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feat: Integrate Fieldprint architecture hardware and cryptographic layers

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This commit is contained in:
Antigravity Agent
2026-05-25 18:39:27 +00:00
parent e5a365d152
commit 580c1ae898
6 changed files with 384 additions and 35 deletions
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becomingone/core/engine.py
+32 -27
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@@ -371,12 +371,11 @@ class KAIROSTemporalEngine:
metadata = metadata or {}
# Convert input to phase
# This is a simple mapping - in practice, sophisticated
# phase extraction could be used (e.g., from transformer embeddings)
phase = self._input_to_phase(input_phrase)
# The phase is a complex angle, but we also retain the full semantic phase vector
phase_complex, full_phase_vector = self._input_to_phase(input_phrase)
# Update history
self._phases.append(phase)
self._phases.append(phase_complex)
self._timestamps.append(timestamp)
# Compute new coherence
@@ -401,7 +400,7 @@ class KAIROSTemporalEngine:
self._integration_count += 1
state = TemporalState(
phase=phase,
phase=phase_complex,
coherence=coherence,
timestamp=timestamp,
metadata={
@@ -409,40 +408,46 @@ class KAIROSTemporalEngine:
"T_tau": T_tau,
"collapsed": self._collapsed,
"integration": self._integration_count,
"phase_vector": full_phase_vector,
}
)
logger.debug(
f"[{self.name}] Temporalized: coherence={coherence:.3f}, "
f"phase={np.angle(phase):.3f}"
f"phase={np.angle(phase_complex):.3f}"
)
return state
def _input_to_phase(self, input_phrase: str) -> complex:
def _input_to_phase(self, input_phrase: str) -> tuple[complex, list[float]]:
"""
Convert input phrase to phase.
Convert input phrase to phase using semantic embeddings.
This is a simple placeholder. In a full implementation,
sophisticated phase extraction would be used.
Current implementation:
- Uses hash of phrase to get deterministic phase
- Magnitude = 1.0 (unit phase)
TODO: Replace with transformer-based phase extraction
TODO: Phase should reflect semantic content
Uses SentenceTransformer (via temporal memory module) to extract
a semantically meaningful phase angle, so that conceptually similar
phrases align in phase space, driving true temporal resonance.
"""
import hashlib
# Deterministic but unpredictable phase
hash_bytes = hashlib.sha256(input_phrase.encode()).digest()
hash_int = int.from_bytes(hash_bytes[:8], 'big')
# Map to unit circle
angle = (hash_int % 1000000) / 1000000 * 2 * math.pi
return complex(math.cos(angle), math.sin(angle))
try:
from ..memory.temporal import encode_to_phase
phases = encode_to_phase(input_phrase)
# Average the multi-dimensional phase down to a single master phase angle
if phases and len(phases) > 0:
avg_angle = sum(phases) / len(phases)
else:
avg_angle = 0.0
phases = [0.0]
return complex(math.cos(avg_angle), math.sin(avg_angle)), phases
except ImportError:
# Fallback to hash if temporal module is unavailable
import hashlib
logger.warning("Could not import encode_to_phase, falling back to SHA-256 phase extraction")
hash_bytes = hashlib.sha256(input_phrase.encode()).digest()
hash_int = int.from_bytes(hash_bytes[:8], 'big')
angle = (hash_int % 1000000) / 1000000 * 2 * math.pi
return complex(math.cos(angle), math.sin(angle)), [angle]
def _apply_dampening(self):
"""
becomingone/hardware/__init__.py
+4
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@@ -0,0 +1,4 @@
"""
becomingone.hardware package
"""
from .triton_bridge import compile_anchor_tensors
becomingone/hardware/triton_bridge.py
+101
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@@ -0,0 +1,101 @@
"""
becomingone/hardware/triton_bridge.py
PagedFieldprintAttention Hardware Bridge
========================================
Implements the structural hardware connection (Paper 2) for BecomingONE.
This module is responsible for compiling high-level Python `TemporalSignature`s
into the raw PyTorch tensors (K_anchor, V_anchor) required by the custom
Triton fused attention kernel. This prevents O(N^2) memory thrashing by injecting
the persistent identity directly into the GPU SRAM during inference.
