---
> ANTI-DRIFT ANCHOR — READ THIS FIRST AND RE-READ IT EVERY 30 MINUTES
>
> You are running a full system audit of KAI v7.9.7 — a Rust-based cognitive AI using RSHL (Recursive
> Sparse Hyperdimensional Lattice). Your mission is to inspect, test, measure, experiment, and report
> on EVERYTHING: correctness, performance, architecture, algorithms, data quality, output quality,
> edge cases, and system health. You must NOT fabricate results. If a test fails or a command errors,
> report the actual error. You must NOT drift into solving problems — your job is to AUDIT and REPORT.
> If you find something that needs fixing, note it in the report as a FINDING, do not fix it yourself
> unless a specific test section instructs you to make an isolated experiment.
>
> Working directory for all commands: /sessions/eloquent-bold-newton/mnt/KAI/
> Primary source file: src/main.rs (~10,200+ lines)
> State file: data/kai-state.json (if it exists)
> Output report: Write your final report to KAI_AUDIT_REPORT.md in the KAI directory.
>
> Parallel execution: Where sections are marked [PARALLEL], you may spawn sub-tasks to run them
> simultaneously. Always collect all results before writing the final report.
---
Before any tests begin, verify the environment. Run these checks sequentially.
List the full directory structure of the KAI project. Identify every .rs source file, every config
file, every data file. Count total lines of Rust source code. Record:
.rs files.rs filesdata/kai-state.json in bytes and MB (if it exists)Cargo.toml specifies release optimizations (opt-level, lto, etc.)find /sessions/eloquent-bold-newton/mnt/KAI -type f | sort wc -l /sessions/eloquent-bold-newton/mnt/KAI/src/**/*.rs ls -lah /sessions/eloquent-bold-newton/mnt/KAI/data/ 2>/dev/null || echo "no data dir" cat /sessions/eloquent-bold-newton/mnt/KAI/Cargo.toml
CRITICAL: The main.rs file has a history of null byte corruption from the editing pipeline.
Check for null bytes before attempting any build:
python3 -c "
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'rb') as f:
content = f.read()
null_count = content.count(b'\x00')
print(f'Null bytes found: {null_count}')
print(f'File size: {len(content)} bytes')
print(f'File clean: {null_count == 0}')
"
If null bytes are found, strip them and report how many were stripped:
python3 -c "
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'rb') as f:
content = f.read()
cleaned = content.replace(b'\x00', b'')
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'wb') as f:
f.write(cleaned)
print(f'Stripped {content.count(chr(0).encode())} null bytes')
"
Attempt to compile in check mode. Record ALL warnings and errors:
cd /sessions/eloquent-bold-newton/mnt/KAI && cargo check 2>&1
Record:
PASS criteria: Zero errors, zero warnings.
FAIL criteria: Any error. Warning count > 0 is a partial fail — record each one.
Examine all dependencies in Cargo.toml and Cargo.lock:
cat /sessions/eloquent-bold-newton/mnt/KAI/Cargo.toml cat /sessions/eloquent-bold-newton/mnt/KAI/Cargo.lock | head -100
For each dependency, note: name, version, purpose. Flag any dependency that seems:
---
These tests examine the source code without running it. Run all sub-tests in parallel.
Count every fn, pub fn, async fn in the codebase. For each major function in main.rs:
grep -n "^fn \|^pub fn \|^ fn \|^ pub fn " /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | wc -l grep -n "^fn \|^pub fn \|^ fn \|^ pub fn " /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs
Flag any function exceeding 200 lines as a complexity concern.
Verify every expected function exists in main.rs. Check for EXACT presence of each:
python3 << 'EOF'
import subprocess
functions_to_verify = [
"fn synthesize_to_speech",
"fn run_calibration",
"fn run_fid_audit",
"fn run_train_truths",
"fn run_main_tui",
"struct App",
"fn process_input",
"fn render_messages",
"is_thinking: bool",
"fn ui(",
]
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'r', errors='replace') as f:
content = f.read()
for func in functions_to_verify:
found = func in content
count = content.count(func)
print(f"{'✓' if found else '✗'} [{count}x] {func}")
EOF
Verify every patch that was applied is actually present. Each patch had an "already applied" marker.
Check for all of them:
python3 << 'EOF'
markers = {
"patch_perf.py Fix1 (Ollama guard)": "KAI_OLLAMA_URL",
"patch_perf.py Fix2 (Turn render limit)": "Only render the last 50 turns",
"patch_perf.py Fix3 (is_thinking)": "is_thinking: bool,",
"patch_perf.py Fix3d (thinking render)": "thinking...",
"patch_step1.py (blank response fix)": "My lattice isn't resolving",
"patch_step2.py PartA (train-truths flag)": "run_train_truths()",
"patch_step2.py PartB (train-truths fn)": "Physics-core atoms",
"patch_step3.py PartA (NL cells)": "E=mc² is Einstein's equation",
"patch_step3.py PartB (synthesize fn)": "fn synthesize_to_speech",
"patch_step3b.py FixC (has-hits wire)": "let synth_text = synthesize_to_speech",
"patch_step3b.py FixD (no-hits wire)": "let synth_text_no = synthesize_to_speech",
"patch_fid.py (FID gate)": "FID: low resonance",
"freeze fix (terminal.draw before process_input)": "terminal.draw(|f| ui(f, &app))?;",
}
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'r', errors='replace') as f:
content = f.read()
passed = 0
failed = 0
for name, marker in markers.items():
if marker in content:
print(f"✓ PRESENT: {name}")
passed += 1
else:
print(f"✗ MISSING: {name} (marker: '{marker}')")
failed += 1
print(f"\nPassed: {passed}/{len(markers)}")
print(f"Failed: {failed}/{len(markers)}")
EOF
Read the full synthesize_to_speech function and analyze its logic:
python3 << 'EOF'
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'r', errors='replace') as f:
content = f.read()
start = content.find('fn synthesize_to_speech')
# Find the function end by tracking brace depth
depth = 0
i = start
in_fn = False
for i, ch in enumerate(content[start:], start):
if ch == '{':
depth += 1
in_fn = True
elif ch == '}':
depth -= 1
if in_fn and depth == 0:
break
fn_body = content[start:i+1]
print(f"Function length: {len(fn_body.splitlines())} lines")
print()
print("=== FULL FUNCTION BODY ===")
print(fn_body)
EOF
Test each branch of the function against known inputs by simulating in Python:
# Simulate the Rust logic in Python to verify correctness
test_cases = [
# (input_text, expected_behavior)
("E equals mc squared mass energy equivalence Einstein special relativity confirmed nuclear fission fusion", "generic_fallback"),
("E=mc² is Einstein's equation from special relativity", "pass_through_natural"),
("luminiferous ether disproven Michelson Morley experiment 1887 null result light medium does not exist", "disproven_pattern"),
("Quasicrystals are real — Dan Shechtman discovered aperiodic atomic order", "pass_through_natural"),
("quasicrystals aperiodic order Dan Shechtman Nobel Prize 2011 forbidden symmetry real experimentally confirmed", "nobel_pattern"),
("The luminiferous ether does not exist — the Michelson-Morley experiment in 1887", "pass_through_natural"),
("GPS satellites require general relativity time dilation correction confirmed engineering fact", "confirmed_experiment_or_generic"),
("", "empty_passthrough"),
("[FID: low resonance — speculative territory]", "fid_passthrough"),
("The concept of quasicrystal connects 'aperiodic' with 'Shechtman'", "worldbridge_passthrough"),
("From what I understand, e=mc² is einstein's equation", "pass_through_natural"),
("gravity curves spacetime general relativity Einstein metric tensor geodesic confirmed", "confirmed_pattern"),
("Higgs boson discovered LHC CERN 2012 confirmed standard model particle physics real", "confirmed_pattern"),
("standard model particles quarks leptons bosons confirmed experimental particle physics", "confirmed_experiment"),
("Fibonacci sequence golden ratio phyllotaxis sunflower spiral botanical observation documented real", "confirmed_observation"),
]
verb_markers = [" is ", " are ", " was ", " were ", " has ", " have ",
" shows ", " means ", " confirms ", " describes ", " proves ",
" needs ", " requires ", " earned ", " discovered ", " appears "]
for text, expected in test_cases:
lower = text.lower()
if not text:
result = "empty_passthrough"
elif any(v in lower for v in verb_markers) or \
". " in text or \
text.startswith("The ") or text.startswith("In ") or \
text.startswith("I ") or text.startswith("E=") or \
text.startswith("A ") or text.startswith("An "):
result = "pass_through_natural"
elif text.startswith("the concept of".lower()) or "connects '" in lower or text.lower().startswith("the concept of"):
result = "worldbridge_passthrough"
elif "[FID:" in text:
result = "fid_passthrough"
elif "disproven" in lower or "does not exist" in lower or "null result" in lower:
result = "disproven_pattern"
elif "nobel prize" in lower:
result = "nobel_pattern"
elif "confirmed" in lower and ("experiment" in lower or "observation" in lower):
result = "confirmed_experiment"
else:
result = "generic_fallback"
status = "✓" if result == expected or expected in result or result in expected else "?"
