault.recall(current.F, top_k=top_k):
+
+    # Exact self-matches are applied at full weight first.
+    for hit in exact_hits:
         recalled_F = np.asarray(hit["versor"], dtype=np.float64)
         try:
             V = word_transition_rotor(current.F, recalled_F)
         except ValueError:
-            # Vault stores field states as well as proposition/memory payloads.
-            # Not every recalled versor is a valid transition target for the
-            # live generation operator. Generation must fail closed here rather
-            # than normalizing or repairing recalled memory in the hot path.
             continue
         current = propagate_step(current, V)
         current = FieldState(
@@ -153,6 +181,31 @@ def _recall_state(state: FieldState, vault, top_k: int) -> tuple[FieldState, int
             valence=state.valence,
         )
         hits_applied += 1
+
+    if finite_hits:
+        raw_scores = [h["score"] for h in finite_hits]
+        weights = _softmax(raw_scores)
+        for hit, weight in zip(finite_hits, weights):
+            recalled_F = np.asarray(hit["versor"], dtype=np.float64)
+            try:
+                V = word_transition_rotor(current.F, recalled_F)
+            except ValueError:
+                continue
+            # Scale the rotor toward identity by (1 - weight) so a weight of
+            # ~0.0 leaves the field nearly unchanged and weight ~1.0 applies
+            # the full transition.
+            V_scaled = weight * V + (1.0 - weight) * np.eye(V.shape[0], dtype=V.dtype)
+            current = propagate_step(current, V_scaled)
+            current = FieldState(
+                F=current.F,
+                node=state.node,
+                step=current.step,
+                holonomy=state.holonomy,
+                energy=state.energy,
+                valence=state.valence,
+            )
+            hits_applied += 1
+
     return current, hits_applied
 
 

session/context.py

@@ -11,7 +11,7 @@ from __future__ import annotations
 import numpy as np
 
 from algebra.backend import cga_inner, versor_apply
-from algebra.versor import versor_condition as _versor_condition
+from algebra.versor import unitize_versor, versor_condition as _versor_condition
 from field.state import FieldState
 from generate.dialogue import DialogueTurn
 from generate.proposition import Proposition
@@ -23,6 +23,45 @@ from session.graph import SessionGraph
 from session.referents import ReferentRegistry
 from vault.store import VaultStore
 
+# Dialogue blade EMA decay — how much the running blade "remembers" prior turns.
+# α=0.15 means each new confirmed turn adds 15% of its blade to the accumulator,
+# so a concept confirmed N times builds proportionally stronger attractor force.
+_BLADE_EMA_ALPHA: float = 0.15
+
+# Anchor pull strength — how hard each finalized turn is pulled back toward the
+# session anchor field. 0.05 is intentionally mild: it corrects slow angular
+# drift without distorting the response field for single-turn queries.
+_ANCHOR_PULL_ALPHA: float = 0.05
+
+
+def _slerp_toward(
+    F: np.ndarray,
+    target: np.ndarray,
+    alpha: float,
+) -> np.ndarray:
+    """Spherical-linear interpolation of F toward target by fraction alpha.
+
+    When the inner product is near ±1 (nearly parallel/antiparallel versors),
+    falls back to linear interpolation to avoid numerical instability.
+    """
+    f_norm = float(np.linalg.norm(F))
+    t_norm = float(np.linalg.norm(target))
+    if f_norm < 1e-10 or t_norm < 1e-10:
+        return F
+    f_unit = F / f_norm
+    t_unit = target / t_norm
+    cos_theta = float(np.clip(np.dot(f_unit.ravel(), t_unit.ravel()), -1.0, 1.0))
+    theta = float(np.arccos(abs(cos_theta)))
+    if theta < 1e-6:
+        # Nearly parallel — linear blend is numerically identical
+        result = (1.0 - alpha) * F + alpha * target
+    else:
+        sin_theta = float(np.sin(theta))
+        w_f = float(np.sin((1.0 - alpha) * theta)) / sin_theta
+        w_t = float(np.sin(alpha * theta)) / sin_theta
+        result = w_f * F + w_t * target
+    return np.asarray(result, dtype=F.dtype)
+
 
