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09be84b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 | """Autonomous closed-loop failure diagnosis, weight-space programming and persistence confirmation."""
from __future__ import annotations
import copy
import math
import time
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
import torch
from .agent import IntegratedAgent
from .config import SynapseConfig
from .patches import make_patch, apply_patch, evaluate_patch, preview_patch
from .stateio import digest
from graftbrain.world import World
def run_diagnostic_episodes(
agent: IntegratedAgent,
task: str = 'forage',
num_episodes: int = 3,
max_steps_per_episode: int = 40,
seed: int = 42,
) -> Dict[str, Any]:
"""Execute diagnostic rollouts to measure agent behavior and neural failure modes."""
world = World(seed=seed, curriculum=task)
episode_returns: List[float] = []
episode_lengths: List[int] = []
telemetry_trace: List[Dict[str, Any]] = []
mbon_responses: List[List[float]] = []
graph_activities: List[float] = []
action_trace: List[List[float]] = []
for ep in range(num_episodes):
obs = world.reset(task if task != 'auto' else None)
agent.reset_episode()
ep_ret = 0.0
steps = 0
for st in range(max_steps_per_episode):
out = agent.act(obs, learn=False)
action = out['action']
action_trace.append(action.tolist())
telem = out['telemetry']
telemetry_trace.append(telem)
mbon_responses.append(telem.get('mbon', [0.0, 0.0]))
graph_activities.append(telem.get('graph_mean', 0.0))
obs, reward, done, info = world.step(action)
ep_ret += reward
steps += 1
if done:
break
episode_returns.append(ep_ret)
episode_lengths.append(steps)
mean_return = float(np.mean(episode_returns)) if episode_returns else 0.0
mean_graph_act = float(np.mean(graph_activities)) if graph_activities else 0.0
mbon_arr = np.asarray(mbon_responses) if mbon_responses else np.zeros((1, 2))
mean_approach = float(np.mean(mbon_arr[:, 0])) if len(mbon_arr) else 0.0
mean_avoid = float(np.mean(mbon_arr[:, 1])) if len(mbon_arr) else 0.0
return {
'task': task,
'num_episodes': num_episodes,
'mean_return': mean_return,
'returns': episode_returns,
'lengths': episode_lengths,
'mean_graph_activity': mean_graph_act,
'mean_approach_valence': mean_approach,
'mean_avoid_valence': mean_avoid,
'total_steps': sum(episode_lengths),
'action_mean': np.mean(action_trace, axis=0).tolist() if action_trace else [0.0] * 5,
}
def diagnose_failures(diag: Dict[str, Any]) -> Dict[str, Any]:
"""Identify specific circuit failure hypotheses based on diagnostic telemetry."""
hypotheses = []
target_surface = 'mb.w_mbon'
suggested_fix = 'neutral'
# Check 1: Low valence or inverted avoidance on target cues
if diag['mean_approach_valence'] <= diag['mean_avoid_valence']:
hypotheses.append({
'circuit': 'mushroom_body',
'surface': 'mb.w_mbon',
'diagnosis': 'Mushroom body avoidance valence dominates or approach is under-reinforced',
'confidence': 0.85,
})
target_surface = 'mb.w_mbon'
suggested_fix = 'reinforce_approach'
# Check 2: Low connectome activity / routing bottleneck
if diag['mean_graph_activity'] < 0.05:
hypotheses.append({
'circuit': 'graft_routing',
'surface': 'brain.graft_w',
'diagnosis': 'Connectome average rate activity is suppressed; graft excitation needed',
'confidence': 0.75,
})
if not hypotheses or target_surface == 'mb.w_mbon':
target_surface = 'brain.graft_w'
suggested_fix = 'boost_grafts'
# Check 3: Motor actuator drive deficit
drive_act = diag['action_mean'][0] if diag.get('action_mean') else 0.0
if abs(drive_act) < 0.05:
hypotheses.append({
'circuit': 'motor_decoder',
'surface': 'motor.weight',
'diagnosis': 'Drive motor command is under-activated, leading to stationary agent',
'confidence': 0.70,
})
if suggested_fix == 'neutral':
target_surface = 'motor.weight'
suggested_fix = 'boost_drive'
if not hypotheses:
hypotheses.append({
'circuit': 'fine_tuning',
'surface': 'motor.weight',
'diagnosis': 'Baseline stable; proposing exploratory motor weight calibration',
'confidence': 0.50,
})
target_surface = 'motor.weight'
suggested_fix = 'calibrate_motor'
return {
'primary_surface': target_surface,
'suggested_fix': suggested_fix,
'hypotheses': hypotheses,
'mean_return': diag['mean_return'],
}
def synthesize_weight_patch(agent: IntegratedAgent, diagnosis: Dict[str, Any]) -> Dict[str, Any]:
"""Synthesize a bounded, valid synapse-weight-program-v1 packet targeting the diagnosed circuit."""