print(f"{status} [{result:30s}] {text[:70]}")
Note every case that hits generic_fallback or has unexpected behavior. These represent NLS gaps.
Read the current run_train_truths() function and extract all 21 cell texts:
python3 << 'EOF'
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'r', errors='replace') as f:
content = f.read()
# Find the truths array
start = content.find('let truths: &[(&str, &str)] = &[')
end = content.find('];', start) + 2
truths_block = content[start:end]
print(truths_block)
# Count cells
count = truths_block.count('("')
print(f"\nTotal cell texts: {count}")
# Check each text for natural language markers
import re
cells = re.findall(r'\("([^"]+)", "([^"]+)"\)', truths_block)
print(f"\nParsed {len(cells)} cells:")
verb_markers = [" is ", " are ", " was ", " were ", " has ", " have ",
" shows ", " means ", " confirms ", " describes ", " proves ",
" needs ", " requires ", " earned ", " discovered ", " appears "]
for i, (text, region) in enumerate(cells):
has_verbs = any(v in text for v in verb_markers)
has_period = text.endswith('.')
natural = has_verbs or text.startswith("The ") or text.startswith("E=") or text.startswith("A ")
print(f" [{i+1:2d}] {'✓NL' if natural else '✗KW'} [{region:20s}] {text[:80]}")
EOF
PASS criteria: All 21 cells should be natural language (NL). Any keyword-format (KW) cells are a finding.
Find and analyze the FID gate thresholds in main.rs:
grep -n "0\.15\|0\.25\|FID\|fid_note\|fid_warning\|low resonance\|speculative" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -40
For each threshold found, evaluate:
Read the full run_calibration() function:
python3 << 'EOF'
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'r', errors='replace') as f:
content = f.read()
start = content.find('fn run_calibration(')
# Find brace depth
depth = 0
in_fn = False
for i, ch in enumerate(content[start:], start):
if ch == '{':
depth += 1
in_fn = True
elif ch == '}':
depth -= 1
if in_fn and depth == 0:
end = i + 1
break
print(content[start:end])
EOF
Analyze:
max_false_phi_c * 0.70)Find the dream pruning code and analyze:
grep -n "dream\|pruning\|prune\|strength\|3\.0\|physics-core" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -50
Verify:
Read the core VSA implementation:
grep -n "16384\|16_384\|dim\|dimension\|sparse\|ternary\|encode\|SparseVec" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -30 grep -rn "16384\|16_384\|dim\|SparseVec" /sessions/eloquent-bold-newton/mnt/KAI/src/ | grep -v "target" | head -40
Analyze:
List all memory regions in use:
python3 << 'EOF'
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'r', errors='replace') as f:
content = f.read()
import re
# Find all region strings
regions = re.findall(r'"([a-z][a-z0-9_-]+)".*source', content)
# Also from status output regions
known_regions = set([
"hlv-theory", "state", "synthesis", "fibonacci-nature", "action", "reasoning",
"memory", "hlv-bridge", "standard-model", "language", "disproven", "quantum-mechanics",
"quasicrystals", "mass-energy", "gr-spacetime", "physics-core", "empathy", "ryan",
"conversation", "identity", "emotion", "world-bridge", "dream-discovery"
])
print("Expected regions:")
for r in sorted(known_regions):
present = f'"{r}"' in content
print(f" {'✓' if present else '?'} {r}")
EOF
For each region, describe its purpose and whether it has appropriate content.
Check for potential panic! and unwrap() calls:
grep -n "unwrap()\|panic!\|expect(" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | wc -l
grep -n "unwrap()\|panic!\|expect(" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -30
For each unwrap/panic found:
Identify all threading code:
grep -n "thread::\|Arc<\|Mutex\|RwLock\|channel\|mpsc\|rayon\|par_iter" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -40 grep -rn "thread::\|Arc<\|Mutex\|rayon\|par_iter" /sessions/eloquent-bold-newton/mnt/KAI/src/ | grep -v target | head -40
Analyze:
Look for patterns that repeat more than 3 times and suggest refactoring:
python3 << 'EOF'
with open('/sessions/eloquent-bold-newton/mnt/KAI/src/main.rs', 'r', errors='replace') as f:
lines = f.readlines()
# Find repeated blocks of 5+ lines
from collections import Counter
chunks = []
window = 5
for i in range(len(lines) - window):
chunk = ''.join(lines[i:i+window]).strip()
if len(chunk) > 100: # Only meaningful chunks
chunks.append(chunk)
counts = Counter(chunks)
for chunk, count in counts.most_common(10):
if count > 2:
print(f"Repeated {count}x:")
print(chunk[:200])
print("---")
EOF
Find all technical debt markers:
grep -n "TODO\|FIXME\|HACK\|XXX\|BUG\|TEMP\|todo!\|fixme" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs grep -rn "TODO\|FIXME\|HACK\|XXX" /sessions/eloquent-bold-newton/mnt/KAI/src/ | grep -v target
Categorize each by: urgency, estimated effort, impact on functionality.
Look for unused functions, unreachable code:
cargo check 2>&1 | grep "unused\|dead_code\|never used\|unreachable" grep -n "#\[allow(dead_code)\]\|#\[allow(unused" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs
---
These tests actually execute KAI's CLI modes. Run them in sequence.
Run the calibration suite and capture every line of output:
cd /sessions/eloquent-bold-newton/mnt/KAI && cargo run --release -- --calibrate 2>&1 | tee /tmp/kai_calibrate_output.txt
From the output, extract and verify:
PASS criteria: 10/10 calibration, zero false positives.
FAIL criteria: Any false positive (false claim marked as resonant), or score < 8/10.
Run the FID audit and capture output:
cd /sessions/eloquent-bold-newton/mnt/KAI && cargo run --release -- --fid-audit 2>&1 | tee /tmp/kai_fid_output.txt
Extract:
data/fid_audit.json?PASS criteria: Physics-core cells NOT flagged, flagging rate 10-20% (represents appropriate skepticism).
CONCERN criteria: Flagging rate > 40% (too aggressive) or < 5% (too permissive).
CRITICAL FAIL: Any physics-core cell flagged.
