Ely-Eye — local multimodal context operating system on Qwen3.5-9B

Real-models implementation: multimodal ingestion, Context Cartridge memory,
sparse + Qwen embedding retrieval, Cache Fabric, Qwen runtime adapters, verifier
contracts, Visual HashHop proofs, BF16 LoRA fine-tuning, and a local dashboard.
Verified by a 15-check PRD proof suite on the live model.

backend/src/ely_eye/dvkv.py

@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class VisualToken:
+    token_id: str
+    vector: np.ndarray
+    attention_importance: float
+    evidence_value: float
+
+
+def select_dvkv_tokens(
+    tokens: list[VisualToken],
+    budget: int,
+    diversity_weight: float = 0.35,
+    evidence_weight: float = 0.30,
+) -> list[VisualToken]:
+    if budget <= 0:
+        raise ValueError("DVKV budget must be positive")
+    if len(tokens) <= budget:
+        return tokens
+
+    selected: list[VisualToken] = []
+    remaining = tokens.copy()
+    first = max(remaining, key=lambda token: token.attention_importance + evidence_weight * token.evidence_value)
+    selected.append(first)
+    remaining.remove(first)
+
+    while remaining and len(selected) < budget:
+        scores = []
+        selected_vectors = np.stack([normalize(token.vector) for token in selected])
+        for token in remaining:
+            vector = normalize(token.vector)
+            nearest_similarity = float(np.max(selected_vectors @ vector))
+            diversity = 1.0 - nearest_similarity
+            score = (
+                token.attention_importance
+                + diversity_weight * diversity
+                + evidence_weight * token.evidence_value
+            )
+            scores.append(score)
+        best_index = int(np.argmax(scores))
+        selected.append(remaining.pop(best_index))
+    return selected
+
+
+def normalize(vector: np.ndarray) -> np.ndarray:
+    norm = float(np.linalg.norm(vector))
+    if norm == 0.0:
+        return vector
+    return vector / norm