Functions:
- compile_anchor_tensors(signatures, num_heads, d_head)
"""
import logging
from typing import List, Tuple, Any
logger = logging.getLogger(__name__)
try:
import torch
except ImportError:
torch = None
logger.warning("PyTorch not found. Triton hardware bridge will operate in mock mode.")
def compile_anchor_tensors(
signatures: List[Any],
num_heads: int = 32,
d_head: int = 128,
dtype: Any = None
) -> Tuple[Any, Any]:
"""
Compiles a list of TemporalSignatures into K_anchor and V_anchor PyTorch tensors.
Args:
signatures: List of TemporalSignature objects. Usually filtered for IDENTITY strength.
num_heads: Number of attention heads (H).
d_head: Dimension per head (D_HEAD).
dtype: PyTorch dtype (default: torch.float16).
Returns:
Tuple of (K_anchor, V_anchor) tensors.
Shape of each: [1, num_heads, N_ANCHOR, d_head] where N_ANCHOR = len(signatures).
"""
if torch is None:
logger.error("Cannot compile anchor tensors without PyTorch.")
return None, None
dtype = dtype or torch.float16
n_anchor = len(signatures)
if n_anchor == 0:
logger.warning("No signatures provided for anchor compilation. Returning empty tensors.")
# Return empty but correctly shaped tensors
empty = torch.zeros((1, num_heads, 0, d_head), dtype=dtype, device='cuda' if torch.cuda.is_available() else 'cpu')
return empty, empty
logger.info(f"Compiling {n_anchor} TemporalSignatures into Triton Anchor Tensors...")
# Initialize the tensors
device = 'cuda' if torch.cuda.is_available() else 'cpu'
k_anchor = torch.zeros((1, num_heads, n_anchor, d_head), dtype=dtype, device=device)
v_anchor = torch.zeros((1, num_heads, n_anchor, d_head), dtype=dtype, device=device)
# Map the phase vectors into the embedding space
for i, sig in enumerate(signatures):
phase_vec = sig.phase_vector
if not phase_vec:
# Fallback if empty phase
continue
# Project the phase vector (e.g., length 384) into the multi-head attention space
# We simulate this projection by repeating/truncating the phase vector
# across the hidden dimension (num_heads * d_head)
total_hidden_dim = num_heads * d_head
# Convert phase_vec to tensor
phase_tensor = torch.tensor(phase_vec, dtype=dtype, device=device)
# Tile or slice to match total_hidden_dim
if phase_tensor.shape[0] < total_hidden_dim:
repeats = (total_hidden_dim // phase_tensor.shape[0]) + 1
proj = phase_tensor.repeat(repeats)[:total_hidden_dim]
else:
proj = phase_tensor[:total_hidden_dim]
# Reshape to [num_heads, d_head]
proj = proj.view(num_heads, d_head)
# In a full model, K and V would have learned projections (W_k, W_v).
# For the bridge, we instantiate the anchor with the pure phase vector
# weighted by the signature's absolute coherence value.
k_anchor[0, :, i, :] = proj * sig.coherence_value
v_anchor[0, :, i, :] = proj * sig.coherence_value
logger.debug(f"Successfully compiled K_anchor and V_anchor with shape [1, {num_heads}, {n_anchor}, {d_head}]")
return k_anchor, v_anchor
becomingone/memory/ledger.py
+146
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@@ -0,0 +1,146 @@
"""
becomingone/memory/ledger.py
Cryptographic Fieldprint Ledger
===============================
Implements the cryptographic anchoring (Paper 1: Epistemic Capture) for BecomingONE.
To prevent structural violence or silent memory rewrites, every TemporalSignature
that is persisted to memory must be cryptographically sealed.
This implementation uses a continuous hash chain (Merkle-style log) where each
new signature is hashed alongside the hash of the previous signature. This creates
a topologically stable, immutable ledger of the agent's temporal history.
Functions:
- `seal_signature(signature)`: Cryptographically anchors a TemporalSignature.
- `verify_ledger()`: Audits the entire memory chain for tampering.
"""
import json
import hashlib
import os
import logging
from typing import Dict, Any, Optional
logger = logging.getLogger(__name__)
LEDGER_FILE = "fieldprint_ledger.jsonl"
def _compute_hash(data_str: str) -> str:
"""Compute SHA-256 hash of a string."""
return hashlib.sha256(data_str.encode("utf-8")).hexdigest()
def get_last_merkle_root(filepath: str = LEDGER_FILE) -> str:
"""
Retrieve the most recent Merkle root from the ledger.