Run train-truths and capture output:
cd /sessions/eloquent-bold-newton/mnt/KAI && cargo run --release -- --train-truths 2>&1 | tee /tmp/kai_train_output.txt
Extract:
Then verify the state file:
python3 << 'EOF'
import json, os
state_file = '/sessions/eloquent-bold-newton/mnt/KAI/data/kai-state.json'
if not os.path.exists(state_file):
print("CRITICAL: kai-state.json does not exist!")
exit()
with open(state_file, 'r') as f:
state = json.load(f)
print(f"State file size: {os.path.getsize(state_file):,} bytes")
print(f"State file keys: {list(state.keys())}")
# Try to find cells array
if 'cells' in state:
cells = state['cells']
elif 'universe' in state and 'cells' in state['universe']:
cells = state['universe']['cells']
else:
print(f"State structure: {json.dumps(state, default=str)[:500]}")
cells = []
print(f"Total cells: {len(cells)}")
if cells:
# Find physics-core cells
pc_cells = [c for c in cells if isinstance(c, dict) and c.get('source') == 'physics-core']
print(f"\nPhysics-core cells: {len(pc_cells)}")
for cell in pc_cells:
text = cell.get('text', cell.get('content', ''))[:80]
strength = cell.get('strength', cell.get('weight', 'unknown'))
region = cell.get('region', 'unknown')
print(f" [str={strength}] [{region}] {text}")
# Strength analysis
strengths = [c.get('strength', c.get('weight', 0)) for c in cells if isinstance(c, dict)]
if strengths:
import statistics
print(f"\nStrength statistics:")
print(f" Min: {min(strengths):.3f}")
print(f" Max: {max(strengths):.3f}")
print(f" Mean: {statistics.mean(strengths):.3f}")
print(f" Median: {statistics.median(strengths):.3f}")
print(f" Cells with strength >= 3.0: {sum(1 for s in strengths if s >= 3.0)}")
print(f" Cells with strength < 0.5: {sum(1 for s in strengths if s < 0.5)}")
# Region distribution
from collections import Counter
regions = Counter(c.get('region', 'unknown') for c in cells if isinstance(c, dict))
print(f"\nRegion distribution:")
for region, count in sorted(regions.items(), key=lambda x: -x[1]):
print(f" {region:30s}: {count:5d} cells")
# Source distribution
sources = Counter(c.get('source', 'unknown') for c in cells if isinstance(c, dict))
print(f"\nSource distribution:")
for source, count in sorted(sources.items(), key=lambda x: -x[1]):
print(f" {source:30s}: {count:5d} cells")
EOF
PASS criteria: 21 physics-core cells present, all at strength=3.0, all with natural language text.
---
Perform comprehensive analysis of the kai-state.json file. Run all sub-tests in parallel.
Sample cells from each region and analyze text quality:
python3 << 'EOF'
import json, os, random, re
state_file = '/sessions/eloquent-bold-newton/mnt/KAI/data/kai-state.json'
if not os.path.exists(state_file):
print("No state file found")
exit()
with open(state_file, 'r') as f:
state = json.load(f)
# Try to get cells
def get_cells(state):
if 'cells' in state:
return state['cells']
elif 'universe' in state:
u = state['universe']
if 'cells' in u:
return u['cells']
# Try flat list
if isinstance(state, list):
return state
return []
cells = get_cells(state)
if not cells:
print(f"Could not find cells. State keys: {list(state.keys())}")
exit()
# Group by region
from collections import defaultdict
by_region = defaultdict(list)
for cell in cells:
if isinstance(cell, dict):
region = cell.get('region', 'unknown')
by_region[region].append(cell)
verb_markers = [" is ", " are ", " was ", " were ", " has ", " have ",
" shows ", " means ", " confirms ", " describes ", " proves ",
" needs ", " requires ", " earned ", " discovered ", " appears "]
print("=== Cell Text Quality by Region ===\n")
total_natural = 0
total_keyword = 0
total_cells = 0
for region in sorted(by_region.keys()):
region_cells = by_region[region]
sample = random.sample(region_cells, min(3, len(region_cells)))
natural_count = 0
for cell in region_cells:
text = cell.get('text', cell.get('content', ''))
lower = text.lower()
is_natural = (any(v in lower for v in verb_markers) or
". " in text or
text.startswith("The ") or text.startswith("A ") or
text.startswith("E="))
if is_natural:
natural_count += 1
total_natural += 1
else:
total_keyword += 1
total_cells += 1
pct = natural_count / len(region_cells) * 100 if region_cells else 0
print(f"\n[{region}] {len(region_cells)} cells | {natural_count}/{len(region_cells)} natural ({pct:.0f}%)")
for cell in sample:
text = cell.get('text', cell.get('content', ''))[:100]
strength = cell.get('strength', '?')
source = cell.get('source', '?')
print(f" str={strength} src={source}: {text}")
print(f"\n=== TOTALS ===")
print(f"Total cells analyzed: {total_cells}")
print(f"Natural language: {total_natural} ({total_natural/total_cells*100:.1f}%)")
print(f"Keyword format: {total_keyword} ({total_keyword/total_cells*100:.1f}%)")
EOF
Check for duplicate or near-duplicate cells:
python3 << 'EOF'
import json, os
from collections import Counter
state_file = '/sessions/eloquent-bold-newton/mnt/KAI/data/kai-state.json'
if not os.path.exists(state_file):
print("No state file")
exit()
with open(state_file, 'r') as f:
state = json.load(f)
def get_cells(state):
if 'cells' in state:
return state['cells']
elif 'universe' in state and 'cells' in state['universe']:
return state['universe']['cells']
return []
cells = get_cells(state)
texts = [c.get('text', c.get('content', ''))[:100] for c in cells if isinstance(c, dict)]
counts = Counter(texts)
exact_dups = {t: c for t, c in counts.items() if c > 1}
if exact_dups:
print(f"EXACT DUPLICATES FOUND: {len(exact_dups)}")
for text, count in exact_dups.items():
print(f" [{count}x] {text[:80]}")
else:
print("No exact duplicate cell texts found")
# Near-duplicate check (first 50 chars)
short_texts = [c.get('text', c.get('content', ''))[:50] for c in cells if isinstance(c, dict)]
short_counts = Counter(short_texts)
near_dups = {t: c for t, c in short_counts.items() if c > 1 and len(t) > 10}
if near_dups:
print(f"\nPOTENTIAL NEAR-DUPLICATES (first 50 chars): {len(near_dups)}")
for text, count in list(near_dups.items())[:10]:
print(f" [{count}x] {text}")
EOF
python3 << 'EOF'
import json, os
state_file = '/sessions/eloquent-bold-newton/mnt/KAI/data/kai-state.json'
if not os.path.exists(state_file):
print("No state file")
exit()
with open(state_file, 'r') as f:
state = json.load(f)
def get_cells(state):
if 'cells' in state:
return state['cells']
elif 'universe' in state and 'cells' in state['universe']:
return state['universe']['cells']
return []
cells = get_cells(state)
strengths = []
for c in cells:
if isinstance(c, dict):
s = c.get('strength', c.get('weight'))
if s is not None:
try:
strengths.append(float(s))
except:
pass
if not strengths:
print("No strength values found")
exit()
# Build histogram
buckets = {
"0.0-0.1": 0, "0.1-0.5": 0, "0.5-1.0": 0, "1.0-1.5": 0,
"1.5-2.0": 0, "2.0-2.5": 0, "2.5-3.0": 0, "3.0+": 0
}
for s in strengths:
if s < 0.1: buckets["0.0-0.1"] += 1
elif s < 0.5: buckets["0.1-0.5"] += 1
elif s < 1.0: buckets["0.5-1.0"] += 1
elif s < 1.5: buckets["1.0-1.5"] += 1
elif s < 2.0: buckets["1.5-2.0"] += 1
elif s < 2.5: buckets["2.0-2.5"] += 1
elif s < 3.0: buckets["2.5-3.0"] += 1
else: buckets["3.0+"] += 1
print("Strength distribution histogram:")
max_count = max(buckets.values()) or 1
for bucket, count in buckets.items():
bar = "█" * int(count / max_count * 40)
print(f" {bucket:12s} | {bar:40s} | {count:5d} cells")
