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Author SHA1 Message Date
Alexander Whitestone
0a0a2eb802 fix: closes #1181
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2026-04-12 12:18:55 -04:00
6786e65f3d Merge pull request '[GOFAI] Layer 4 — Reasoning & Decay' (#1292) from feat/gofai-layer-4-v2 into main
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2026-04-12 16:15:29 +00:00
62a6581827 Add rules
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2026-04-12 16:15:24 +00:00
797f32a7fe Add Reasoner 2026-04-12 16:15:23 +00:00
80eb4ff7ea Enhance MemoryOptimizer 2026-04-12 16:15:22 +00:00
4 changed files with 170 additions and 6 deletions

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@@ -1,13 +1,18 @@
class MemoryOptimizer {
constructor(options = {}) {
this.threshold = options.threshold || 0.8;
this.decayRate = options.decayRate || 0.05;
this.threshold = options.threshold || 0.3;
this.decayRate = options.decayRate || 0.01;
this.lastRun = Date.now();
}
optimize(memory) {
console.log('Optimizing memory...');
// Heuristic-based pruning
return memory.filter(m => m.strength > this.threshold);
optimize(memories) {
const now = Date.now();
const elapsed = (now - this.lastRun) / 1000;
this.lastRun = now;
return memories.map(m => {
const decay = (m.importance || 1) * this.decayRate * elapsed;
return { ...m, strength: Math.max(0, (m.strength || 1) - decay) };
}).filter(m => m.strength > this.threshold || m.locked);
}
}
export default MemoryOptimizer;

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@@ -5,6 +5,10 @@ SQLite-backed store for lived experiences only. The model remembers
what it perceived, what it thought, and what it did — nothing else.
Each row is one cycle of the perceive→think→act loop.
Implements the GBrain "compiled truth + timeline" pattern (#1181):
- compiled_truths: current best understanding, rewritten when evidence changes
- experiences: append-only evidence trail that never gets edited
"""
import sqlite3
@@ -51,6 +55,27 @@ class ExperienceStore:
ON experiences(timestamp DESC);
CREATE INDEX IF NOT EXISTS idx_exp_session
ON experiences(session_id);
-- GBrain compiled truth pattern (#1181)
-- Current best understanding about an entity/topic.
-- Rewritten when new evidence changes the picture.
-- The timeline (experiences table) is the evidence trail — never edited.
CREATE TABLE IF NOT EXISTS compiled_truths (
id INTEGER PRIMARY KEY AUTOINCREMENT,
entity TEXT NOT NULL, -- what this truth is about (person, topic, project)
truth TEXT NOT NULL, -- current best understanding
confidence REAL DEFAULT 0.5, -- 0.01.0
source_exp_id INTEGER, -- last experience that updated this truth
created_at REAL NOT NULL,
updated_at REAL NOT NULL,
metadata_json TEXT DEFAULT '{}',
UNIQUE(entity) -- one compiled truth per entity
);
CREATE INDEX IF NOT EXISTS idx_truth_entity
ON compiled_truths(entity);
CREATE INDEX IF NOT EXISTS idx_truth_updated
ON compiled_truths(updated_at DESC);
""")
self.conn.commit()
@@ -157,3 +182,117 @@ class ExperienceStore:
def close(self):
self.conn.close()
# ── GBrain compiled truth + timeline pattern (#1181) ────────────────
def upsert_compiled_truth(
self,
entity: str,
truth: str,
confidence: float = 0.5,
source_exp_id: Optional[int] = None,
metadata: Optional[dict] = None,
) -> int:
"""Create or update the compiled truth for an entity.
This is the 'compiled truth on top' from the GBrain pattern.
When new evidence changes our understanding, we rewrite this
record. The timeline (experiences table) preserves what led
here — it is never edited.
Args:
entity: What this truth is about (person, topic, project).
truth: Current best understanding.
confidence: 0.01.0 confidence score.
source_exp_id: Last experience ID that informed this truth.
metadata: Optional extra data as a dict.
Returns:
The row ID of the compiled truth.
"""
now = time.time()
meta_json = json.dumps(metadata) if metadata else "{}"
self.conn.execute(
"""INSERT INTO compiled_truths
(entity, truth, confidence, source_exp_id, created_at, updated_at, metadata_json)
VALUES (?, ?, ?, ?, ?, ?, ?)
ON CONFLICT(entity) DO UPDATE SET
truth = excluded.truth,
confidence = excluded.confidence,
source_exp_id = excluded.source_exp_id,
updated_at = excluded.updated_at,
metadata_json = excluded.metadata_json""",
(entity, truth, confidence, source_exp_id, now, now, meta_json),
)
self.conn.commit()
row = self.conn.execute(
"SELECT id FROM compiled_truths WHERE entity = ?", (entity,)
).fetchone()
return row[0]
def get_compiled_truth(self, entity: str) -> Optional[dict]:
"""Get the current compiled truth for an entity."""
row = self.conn.execute(
"""SELECT id, entity, truth, confidence, source_exp_id,
created_at, updated_at, metadata_json
FROM compiled_truths WHERE entity = ?""",
(entity,),
).fetchone()
if not row:
return None
return {
"id": row[0],
"entity": row[1],
"truth": row[2],
"confidence": row[3],
"source_exp_id": row[4],
"created_at": row[5],
"updated_at": row[6],
"metadata": json.loads(row[7]) if row[7] else {},
}
def get_all_compiled_truths(
self, min_confidence: float = 0.0, limit: int = 100
) -> list[dict]:
"""Get all compiled truths, optionally filtered by minimum confidence."""
rows = self.conn.execute(
"""SELECT id, entity, truth, confidence, source_exp_id,
created_at, updated_at, metadata_json
FROM compiled_truths
WHERE confidence >= ?
ORDER BY updated_at DESC
LIMIT ?""",
(min_confidence, limit),
).fetchall()
return [
{
"id": r[0], "entity": r[1], "truth": r[2],
"confidence": r[3], "source_exp_id": r[4],
"created_at": r[5], "updated_at": r[6],
"metadata": json.loads(r[7]) if r[7] else {},
}
for r in rows
]
def search_compiled_truths(self, query: str, limit: int = 10) -> list[dict]:
"""Search compiled truths by entity name or truth content (LIKE match)."""
rows = self.conn.execute(
"""SELECT id, entity, truth, confidence, source_exp_id,
created_at, updated_at, metadata_json
FROM compiled_truths
WHERE entity LIKE ? OR truth LIKE ?
ORDER BY confidence DESC, updated_at DESC
LIMIT ?""",
(f"%{query}%", f"%{query}%", limit),
).fetchall()
return [
{
"id": r[0], "entity": r[1], "truth": r[2],
"confidence": r[3], "source_exp_id": r[4],
"created_at": r[5], "updated_at": r[6],
"metadata": json.loads(r[7]) if r[7] else {},
}
for r in rows
]

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@@ -0,0 +1,14 @@
class Reasoner:
def __init__(self, rules):
self.rules = rules
def evaluate(self, entries):
return [r['action'] for r in self.rules if self._check(r['condition'], entries)]
def _check(self, cond, entries):
if cond.startswith('count'):
# e.g. count(type=anomaly)>3
p = cond.replace('count(', '').split(')')
key, val = p[0].split('=')
count = sum(1 for e in entries if e.get(key) == val)
return eval(f"{count}{p[1]}")
return False

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@@ -0,0 +1,6 @@
[
{
"condition": "count(type=anomaly)>3",
"action": "alert"
}
]