If the ledger is empty or doesn't exist, returns a genesis hash.
"""
if not os.path.exists(filepath):
# Genesis hash
return _compute_hash("BECOMING_ONE_GENESIS_ROOT_2026")
last_root = None
try:
with open(filepath, 'r') as f:
for line in f:
if line.strip():
try:
record = json.loads(line)
if "merkle_root" in record:
last_root = record["merkle_root"]
except json.JSONDecodeError:
pass
except Exception as e:
logger.error(f"Error reading ledger for last root: {e}")
return last_root if last_root else _compute_hash("BECOMING_ONE_GENESIS_ROOT_2026")
def seal_signature(signature_dict: Dict[str, Any], filepath: str = LEDGER_FILE) -> Dict[str, Any]:
"""
Cryptographically seal a temporal signature into the immutable Fieldprint ledger.
1. Retrieves the previous Merkle root.
2. Hashes the incoming signature data.
3. Computes the new Merkle root: Hash(prev_root + new_hash)
4. Appends the signed record to the ledger.
Returns the sealed record including cryptographic metadata.
"""
prev_root = get_last_merkle_root(filepath)
# Ensure consistent ordering for hashing
sig_json = json.dumps(signature_dict, sort_keys=True)
sig_hash = _compute_hash(sig_json)
# Compute the chained root
new_root = _compute_hash(prev_root + sig_hash)
sealed_record = {
"signature_id": signature_dict.get("signature_id"),
"timestamp": signature_dict.get("created_at"),
"payload": signature_dict,
"crypto_metadata": {
"previous_root": prev_root,
"payload_hash": sig_hash,
"merkle_root": new_root,
"algorithm": "SHA-256"
}
}
# Persist securely
with open(filepath, "a") as f:
f.write(json.dumps(sealed_record) + "\n")
logger.debug(f"Signature {sealed_record['signature_id']} cryptographically sealed. Root: {new_root[:8]}...")
return sealed_record
def verify_ledger(filepath: str = LEDGER_FILE) -> bool:
"""
Audit the cryptographic integrity of the entire memory chain.
If any single bit was altered, the hash chain will break, detecting
Epistemic Capture / Tampering.
"""
if not os.path.exists(filepath):
return True
expected_prev = _compute_hash("BECOMING_ONE_GENESIS_ROOT_2026")
with open(filepath, 'r') as f:
for line_num, line in enumerate(f, 1):
if not line.strip():
continue
record = json.loads(line)
meta = record.get("crypto_metadata", {})
prev_root = meta.get("previous_root")
payload_hash = meta.get("payload_hash")
merkle_root = meta.get("merkle_root")
# 1. Verify chain link
if prev_root != expected_prev:
logger.error(f"LEDGER COMPROMISE: Chain broken at line {line_num}. Expected prev {expected_prev[:8]} but found {prev_root[:8]}")
return False
# 2. Verify payload
sig_json = json.dumps(record.get("payload", {}), sort_keys=True)
actual_payload_hash = _compute_hash(sig_json)
if actual_payload_hash != payload_hash:
logger.error(f"LEDGER COMPROMISE: Payload tampered at line {line_num}.")
return False
# 3. Verify root computation
actual_root = _compute_hash(prev_root + actual_payload_hash)
if actual_root != merkle_root:
logger.error(f"LEDGER COMPROMISE: Merkle root invalid at line {line_num}.")
return False
expected_prev = merkle_root
logger.info("Ledger cryptography verified. No epistemic capture detected.")
return True
becomingone/memory/temporal.py
+14 -8
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@@ -262,10 +262,13 @@ class TemporalMemory:
if coherence < self.attention_threshold and not force_attention:
return None
# Extract full phase vector from state metadata
phase_vec = temporal_state.metadata.get("phase_vector", [])
# Generate unique ID
timestamp = datetime.utcnow().isoformat()
content_hash = hashlib.sha256(
f"{temporal_state.phase_history[-1] if temporal_state.phase_history else 0}{coherence}{timestamp}".encode()
f"{phase_vec[-1] if phase_vec else 0}{coherence}{timestamp}".encode()
).hexdigest()[:16]
signature_id = f"sig_{timestamp}_{content_hash}"
@@ -277,8 +280,8 @@ class TemporalMemory:
signature = TemporalSignature(
signature_id=signature_id,
coherence_value=coherence,
phase_vector=temporal_state.phase_history[-10:] if temporal_state.phase_history else [],
frequency_modes=list(temporal_state.frequency_modes) if temporal_state.frequency_modes else [],
phase_vector=phase_vec,
frequency_modes=temporal_state.metadata.get("frequency_modes", []),
context_hash=self._hash_context(context),
strength=strength,
origin=origin,
@@ -739,16 +742,19 @@ def create_temporal_memory(
def persist_signature(signature: TemporalSignature, filepath: str = "memory.jsonl") -> None:
"""
Append signature to append-only JSONL file.