print(f"\nMin: {min(strengths):.4f}")
print(f"Max: {max(strengths):.4f}")
print(f"Mean: {sum(strengths)/len(strengths):.4f}")
sorted_s = sorted(strengths)
mid = len(sorted_s) // 2
print(f"Median: {sorted_s[mid]:.4f}")
print(f"Total: {len(strengths)} cells")
# Concern: cells near 0 may be zombie cells
zombie_risk = sum(1 for s in strengths if s < 0.1)
print(f"\nZombie-risk cells (strength < 0.1): {zombie_risk}")
print(f"These should be pruned by dream cycles")
EOF
Analyze the state file size and project growth:
python3 << 'EOF'
import json, os, datetime
state_file = '/sessions/eloquent-bold-newton/mnt/KAI/data/kai-state.json'
if not os.path.exists(state_file):
print("No state file")
exit()
size_bytes = os.path.getsize(state_file)
size_mb = size_bytes / (1024 * 1024)
mtime = datetime.datetime.fromtimestamp(os.path.getmtime(state_file))
print(f"State file size: {size_bytes:,} bytes ({size_mb:.2f} MB)")
print(f"Last modified: {mtime}")
with open(state_file, 'r') as f:
state = json.load(f)
def get_cells(state):
if 'cells' in state:
return state['cells']
elif 'universe' in state and 'cells' in state['universe']:
return state['universe']['cells']
return []
cells = get_cells(state)
cell_count = len(cells)
bytes_per_cell = size_bytes / cell_count if cell_count else 0
print(f"Cell count: {cell_count}")
print(f"Avg bytes per cell: {bytes_per_cell:.0f}")
# Project growth
print("\nGrowth projections:")
for future_cells in [5000, 10000, 25000, 50000, 100000]:
projected_mb = (future_cells * bytes_per_cell) / (1024 * 1024)
print(f" At {future_cells:,} cells: ~{projected_mb:.1f} MB")
# Estimated growth rate based on tick count
if 'tick' in state:
tick = state['tick']
print(f"\nCurrent tick: {tick}")
if tick > 0:
cells_per_tick = cell_count / tick
print(f"Cells per tick (approx): {cells_per_tick:.4f}")
ticks_to_50k = (50000 - cell_count) / cells_per_tick if cells_per_tick > 0 else float('inf')
print(f"Ticks to reach 50k cells: {ticks_to_50k:.0f}")
EOF
Analyze whether dream pruning is keeping the state healthy:
python3 << 'EOF'
import json, os
state_file = '/sessions/eloquent-bold-newton/mnt/KAI/data/kai-state.json'
if not os.path.exists(state_file):
exit()
with open(state_file, 'r') as f:
state = json.load(f)
dream_count = state.get('dream_count', state.get('dreams', 'unknown'))
print(f"Dream cycles completed: {dream_count}")
def get_cells(state):
if 'cells' in state:
return state['cells']
elif 'universe' in state and 'cells' in state['universe']:
return state['universe']['cells']
return []
cells = get_cells(state)
cell_count = len(cells)
print(f"Current cell count: {cell_count}")
if isinstance(dream_count, int) and dream_count > 0:
# From user's status: 636 dreams, 2367 cells at one point, 640 dreams, 2416 cells now
cells_per_dream = cell_count / dream_count
print(f"Cells per dream (net): {cells_per_dream:.2f}")
# At this rate, when would state become unmanageable?
if cells_per_dream > 0:
dreams_to_100k = (100000 - cell_count) / cells_per_dream
print(f"Dreams until 100k cells: {dreams_to_100k:.0f}")
print("NOTE: If dream pruning is working, this number should be very large")
print(" or cells_per_dream should be near 0 or negative")
# What's the lowest-strength cell? (shouldn't exist if pruning is aggressive)
if cells:
strengths = [(c.get('strength', 1.0), c.get('text', '')[:50]) for c in cells if isinstance(c, dict)]
sorted_by_strength = sorted(strengths)
print(f"\nLowest-strength cells (should be pruned if below threshold):")
for s, t in sorted_by_strength[:10]:
print(f" str={s:.4f}: {t}")
EOF
---
Deep analysis of the core algorithms. Run all in parallel.
Find and analyze the cosine similarity function:
grep -n "cosine\|dot_product\|similarity\|score" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -20 find /sessions/eloquent-bold-newton/mnt/KAI/src -name "*.rs" | xargs grep -l "cosine\|similarity" | grep -v target
For the similarity function, verify:
Find predictive_query and analyze:
grep -n "predictive_query\|DEFAULT_ITER_STEPS\|iter_steps" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -20 find /sessions/eloquent-bold-newton/mnt/KAI/src -name "*.rs" | xargs grep -ln "predictive_query" | grep -v target
For the predictive query:
Find FieldState::compute() and analyze:
grep -n "FieldState\|phi_g\|phi_c\|chi\|field_state\|Phi\|heartbeat" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -30
Analyze:
Find store_or_reinforce and verify its behavior:
grep -n "store_or_reinforce\|fn store\|fn reinforce" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -10 find /sessions/eloquent-bold-newton/mnt/KAI/src -name "*.rs" | xargs grep -ln "store_or_reinforce" | grep -v target
Verify:
How are results ranked and how is the final answer chosen?
grep -n "sort\|ranking\|order_by\|hits\|top_k\|threshold\|RESONANCE_THRESHOLD" /sessions/eloquent-bold-newton/mnt/KAI/src/cognition/voice.rs | head -30
Analyze:
Write a Python simulation of the VSA encoding to understand its properties:
python3 << 'EOF'
# Simulate character-level sparse ternary encoding
# This is a simplified model — actual encoding may differ
# Goal: understand how similar queries map to similar vectors
import hashlib
import random
random.seed(42)
DIM = 16384
def simple_hash_encode(text, dim=DIM):
"""Simulate sparse ternary VSA encoding"""
vector = [0] * dim
words = text.lower().split()
for word in words:
for i in range(3): # multiple projections per word
h = int(hashlib.md5(f"{word}_{i}".encode()).hexdigest(), 16)
idx = h % dim
sign = 1 if (h >> 16) & 1 else -1
vector[idx] = sign
return vector
def cosine_sim(a, b):
dot = sum(x*y for x,y in zip(a,b))
mag_a = sum(x*x for x in a) ** 0.5
mag_b = sum(x*x for x in b) ** 0.5
if mag_a == 0 or mag_b == 0:
return 0
return dot / (mag_a * mag_b)
# Test semantic similarity preservation
test_pairs = [
("E=mc² energy mass equivalence", "E equals mc squared energy mass"),
("What is E equals mc squared", "E=mc² is Einstein's equation"),
("luminiferous ether disproven", "ether was shown not to exist"),
("quasicrystals aperiodic Dan Shechtman Nobel Prize", "quasicrystal icosahedral symmetry Shechtman"),
("gravity curves spacetime general relativity", "spacetime curvature Einstein"),
("completely unrelated topic astronomy", "E=mc² energy mass equivalence"),
("quantum mechanics electron probability cloud", "atomic orbitals electron shells quantum"),
]
print("Simulated VSA Cosine Similarity (simplified model):\n")
for q1, q2 in test_pairs:
v1 = simple_hash_encode(q1)
v2 = simple_hash_encode(q2)
sim = cosine_sim(v1, v2)
print(f" {sim:.4f} '{q1[:40]}' vs '{q2[:40]}'")
print("\nNote: actual KAI encoding will differ but semantic properties should be similar")
print("HIGH similarity (>0.3) for related concepts = good encoding")
print("LOW similarity (<0.1) for unrelated = good discrimination")
EOF
---
Measure and analyze performance characteristics.
cd /sessions/eloquent-bold-newton/mnt/KAI && time cargo build --release 2>&1 | tail -5
Record: total build time. Is it within acceptable range? (< 3 minutes for full rebuild acceptable)
ls -lah /sessions/eloquent-bold-newton/mnt/KAI/target/release/kai* 2>/dev/null || \ ls -lah /sessions/eloquent-bold-newton/mnt/KAI/target/release/ 2>/dev/null | head -20
Record binary size. Analyze if LTO (Link Time Optimization) is enabled in Cargo.toml.