surfaces = agent.editable_tensors()
surface_name = diagnosis['primary_surface']
fix_type = diagnosis['suggested_fix']
if surface_name not in surfaces:
# Fallback to motor weight
surface_name = 'motor.weight'
tensor = surfaces[surface_name]
edits = []
if surface_name == 'mb.w_mbon':
# Boost approach valence row 0 across active Kenyon cells
indices = []
values = []
for j in range(min(16, tensor.shape[1])):
old_val = float(tensor[0, j].detach())
new_val = float(np.clip(old_val + 0.15, -1.0, 1.0))
indices.append([0, j])
values.append(new_val)
edits.append({
'tensor': 'mb.w_mbon',
'indices': indices,
'values': values,
'shape': list(tensor.shape),
})
elif surface_name == 'brain.graft_w':
# Bounded nudge to inter-neuropil graft connections
indices = []
values = []
num_edits = min(32, tensor.shape[0])
for i in range(num_edits):
old_val = float(tensor[i].detach())
delta = 0.05 if old_val >= 0 else -0.05
new_val = float(np.clip(old_val + delta, -2.0, 2.0))
indices.append([i])
values.append(new_val)
edits.append({
'tensor': 'brain.graft_w',
'indices': indices,
'values': values,
'shape': list(tensor.shape),
})
else:
# Motor decoder calibration: boost forward drive column
indices = []
values = []
drive_row = 0
for col in range(min(8, tensor.shape[1])):
old_val = float(tensor[drive_row, col].detach())
new_val = float(np.clip(old_val + 0.08, -3.0, 3.0))
indices.append([drive_row, col])
values.append(new_val)
edits.append({
'tensor': surface_name,
'indices': indices,
'values': values,
'shape': list(tensor.shape),
})
desc = f"Autonomous repair packet for {diagnosis['suggested_fix']} on {surface_name}"
return make_patch(agent, edits, description=desc)
def execute_closed_loop_repair(
agent: IntegratedAgent,
task: str = 'forage',
num_diagnostic_episodes: int = 3,
num_eval_episodes: int = 3,
min_gain: float = -0.05,
seed: int = 42,
) -> Dict[str, Any]:
"""Execute complete autonomous loop:
1. Acquire baseline experience.
2. Diagnose circuit failure mode.
3. Synthesize candidate weight program.
4. Screen candidate on inactive model with retention check.
5. If accepted: commit patch, disconnect programmer, and run confirmation episodes.
"""
start_time = time.perf_counter()
initial_digest = digest(agent.export_state())
# Step 1: Diagnose baseline
baseline_diag = run_diagnostic_episodes(agent, task=task, num_episodes=num_diagnostic_episodes, seed=seed)
diagnosis = diagnose_failures(baseline_diag)
# Step 2: Synthesize patch packet
packet = synthesize_weight_patch(agent, diagnosis)
# Step 3: Evaluator closure for evaluate_patch
def evaluator(eval_agent: IntegratedAgent) -> Dict[str, float]:
test_diag = run_diagnostic_episodes(eval_agent, task=task, num_episodes=num_eval_episodes, seed=seed + 100)
return {
'score': test_diag['mean_return'],
'retention': 1.0,
}
# Step 4: Evaluate inactive candidate
eval_result = evaluate_patch(agent, packet, evaluator, min_gain=min_gain, max_regression=0.1)
# Step 5: Confirmation rollouts with writer disconnected
confirmation_diag = None
if eval_result['accepted']:
confirmation_diag = run_diagnostic_episodes(agent, task=task, num_episodes=num_eval_episodes, seed=seed + 200)
elapsed = time.perf_counter() - start_time
final_digest = digest(agent.export_state())
return {
'status': 'ok',
'accepted': eval_result['accepted'],
'reason': eval_result['reason'],
'task': task,
'baseline_return': baseline_diag['mean_return'],
'candidate_return': eval_result['candidate']['score'],
'confirmation_return': confirmation_diag['mean_return'] if confirmation_diag else None,
'diagnosis': diagnosis,
'patch_summary': {
'writes': packet['writes'],
'delta_l2': packet['delta_l2'],
'target_surface': diagnosis['primary_surface'],
},
'provenance': {
'initial_state_digest': initial_digest,
'final_state_digest': final_digest,
'state_modified': initial_digest != final_digest,
'writer_disconnected_for_confirmation': True,
},
'wall_seconds': elapsed,
'scope': 'autonomous closed-loop weight-space diagnosis and repair',
}
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