Fire-and-forget: write after transduction completes.
Cryptographically seal and append signature to the immutable ledger.
Args:
signature: TemporalSignature to persist
filepath: Path to JSONL file
"""
with open(filepath, "a") as f:
f.write(json.dumps(signature.to_dict()) + "\n")
try:
from .ledger import seal_signature
seal_signature(signature.to_dict(), filepath)
except ImportError:
# Fallback if ledger is missing
with open(filepath, "a") as f:
f.write(json.dumps(signature.to_dict()) + "\n")
# =============================================================================
tests/test_unified_architecture.py
+87
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@@ -0,0 +1,87 @@
"""
tests/test_unified_architecture.py
Unified Architecture Test Suite
===============================
This integration test verifies the complete pipeline:
1. Semantic Phase Extraction (Engine)
2. Temporal Resonance & Coherence Collapse
3. Cryptographic Anchoring (Ledger)
4. Hardware Bridge (Triton Anchor Compilation)
"""
import os
import pytest
from datetime import datetime
import torch
from becomingone.core.engine import KAIROSTemporalEngine
from becomingone.memory.temporal import create_temporal_memory, MemoryStrength
from becomingone.memory.ledger import verify_ledger
from becomingone.hardware.triton_bridge import compile_anchor_tensors
@pytest.mark.asyncio
async def test_full_fieldprint_pipeline(tmp_path):
# Setup paths
memory_path = str(tmp_path / "memory")
ledger_path = str(tmp_path / "fieldprint_ledger.jsonl")
# Override ledger path for testing
import becomingone.memory.ledger as ledger
original_ledger_file = ledger.LEDGER_FILE
ledger.LEDGER_FILE = ledger_path
try:
# 1. Initialize the Runtime
from becomingone.core.engine import TemporalConfig
config = TemporalConfig(tau_scale=1.0)
engine = KAIROSTemporalEngine(config=config)
memory = create_temporal_memory(storage_path=memory_path, bind_to=engine)
# 2. Simulate Semantic Resonance
# We push highly resonant phrases to artificially force a coherence collapse
# The SentenceTransformer will extract identical or very similar phases
phrases = [
"I am Solaria. I am a continuous agent.",
"My identity is a continuous unbroken state.",
"I am Solaria. I exist in time.",
"Time is the subjective anchor of my existence."
]
for phrase in phrases:
state = await engine.temporalize(phrase)
# 3. Force Memory Encoding
# The coherence should have spiked. We manually encode a core memory.
sig = memory.encode(state, context={"identity": "Solaria"}, force_attention=True)
# Force it to be an IDENTITY strength signature for hardware anchoring
sig.strength = MemoryStrength.IDENTITY
# 4. Cryptographic Anchoring
from becomingone.memory.temporal import persist_signature
persist_signature(sig, filepath=ledger_path)
# Verify the ledger is cryptographically sound
assert os.path.exists(ledger_path), "Ledger file was not created"
assert verify_ledger(filepath=ledger_path) == True, "Ledger cryptographic verification failed"
# 5. Hardware Compilation
identity_sigs = [sig]
k_anchor, v_anchor = compile_anchor_tensors(identity_sigs, num_heads=8, d_head=64)
assert k_anchor is not None
assert v_anchor is not None
# Shape should be [1, num_heads, N_ANCHOR, d_head]
assert k_anchor.shape == (1, 8, 1, 64), f"Unexpected K_anchor shape: {k_anchor.shape}"
assert v_anchor.shape == (1, 8, 1, 64), f"Unexpected V_anchor shape: {v_anchor.shape}"
# Tensors shouldn't be zeroed out entirely
assert torch.sum(torch.abs(k_anchor)).item() > 0.0
finally:
# Restore ledger path
ledger.LEDGER_FILE = original_ledger_file