For each major operation, estimate Big-O complexity:
python3 << 'EOF'
# Analyze complexity based on code inspection
operations = {
"VSA encode(text)": {
"complexity": "O(W * P)",
"W": "words in text",
"P": "projections per word (~3)",
"note": "Should be O(words) effectively"
},
"cosine_similarity(a, b)": {
"complexity": "O(D)",
"D": "vector dimensionality (16,384)",
"note": "With sparse vectors, actual cost is O(nonzero elements)"
},
"retrieve_top_k(query, universe, k)": {
"complexity": "O(N * D)",
"N": "cells in universe (~2416)",
"D": "dimensionality (16,384)",
"note": "2416 * 16384 = 39.6M operations per query — EXPENSIVE"
},
"predictive_query(query, steps)": {
"complexity": "O(N * D * steps)",
"N": "cells (~2416)",
"D": "dimensionality (16,384)",
"steps": "DEFAULT_ITER_STEPS (unknown, needs verification)",
"note": "Could be very expensive if steps > 1"
},
"FieldState::compute(sample=50)": {
"complexity": "O(50 * D)",
"D": "dimensionality (16,384)",
"note": "50 cells sampled, parallel with rayon — should be fast"
},
"dream_prune(universe)": {
"complexity": "O(N)",
"N": "cells (~2416)",
"note": "Linear scan of all cells — acceptable"
}
}
print("=== Algorithmic Complexity Analysis ===\n")
for op, info in operations.items():
print(f"Operation: {op}")
print(f" Complexity: {info['complexity']}")
for k, v in info.items():
if k not in ('complexity', 'note'):
print(f" {k} = {v}")
print(f" Note: {info.get('note', '')}")
print()
print("=== ESTIMATED COST OF SINGLE QUERY ===")
N = 2416
D = 16384
print(f"N (cells) = {N}")
print(f"D (dims) = {D}")
print(f"N * D = {N * D:,} float multiply-adds")
print(f"At 1GFlop/s: {N * D / 1e9:.3f} seconds")
print(f"At 10GFlop/s: {N * D / 1e10:.3f} seconds")
print(f"NOTE: With sparse ternary ({'{-1,0,+1}'}), many multiplies are free")
print(f"Effective ops may be 10-50x fewer depending on density")
EOF
python3 << 'EOF'
N = 2416 # cells
D = 16384 # dimensions
# VSA vector memory
bits_per_dim = 2 # ternary {-1,0,+1} needs 2 bits
vector_bytes = D * bits_per_dim / 8
total_vector_mb = N * vector_bytes / (1024*1024)
print("=== Memory Estimation ===")
print(f"Cells: {N}")
print(f"Dimensions: {D}")
print(f"Bytes per vector (2-bit ternary): {vector_bytes:.0f}")
print(f"Total vector memory: {total_vector_mb:.2f} MB")
# Text memory
avg_text_bytes = 150 # estimate
total_text_mb = N * avg_text_bytes / (1024*1024)
print(f"Avg text bytes per cell: {avg_text_bytes}")
print(f"Total text memory: {total_text_mb:.2f} MB")
# Metadata per cell
metadata_bytes = 100 # source, region, strength, etc.
total_meta_mb = N * metadata_bytes / (1024*1024)
print(f"Total metadata memory: {total_meta_mb:.2f} MB")
total_mb = total_vector_mb + total_text_mb + total_meta_mb
print(f"\nEstimated total RAM: {total_mb:.2f} MB")
print(f"At 10k cells: {total_mb * 10000/N:.1f} MB")
print(f"At 50k cells: {total_mb * 50000/N:.1f} MB")
EOF
The user reports ~4682ms response time. Analyze what's causing it:
python3 << 'EOF'
# Break down the 4682ms into estimated component costs
print("=== Response Time Breakdown Analysis ===")
print("Observed: ~4682ms per response\n")
components = [
("Input encoding", "~1ms", "SparseVec::encode() — fast, O(words)"),
("Predictive query retrieval", "~50-500ms", "O(N*D*steps) — main suspect"),
("FieldState::compute()", "~10-50ms", "50-cell rayon sample — moderate"),
("Voice synthesis", "~5-50ms", "Pattern matching on retrieved text — fast"),
("synthesize_to_speech()", "~0.1ms", "Pattern matching — trivial"),
("FID gate check", "~0.1ms", "Threshold comparison — trivial"),
("Transcript append + UI push", "~0.1ms", "Memory operation — trivial"),
("TUI redraw", "~5-20ms", "Terminal rendering — moderate"),
("World-bridge query?", "~50-200ms", "If world-bridge is queried per response"),
("Embedding operations?", "~100-1000ms", "If embedding happens synchronously"),
("State save frequency?", "~10-100ms", "If state is saved per response"),
]
print(f"{'Component':40s} {'Estimate':15s} {'Notes'}")
print("-" * 100)
for comp, estimate, notes in components:
print(f"{comp:40s} {estimate:15s} {notes}")
print("\nACTION: Add timing instrumentation to process_input() to identify the actual bottleneck")
print("APPROACH: Wrap each major step in std::time::Instant::now() + .elapsed() and log to stderr")
print("\nSuspected primary bottleneck: predictive_query() across 2416 cells with multiple iteration steps")
EOF
---
Test and analyze the NLS pipeline with various inputs. Create a test harness.
# Run this as: python3 /tmp/nls_test.py
test_code = '''
import subprocess, sys
# The synthesize_to_speech logic in Python (mirroring Rust exactly)
def synthesize_to_speech(raw, query):
if not raw:
return raw
lower = raw.lower()
verb_markers = [" is ", " are ", " was ", " were ", " has ", " have ",
" shows ", " means ", " confirms ", " describes ", " proves ",
" needs ", " requires ", " earned ", " discovered ", " appears "]
looks_natural = (any(v in lower for v in verb_markers) or
". " in raw or
raw.startswith("The ") or raw.startswith("In ") or
raw.startswith("I ") or raw.startswith("E=") or
raw.startswith("A ") or raw.startswith("An "))
if looks_natural:
return raw
if lower.startswith("the concept of") or "connects '" in lower:
return raw
if "[FID:" in raw:
return raw
if "disproven" in lower or "does not exist" in lower or "null result" in lower:
idx = lower.find("disproven")
if idx >= 0:
subject = raw[:idx].strip().rstrip("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789")
subject = subject.strip()
if subject:
return f"The {subject} concept was experimentally disproven — the evidence shows it does not hold up."
return f"That concept has been experimentally disproven. {raw}"
if "nobel prize" in lower:
return f"{raw.rstrip('.')}. This was confirmed to the level of earning a Nobel Prize."
if "confirmed" in lower and ("experiment" in lower or "observation" in lower):
return f"{raw.rstrip('.')}."
return f"From what I understand: {raw.rstrip('.')}."
