feat: GPU/TensorCore integration — TensorFlow backend, GPU-accelerated reasoning, training, and memory

- New fusionagi/gpu/ module with TensorBackend protocol abstraction
  - TensorFlowBackend: GPU-accelerated ops with TensorCore mixed-precision
  - NumPyBackend: CPU fallback (always available, no extra deps)
  - Auto-selects best available backend at runtime

- GPU-accelerated operations:
  - Cosine similarity matrix (batched, XLA-compiled)
  - Multi-head attention for consensus scoring
  - Batch hypothesis scoring on GPU
  - Semantic similarity search (pairwise, nearest-neighbor, deduplication)

- New TensorFlowAdapter (fusionagi/adapters/):
  - LLMAdapter for local TF/Keras model inference
  - TensorCore mixed-precision support
  - GPU-accelerated embedding synthesis fallback

- Reasoning pipeline integration:
  - gpu_scoring.py: drop-in GPU replacement for multi_path scoring
  - Super Big Brain: use_gpu config flag, GPU scoring when available

- Memory integration:
  - gpu_search.py: GPU-accelerated semantic search for SemanticGraphMemory

- Self-improvement integration:
  - gpu_training.py: gradient-based heuristic weight optimization
  - Reflective memory training loop with loss tracking

- Dependencies: gpu extra (tensorflow>=2.16, numpy>=1.26)
- 64 new tests (276 total), all passing
- Architecture spec: docs/gpu_tensorcore_integration.md

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>

fusionagi/gpu/tensor_scoring.py

@@ -0,0 +1,135 @@
+"""GPU-accelerated hypothesis scoring for reasoning pipelines.
+
+Provides batched scoring of hypotheses against atomic semantic units
+using GPU-accelerated tensor operations. Replaces the CPU-bound
+ThreadPoolExecutor-based scoring in multi_path.py.
+"""
+
+from __future__ import annotations
+
+from fusionagi._logger import logger
+from fusionagi.gpu.backend import TensorBackend, get_backend
+from fusionagi.reasoning.tot import ThoughtNode
+from fusionagi.schemas.atomic import AtomicSemanticUnit
+
+
+def gpu_score_hypotheses(
+    hypotheses: list[str],
+    units: list[AtomicSemanticUnit],
+    backend: TensorBackend | None = None,
+) -> list[tuple[ThoughtNode, float]]:
+    """Score hypotheses against atomic units using GPU-accelerated similarity.
+
+    Replaces the CPU-based generate_and_score_parallel with batched GPU operations.
+
+    Args:
+        hypotheses: List of hypothesis text strings.
+        units: List of atomic semantic units for reference.
+        backend: TensorBackend to use.
+
+    Returns:
+        List of (ThoughtNode, score) tuples sorted by score descending.
+    """
+    if not hypotheses:
+        return []
+
+    be = backend or get_backend()
+    import numpy as np
+
+    hyp_embeddings = be.embed_texts(hypotheses)
+
+    unit_texts = [u.content for u in units if u.content]
+    if not unit_texts:
+        nodes = []
+        for h in hypotheses:
+            node = ThoughtNode(
+                thought=h,
+                trace=[h],
+                unit_refs=[u.unit_id for u in units[:10]],
+                score=0.5,
+            )
+            nodes.append((node, 0.5))
+        return nodes
+
+    unit_embeddings = be.embed_texts(unit_texts)
+
+    sim_matrix = be.to_numpy(be.cosine_similarity_matrix(hyp_embeddings, unit_embeddings))
+
+    coherence_scores = np.mean(sim_matrix, axis=1)
+
+    max_sim = np.max(sim_matrix, axis=1)
+    consistency_scores = max_sim
+
+    combined_scores = 0.5 * coherence_scores + 0.5 * consistency_scores
+    combined_scores = np.clip(combined_scores, 0.0, 1.0)
+
+    results: list[tuple[ThoughtNode, float]] = []
+    for i, h in enumerate(hypotheses):
+        score = float(combined_scores[i])
+        node = ThoughtNode(
+            thought=h,
+            trace=[h],
+            unit_refs=[u.unit_id for u in units[:10]],
+            score=score,
+            metadata={"gpu_scored": True, "coherence": float(coherence_scores[i])},
+        )
+        results.append((node, score))
+
+    results.sort(key=lambda x: x[1], reverse=True)
+
+    logger.debug(
+        "GPU hypothesis scoring complete",
+        extra={
+            "hypotheses": len(hypotheses),
+            "units": len(units),
+            "best_score": results[0][1] if results else 0.0,
+            "backend": be.name,
+        },
+    )
+    return results
+
+
+def gpu_score_claims_against_reference(
+    claims: list[str],
+    reference: str,
+    weights: list[float] | None = None,
+    backend: TensorBackend | None = None,
+) -> list[float]:
+    """Score a batch of claims against a single reference using GPU batch_score.
+
+    Args:
+        claims: List of claim texts.
+        reference: Reference text to score against.
+        weights: Optional per-dimension weights.
+        backend: TensorBackend to use.
+
+    Returns:
+        List of scores for each claim.
+    """
+    if not claims:
+        return []
+
+    be = backend or get_backend()
+
+    claim_emb = be.embed_texts(claims)
+    ref_emb = be.embed_texts([reference])
+
+    weight_tensor = None
+    if weights is not None:
+        import numpy as np
+
+        dim = be.to_numpy(ref_emb).shape[-1]
+        w = np.ones(dim, dtype=np.float32)
+        for i, wt in enumerate(weights[:dim]):
+            w[i] = wt
+        weight_tensor = be.from_numpy(w)
+
+    import numpy as np
+
+    ref_squeezed = be.to_numpy(ref_emb)[0]
+    scores = be.to_numpy(
+        be.batch_score(claim_emb, be.from_numpy(ref_squeezed), weight_tensor)
+    )
+
+    scores = np.atleast_1d(scores)
+    return list(scores.tolist())