 class SessionContext:
     def __init__(self, vocab, persona=None, vault=None, vault_reproject_interval: int = 100):
@@ -93,8 +132,7 @@ class SessionContext:
         snapshot_sources = self.referents.consumed_turns()
         snapshot_slots = self.referents.consumed_slots()
         candidate, _ = self._field_from_tokens(tokens, resolve_referents=True)
-        # Restore consumed metadata because probe must not define graph edges.
-        self.referents._last_resolved_sources = snapshot_sources  # internal rollback by design
+        self.referents._last_resolved_sources = snapshot_sources
         self.referents._last_resolved_slots = snapshot_slots
         return candidate
 
@@ -120,12 +158,29 @@ class SessionContext:
         blade = proposition.relation
         turn = _DT(proposition=proposition, outer_product_blade=blade)
         self._dialogue_history_compat.append(turn)
+
         if self.running_dialogue_blade is None:
+            # First turn: initialise the accumulator at full blade magnitude.
             self.running_dialogue_blade = blade.copy()
         else:
-            alpha = cga_inner(self.running_dialogue_blade, blade)
-            sign = 1.0 if alpha >= 0.0 else -1.0
-            self.running_dialogue_blade = sign * blade
+            # Drift fix 1: magnitude-preserving EMA accumulation.
+            #
+            # Previously: running_blade = sign(inner) * new_blade
+            # This reset magnitude to 1 on every turn, discarding how many
+            # prior turns had confirmed the same concept direction.
+            #
+            # Now: running_blade = (1 - α) * running_blade + α * new_blade
+            # when the new blade is aligned (inner ≥ 0), or
+            #        running_blade = (1 - α) * running_blade - α * new_blade
+            # when anti-aligned, so the accumulator always reinforces the
+            # dominant direction and grows in magnitude with each confirmation.
+            alpha = _BLADE_EMA_ALPHA
+            alignment = cga_inner(self.running_dialogue_blade, blade)
+            sign = 1.0 if float(alignment) >= 0.0 else -1.0
+            self.running_dialogue_blade = (
+                (1.0 - alpha) * self.running_dialogue_blade + alpha * sign * blade
+            )
+
         return turn
 
     @property
@@ -160,6 +215,29 @@ class SessionContext:
             valence=field_state.valence,
         )
 
+    def _anchor_pull(self, field_state: FieldState) -> FieldState:
+        """Drift fix 3: mild slerp toward the session anchor field.
+
+        Applied after hemisphere correction. Provides continuous conjugate
+        correction against slow angular drift that stays within the hemisphere
+        but gradually moves away from the session concept attractor.
+
+        α=0.05 is intentionally mild — it corrects accumulated drift over many
+        turns without distorting single-turn response fields.
+        """
+        if self._anchor_field is None:
+            return field_state
+        pulled_F = _slerp_toward(field_state.F, self._anchor_field, _ANCHOR_PULL_ALPHA)
+        pulled_F = unitize_versor(pulled_F)
+        return FieldState(
+            F=pulled_F,
+            node=field_state.node,
+            step=field_state.step,
+            holonomy=field_state.holonomy,
+            energy=field_state.energy,
+            valence=field_state.valence,
+        )
+
     def finalize_turn(
         self,
         result: GenerationResult,
@@ -185,7 +263,9 @@ class SessionContext:
         self._register_result_referent(result)
         active_slots = self.referents.active_slots() | active_slots
 
+        # Drift fix 3: hemisphere correction + anchor pull (conjugate correction).
         oriented_state = self._hemisphere_consistent_field(result.final_state)
+        oriented_state = self._anchor_pull(oriented_state)
 
         self.graph.add_turn(
             turn_idx=self.turn,