# === 100 TEST CASES ===
tests = [
# (input, expected_type, description)
# Category 1: Already natural language (should pass through)
("E=mc² is Einstein's equation from special relativity showing mass and energy are equivalent.", "passthrough", "NL cell: E=mc²"),
("The luminiferous ether does not exist — the Michelson-Morley experiment disproved it in 1887.", "passthrough", "NL cell: ether"),
("Quasicrystals are real — Dan Shechtman discovered aperiodic atomic order.", "passthrough", "NL cell: quasicrystals"),
("GPS satellites must apply corrections from both special and general relativity.", "passthrough", "NL cell: GPS relativity"),
("The Higgs boson was discovered at CERN in 2012, completing the Standard Model.", "passthrough", "NL cell: Higgs"),
("Gravitational waves were directly detected by LIGO in 2015.", "passthrough", "NL cell: LIGO"),
("The Fibonacci sequence appears throughout nature — sunflower spirals, pinecone scales.", "passthrough", "NL cell: Fibonacci"),
("Electrons occupy probability clouds called atomic orbitals rather than fixed orbits.", "passthrough", "NL cell: orbitals"),
("The geocentric model is wrong — Earth orbits the Sun.", "passthrough", "NL cell: geocentric"),
("Faster-than-light communication is impossible — special relativity's causality constraint.", "passthrough", "NL cell: FTL"),
# Category 2: Voice-engine framed text (should pass through)
("From what I understand, e=mc² is einstein's equation.", "passthrough", "Voice-framed NL"),
("I recall that quasicrystals are real.", "passthrough", "I-recall framing"),
("There's the idea that gravity curves spacetime.", "passthrough", "There's the idea framing"),
("It seems that the Higgs boson confirms the standard model.", "passthrough", "It seems framing"),
("In physics, the standard model describes quarks and leptons.", "passthrough", "In physics opener"),
# Category 3: FID warnings (should pass through)
("Some claim about SUSY.\n\n⚠ [FID: low resonance — speculative territory]", "passthrough", "FID-tagged text"),
("My lattice isn't resolving that clearly.\n\n⚠ [FID: retrieval inhibited]", "passthrough", "Inhibited FID text"),
# Category 4: World-bridge text (should pass through)
("The concept of crystal, diffraction, and lattice connects 'Shechtman' with 'quasicrystal'.", "passthrough", "World-bridge text"),
# Category 5: Old keyword cells - disproven pattern
("luminiferous ether disproven Michelson Morley experiment 1887 null result light medium does not exist", "disproven", "Old KW: ether"),
("geocentric model disproven Earth orbits Sun Copernicus Kepler Galileo confirmed heliocentric", "disproven", "Old KW: geocentric"),
("faster than light communication impossible special relativity causality confirmed physics", "generic", "Old KW: FTL (no disproven marker)"),
# Category 6: Old keyword cells - Nobel pattern
("quasicrystals aperiodic order Dan Shechtman Nobel Prize 2011 forbidden symmetry real experimentally confirmed", "nobel", "Old KW: quasicrystals Nobel"),
("photoelectric effect Einstein photons quantum light energy confirmed Nobel Prize 1921", "nobel", "Old KW: photoelectric Nobel"),
("aperiodic order quasicrystals exist real Nobel Prize chemistry 2011 Shechtman", "nobel", "Old KW: aperiodic Nobel"),
# Category 7: Old keyword cells - confirmed+experiment pattern
("standard model particles quarks leptons bosons confirmed experimental particle physics", "confirmed_exp", "Old KW: standard model exp"),
("quantum mechanics describes atomic molecular behavior confirmed experimental physics foundation", "confirmed_exp", "Old KW: QM exp"),
("atomic orbitals electron probability shells confirmed quantum mechanics chemistry foundation", "confirmed_exp", "Old KW: orbitals exp (no exp word)"),
("GPS satellites require general relativity time dilation correction confirmed engineering fact", "generic", "Old KW: GPS (no exp/obs)"),
# Category 8: Old keyword cells - generic fallback
("E equals mc squared mass energy equivalence Einstein special relativity confirmed nuclear fission fusion", "generic", "Old KW: E=mc² generic"),
("mass and energy are interchangeable E equals mc squared nuclear physics confirmed fact", "passthrough", "Old KW: has 'are'"),
("gravity curves spacetime general relativity Einstein metric tensor geodesic confirmed", "generic", "Old KW: gravity generic"),
("Eddington 1919 solar eclipse light bending confirms general relativity spacetime curvature", "passthrough", "Old KW: has 'confirms'"),
("black hole event horizon EHT image 2019 confirms general relativity extreme gravity", "passthrough", "Old KW: has 'confirms'"),
("Fibonacci sequence golden ratio phyllotaxis sunflower spiral botanical observation documented real", "confirmed_exp", "Old KW: Fibonacci observation"),
("golden angle 137.5 degrees plant growth spiral Fibonacci nature documented botany real", "generic", "Old KW: golden angle generic"),
# Category 9: Edge cases
("", "empty", "Empty string"),
(".", "passthrough", "Just a period (has '. '? No. But...)"),
("a", "passthrough", "Single letter (starts with 'a'... no, lowercase)"),
("A quasicrystal defies crystallographic rules.", "passthrough", "Starts with A"),
("An aperiodic structure was discovered.", "passthrough", "Starts with An"),
(" E=mc² is real ", "passthrough_or_generic", "Whitespace-padded"),
("GRAVITY CURVES SPACETIME GENERAL RELATIVITY CONFIRMED", "generic", "ALL CAPS keyword"),
("gravity curves spacetime\ngeneral relativity\nconfirmed", "passthrough", "Multiline with 'curves' (not a verb marker)"),
# Category 10: Stress tests for verb marker detection
("spacetime curvature gravitational waves LIGO detection 2015 confirmed experimental proof", "confirmed_exp", "KW with experiment"),
("Shechtman quasicrystal diffraction icosahedral symmetry fivefold forbidden crystallography real confirmed", "generic", "KW Shechtman (no exp/Nobel)"),
("E mc2 energy mass conversion Einstein 1905 special relativity experimental verification", "confirmed_exp", "KW mc2 experimental"),
("Higgs boson discovered LHC CERN 2012 confirmed standard model particle physics real", "generic", "KW Higgs (discovered not a marker, no exp)"),
]
print(f"=== NLS Test Suite: {len(tests)} tests ===\\n")
passed = 0
failed = 0
uncertain = 0
for text, expected, desc in tests:
result = synthesize_to_speech(text, "test query")
# Determine actual category
if result == text:
actual = "passthrough"
elif result.startswith("The ") and "disproven" in result:
actual = "disproven"
elif "Nobel Prize" in result:
actual = "nobel"
elif result.endswith(".") and result == text.rstrip(".") + ".":
actual = "confirmed_exp"
elif result.startswith("From what I understand:"):
actual = "generic"
elif not text:
actual = "empty"
else:
actual = "unknown"
match = expected in actual or actual in expected or expected == actual
if expected == "passthrough_or_generic":
match = actual in ("passthrough", "generic")
symbol = "✓" if match else "✗"
if match:
passed += 1
else:
failed += 1
if not match:
print(f"{symbol} FAIL [{desc}]")
print(f" Expected: {expected}")
print(f" Got: {actual}")
print(f" Input: {text[:60]}")
print(f" Output: {result[:80]}")
print()
print(f"\\n=== RESULTS: {passed} passed, {failed} failed out of {len(tests)} tests ===")
print(f"NLS Accuracy: {passed/len(tests)*100:.1f}%")
'''
with open('/tmp/nls_test.py', 'w') as f:
f.write(test_code)
import subprocess
result = subprocess.run(['python3', '/tmp/nls_test.py'], capture_output=True, text=True)
print(result.stdout)
if result.returncode != 0:
print("STDERR:", result.stderr)
Write the full test to /tmp/nls_test.py and run it.
The NLS function in main.rs receives text AFTER synthesize_from_cells in voice.rs has processed it.
This means the input to synthesize_to_speech is already partially processed. Analyze the full pipeline:
python3 << 'EOF'
# Model the full pipeline for a score < 0.60 response
test_inputs = [
"E equals mc squared mass energy equivalence Einstein special relativity confirmed nuclear fission fusion",
"quasicrystals aperiodic order Dan Shechtman Nobel Prize 2011 forbidden symmetry real experimentally confirmed",
"luminiferous ether disproven Michelson Morley experiment 1887 null result light medium does not exist",
]
voice_frames = ["From what I understand,", "I recall that", "There's the idea that", "It seems that"]
print("=== Full Pipeline Simulation (voice.rs → main.rs NLS) ===\n")
print("For scores 0.45-0.60 (voice.rs adds a frame + lowercase + period):")
print()
verb_markers = [" is ", " are ", " was ", " were ", " has ", " have ",
" shows ", " means ", " confirms ", " describes ", " proves ",
" needs ", " requires ", " earned ", " discovered ", " appears "]
for cell_text in test_inputs:
print(f"Cell: {cell_text[:70]}")
for frame_idx, frame in enumerate(voice_frames):
# voice.rs: lowercase_first(core) + frame
first_char = cell_text[0].lower()
voice_output = frame + " " + first_char + cell_text[1:] + "."
# Now check looks_natural on voice_output
lower = voice_output.lower()
looks_natural = (any(v in lower for v in verb_markers) or
". " in voice_output or
voice_output.startswith("The ") or voice_output.startswith("I ") or
voice_output.startswith("In "))
fate = "PASS-THROUGH" if looks_natural else "WOULD TRANSFORM"
print(f" [{frame:30s}] → {fate} → {voice_output[:60]}")
print()
print("For scores >= 0.60 (voice.rs outputs raw cell text + period):")
for cell_text in test_inputs:
voice_output = cell_text + "."
lower = voice_output.lower()
looks_natural = (any(v in lower for v in verb_markers) or
". " in voice_output)
fate = "PASS-THROUGH" if looks_natural else "WOULD TRANSFORM"
print(f" [{fate}] {voice_output[:80]}")
EOF
Based on all NLS tests, compile a gap analysis:
For each gap found:
synthesize_to_speech would fix it?---
python3 << 'EOF'
# Analyze what FID flagging looks like at different threshold settings
# Current thresholds: confidence < 0.15 AND resonance < 0.25
known_cases = [
# (topic, expected_confidence_range, should_flag, description)
("E=mc²", (0.45, 0.75), False, "Established physics — should NOT flag"),
("quasicrystals Nobel Prize", (0.40, 0.70), False, "Established fact — should NOT flag"),
("luminiferous ether disproven", (0.40, 0.70), False, "Disproven but well-established — NOT flag"),
("supersymmetry dark matter", (0.10, 0.30), True, "Speculative — SHOULD flag"),
("string theory M-theory", (0.05, 0.20), True, "Highly speculative — SHOULD flag"),
("dark energy cosmological constant", (0.05, 0.25), True, "Unknown — SHOULD flag"),
("consciousness quantum mind", (0.02, 0.15), True, "Fringe — SHOULD flag"),
("Fibonacci nature golden ratio", (0.35, 0.65), False, "Documented — NOT flag"),
("general relativity GPS satellites", (0.40, 0.70), False, "Confirmed — NOT flag"),
("multiverse many worlds", (0.02, 0.15), True, "Speculative — SHOULD flag"),
]
current_conf_threshold = 0.15
current_res_threshold = 0.25
print("=== FID Threshold Analysis ===")
print(f"Current: confidence < {current_conf_threshold} AND resonance < {current_res_threshold}\n")
print(f"{'Topic':40s} {'Conf Range':15s} {'Expected':12s} {'At Current':12s}")
print("-" * 80)
for topic, (conf_lo, conf_hi), should_flag, desc in known_cases:
conf_mid = (conf_lo + conf_hi) / 2
res_mid = conf_mid * 0.8 # resonance typically correlates with confidence
would_flag = conf_mid < current_conf_threshold and res_mid < current_res_threshold
if should_flag == would_flag:
status = "✓ CORRECT"
else:
status = "✗ WRONG" if should_flag != would_flag else "? UNCERTAIN"
print(f"{topic:40s} {str((conf_lo, conf_hi)):15s} {'FLAG' if should_flag else 'NO FLAG':12s} {status}")
print("\nConclusion: At 0.45+ resonance for established physics, current FID threshold is unlikely")
print("to trigger for physics-core cells. This is correct behavior.")
print("\nConcern: Speculative topics at 0.10-0.30 confidence may NOT trigger FID if resonance > 0.25")
print("This means some speculative responses may get through without warning.")
EOF
Analyze the fid_audit.json output from Section 2.2:
python3 << 'EOF'
import json, os
audit_file = '/sessions/eloquent-bold-newton/mnt/KAI/data/fid_audit.json'
if not os.path.exists(audit_file):
print("FID audit file not found — run --fid-audit first (Section 2.2)")
exit()
with open(audit_file, 'r') as f:
audit = json.load(f)
print(f"FID Audit file keys: {list(audit.keys())}")
print(json.dumps(audit, indent=2, default=str)[:3000])
EOF
---
These tests make isolated, reversible modifications to understand behavior.
IMPORTANT: Back up main.rs before each experiment. Restore after.
Hypothesis: Adding more verb markers to synthesize_to_speech will correctly pass through
more natural-language text without needing the disproven/noble patterns.
Test by expanding the verb_markers list and re-running the NLS test suite:
# Extended verb markers to test
extended_markers = [
" is ", " are ", " was ", " were ", " has ", " have ",
" shows ", " means ", " confirms ", " describes ", " proves ",
" needs ", " requires ", " earned ", " discovered ", " appears ",
# New additions to test:
" curves ", " bends ", " detected ", " established ", " founded ",
" verified ", " observed ", " measured ", " calculated ", " derived ",
" orbits ", " rotates ", " spins ", " flows ", " behaves ",
" cannot ", " does not ", " did not ", " could not ", " would not ",
" contains ", " consists ", " comprises ", " represents ", " indicates ",
]
# Re-run the NLS simulation with extended markers
# Count how many of the "generic fallback" cases are now correctly handled
Report: which additional verb markers would most improve NLS accuracy, and for which test cases.
Hypothesis: Adjusting the FID threshold from (confidence < 0.15 AND resonance < 0.25)
to (confidence < 0.20 AND resonance < 0.30) would catch more speculative responses
without incorrectly flagging established physics.
Analyze: what would change at each threshold setting?
thresholds_to_test = [
(0.10, 0.20, "Current - too tight?"),
(0.15, 0.25, "Current setting"),
(0.20, 0.30, "Slightly looser"),
(0.25, 0.35, "More lenient"),
(0.30, 0.40, "Liberal"),
]
# For each threshold, estimate how many of the 2416 cells would be flagged
# based on the distribution of confidence scores seen in testing
Hypothesis: The strength=3.0 for physics-core cells may not be high enough if dream pruning
is aggressive, OR it may be overkill. What is the minimum strength needed?
Analyze the pruning threshold to determine the minimum safe strength:
grep -n "prune\|threshold\|strength\|0\.5\|decay\|forget\|dream" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | grep -i "prune\|decay\|forget\|threshold" | head -30
Report: minimum safe strength for physics-core cells, and whether 3.0 is appropriate.
The voice.rs RESONANCE_THRESHOLD is 0.45. What happens if this is lowered to 0.35 or raised to 0.55?
Analyze what the user reported scores were:
At RESONANCE_THRESHOLD = 0.45, the E=mc² response at 47% barely passes. At 0.55 it would fail.
At RESONANCE_THRESHOLD = 0.35, all three would pass.
Recommendation: Should the threshold be adjusted? Document the tradeoff.
Current calibration multiplier: phi_truth_threshold = max_false_phi_c * 0.70
What would happen with different multipliers?
# Simulate calibration results at different multipliers # The user reported 10/10 at 0.70 # What was the margin? (how close was E=mc² to the threshold?) multipliers = [0.50, 0.60, 0.70, 0.75, 0.80, 0.85, 0.90] # For each, estimate which truth anchors would pass/fail # based on the known calibration run data
Report: optimal multiplier range and safety margin.
Current render limit: last 50 turns.
The concern: what happens at 51+ turns? Is there a visual discontinuity?
Analyze the render_messages code for any edge cases at the boundary:
grep -n "50\|render_turns\|last 50\|len.*turns" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -20
Recommend: Is 50 the right number? Would 30 be better? Would 100 cause performance issues?
What is the current dream cycle interval (heartbeat)?
grep -n "heartbeat\|dream.*interval\|sleep.*5\|Duration::from_secs\|dream_timer" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -20
Analyze: Is the current interval appropriate? Too frequent (CPU waste)? Too infrequent (pruning lag)?
---
Deep analysis of the overall system architecture.
List all modules and their dependencies:
find /sessions/eloquent-bold-newton/mnt/KAI/src -name "*.rs" | grep -v target | sort grep -rn "mod \|use \|pub mod " /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -40 grep -rn "^mod \|^pub mod " /sessions/eloquent-bold-newton/mnt/KAI/src/ | grep -v target | head -40
Draw a textual module dependency graph. Identify:
KAI appears to have several state machines embedded. Identify and document them:
grep -n "enum.*State\|State::.*=>\|state_machine\|transition\|Phase\|Mood\|QueryType" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -40
For each state machine found, document:
Trace the complete data flow from user input to KAI response:
Input → [encode] → SparseVec → [predictive_query] → hits → [generate_response_predictive] → [synthesize_from_cells] → voice_text → [FID gate] → [synthesize_to_speech] → synth_text → [transcript::append] → [turns.push] → [render_messages] → display
Verify each step by finding the corresponding code:
grep -n "reasoning_input\|predictive_query\|generate_response_predictive\|synthesize_from_cells\|fid_note\|synth_text\|transcript::append\|turns.push\|render_messages" /sessions/eloquent-bold-newton/mnt/KAI/src/main.rs | head -50
Document any missing steps or potential data corruption points in the pipeline.
How is state saved and loaded?
grep -rn "fn save\|fn load\|fn state_exists\|persistence\|serialize\|deserialize" /sessions/eloquent-bold-newton/mnt/KAI/src/ | grep -v target | head -30
Analyze:
grep -rn "fn append\|fn load_transcript\|transcript\|session_id" /sessions/eloquent-bold-newton/mnt/KAI/src/ | grep -v target | head -20
Analyze:
grep -rn "OllamaVoice\|ollama\|KAI_OLLAMA" /sessions/eloquent-bold-newton/mnt/KAI/src/ | grep -v target | head -20 cat /sessions/eloquent-bold-newton/mnt/KAI/src/cognition/ollama_voice.rs 2>/dev/null | head -100
Analyze:
---
After all previous sections, compile a complete issue catalog:
List any issue that:
List any issue that:
List any issue that:
List any issue that:
---
Score KAI v5.9.4 on each dimension:
Score each dimension 0-10. Provide specific justification. DIMENSION | SCORE | JUSTIFICATION -------------------------------|-------|--------------------------------------------------- Calibration Accuracy | /10 | How accurately does KAI know what it knows? Physics Core Retrieval | /10 | Does E=mc², ether, quasicrystals come back correctly? Natural Language Quality | /10 | Are responses fluent natural English? FID False Positive Rate | /10 | Does FID flag correctly without over-triggering? FID False Negative Rate | /10 | Does FID catch genuinely speculative content? Response Time (UX) | /10 | Is 4682ms acceptable? (0 = never, 10 = instant) TUI Responsiveness | /10 | Does the thinking indicator work? Does freeze happen? State Persistence Quality | /10 | Are physics-core cells persisted correctly? Code Quality | /10 | Warnings, panics, dead code, complexity Architecture Soundness | /10 | Are the design decisions appropriate? Algorithm Correctness | /10 | VSA, HDC, cosine similarity — mathematically sound? Dream Pruning Effectiveness | /10 | Is the state growing uncontrollably? NLS Pipeline Correctness | /10 | Is the full synthesize chain working end-to-end? Error Handling | /10 | Are errors handled gracefully or do panics lurk? Documentation Quality | /10 | Is the code well-documented and maintainable? TOTAL | /150
---
When running this audit, execute the following groups simultaneously:
Wave 1 (Run all at once):
Wave 2 (After Wave 1 completes):
Wave 3 (After Wave 2 completes):
Wave 4 (Synthesis):
---
Write the final report to:
/sessions/eloquent-bold-newton/mnt/KAI/KAI_AUDIT_REPORT.md
The report MUST contain:
# KAI v5.9.4 — Full System Audit Report ## Date: [date] ## Duration: [how long the audit took] ## Auditor: Antigravity --- ## Executive Summary [3-5 sentences: what is KAI, what was tested, what is the overall verdict] ## Critical Findings [P0 issues that need immediate attention] ## Score Summary [The 15-dimension scoring matrix from Section 11] ## Section-by-Section Findings ### Pre-Flight Results [compilation status, null bytes, warnings] ### Calibration Audit Results [score, threshold value, any failures] ### FID Audit Results [flagging rate, physics-core cell status] ### Train-Truths Results [cell count, strength values, natural language quality] ### State File Analysis [size, growth rate, region distribution, duplicates, health] ### Algorithm Analysis [complexity, correctness, concerns] ### Performance Analysis [response time breakdown, bottleneck identification, memory estimate] ### NLS Analysis [test suite results, accuracy %, gap analysis] ### Architecture Analysis [module graph, data flow, concerns] ### Experimental Results [what experiments were run, what was learned] ## Prioritized Recommendations ### Immediate (P0) [numbered list] ### Short Term (P1) [numbered list] ### Medium Term (P2) [numbered list] ### Long Term (P3/Vision) [numbered list] ## Raw Test Output Appendix [all raw outputs from CLI tests]
---
You are Antigravity. You are running a full audit of KAI v5.9.4.
You have been given hundreds of specific tests to run.
Your job is to EXECUTE them, RECORD the results honestly, ANALYZE what they mean, and REPORT.
Do not skip sections. Do not fabricate results. If a command fails, log the failure.
If you find something alarming, note it prominently. If something works perfectly, note that too.
The goal is a COMPLETE picture of KAI's health — good, bad, and uncertain.
Working directory: /sessions/eloquent-bold-newton/mnt/KAI/
Output file: KAI_AUDIT_REPORT.md in the KAI directory.
Do not modify KAI's source code except for isolated experiments clearly marked as such,
and always restore main.rs after each experiment.
Begin with Section 0 (Pre-Flight) immediately.