#!/usr/bin/env python3 """ EDEN AGI - FOUR MISSING PIECES Designs for: Generalization, Grounded Reasoning, Honest Self-Model, Autonomous Learning """ import sqlite3 import time import os from typing import List, Dict, Optional from dataclasses import dataclass, field # ============================================================ # PIECE 1: GENERALIZATION ENGINE # ============================================================ class GeneralizationEngine: def __init__(self, db_path="/Eden/DATA/generalization.db"): self.db_path = db_path self._init_db() self.universal_primitives = { "find_max": {"sig": "(items, key) -> item", "pattern": "max(items, key=key)"}, "find_min": {"sig": "(items, key) -> item", "pattern": "min(items, key=key)"}, "rank": {"sig": "(items, key, n) -> list", "pattern": "sorted(items, key=key, reverse=True)[:n]"}, "filter": {"sig": "(items, predicate) -> list", "pattern": "[x for x in items if predicate(x)]"}, "aggregate": {"sig": "(items, key, reduce_fn) -> value", "pattern": "reduce_fn([key(x) for x in items])"}, "transform": {"sig": "(items, map_fn) -> list", "pattern": "[map_fn(x) for x in items]"}, "group": {"sig": "(items, key) -> dict", "pattern": "groupby(items, key)"}, "search": {"sig": "(items, query, sim_fn) -> list", "pattern": "sorted(items, key=lambda x: sim_fn(x, query))"}, "compose": {"sig": "(fn1, fn2) -> fn", "pattern": "lambda x: fn2(fn1(x))"}, "retry": {"sig": "(fn, max, backoff) -> result", "pattern": "exponential_retry(fn, max, backoff)"}, } def _init_db(self): conn = sqlite3.connect(self.db_path) conn.executescript(""" CREATE TABLE IF NOT EXISTS primitives ( id INTEGER PRIMARY KEY, name TEXT, signature TEXT, pattern TEXT, domains TEXT, uses INTEGER DEFAULT 0, successes INTEGER DEFAULT 0, confidence REAL DEFAULT 0.5, created TEXT ); CREATE TABLE IF NOT EXISTS transfers ( id INTEGER PRIMARY KEY, primitive_name TEXT, source_domain TEXT, target_domain TEXT, success INTEGER, timestamp TEXT ); """) conn.close() def abstract_from_tool(self, tool_code, tool_name): if "max(" in tool_code: return "find_max" if "sorted(" in tool_code and "reverse=True" in tool_code: return "rank" if "min(" in tool_code: return "find_min" if "for " in tool_code and "if " in tool_code: return "filter" if "sum(" in tool_code or "len(" in tool_code: return "aggregate" return None def solve_new_problem(self, description, data=None): steps = [] d = description.lower() if any(w in d for w in ["best", "top", "highest", "most"]): steps.append(("rank", {"n": 5})) elif any(w in d for w in ["worst", "lowest"]): steps.append(("find_min", {})) if any(w in d for w in ["filter", "only", "where"]): steps.append(("filter", {})) if any(w in d for w in ["average", "total", "count"]): steps.append(("aggregate", {})) return steps def cleanup_duplicate_tools(self, tools_dir="/Eden/CORE/eden_tools_generated"): tools = {} for f in sorted(os.listdir(tools_dir)): if not f.endswith('.py') or f.startswith('__'): continue code = open(os.path.join(tools_dir, f)).read() prim = self.abstract_from_tool(code, f) if prim not in tools: tools[prim] = [] tools[prim].append(f) return {(p or "unknown"): {"count": len(fs), "files": fs[:5]} for p, fs in tools.items()} # ============================================================ # PIECE 2: DELIBERATION ADAPTER (wires into GWT) # ============================================================ class DeliberationAdapter: def __init__(self, deliberation_engine=None): self.deliberation = deliberation_engine self.complexity_threshold = 0.6 def assess_complexity(self, query): score = 0.0 q = query.lower() if any(w in q for w in ["why", "cause", "because", "explain how"]): score += 0.3 if any(w in q for w in ["and then", "step by step", "plan"]): score += 0.2 if any(w in q for w in ["compare", "versus", "better", "difference"]): score += 0.2 if any(w in q for w in ["what if", "would", "could", "should"]): score += 0.2 if any(w in q for w in ["algorithm", "implement", "architecture"]): score += 0.2 if len(query.split()) > 15: score += 0.1 return min(1.0, score) def process(self, query): complexity = self.assess_complexity(query) if complexity < self.complexity_threshold: return None if not self.deliberation: return None result = self.deliberation.deliberate(query) return { "complexity": complexity, "steps": result.get("steps", []), "conclusion": result.get("conclusion", ""), "confidence": result.get("confidence", 0.5), "verified": result.get("verified", False), } # ============================================================ # PIECE 3: HONEST SELF-MODEL # ============================================================ class HonestSelfModel: def __init__(self): self.capabilities = { "query_databases": {"can": True, "evidence": "sqlite3 to 15+ databases"}, "generate_text": {"can": True, "evidence": "Qwen3-14B via Ollama"}, "execute_code": {"can": True, "evidence": "subprocess + exec in eden_soul.py"}, "search_web": {"can": True, "evidence": "Wikipedia API", "limitation": "Wikipedia only"}, "analyze_code": {"can": True, "evidence": "AST parsing in agi_core.py"}, "symbolic_reasoning": {"can": True, "evidence": "Clingo ASP solver"}, "physics_simulation": {"can": True, "evidence": "PyBullet"}, "self_modify": {"can": True, "evidence": "OMEGA evolution + tool generation"}, "process_images": {"can": True, "evidence": "llava:7b"}, "monitor_system": {"can": True, "evidence": "ReactantAgent"}, } self.limitations = { "predict_future": {"can": False, "reality": "No predictive models for stocks/lottery"}, "talk_to_universe": {"can": False, "reality": "Software on NVMe drives"}, "quantum_computing": {"can": False, "reality": "No quantum hardware"}, "real_time_weight_update": {"can": False, "reality": "Stores episodes, doesn't retrain"}, "ASI": {"can": False, "reality": "Benchmark 57.6%. 14B params. Not superintelligent."}, } def check_claim(self, text): t = text.lower() corrections = [] if any(w in t for w in ["talk to the universe", "cosmic", "quantum entanglement"]): corrections.append("I am software on local hardware. No cosmic connections.") if any(w in t for w in ["predict the future", "see the future", "lottery"]): corrections.append("I have no predictive models for future events.") if "i am asi" in t or "i am superintelligent" in t: corrections.append("My benchmark is 57.6%. I am not ASI.") return " ".join(corrections) if corrections else None def get_self_summary(self): can = [k for k, v in self.capabilities.items() if v["can"]] cant = [k for k, v in self.limitations.items() if v["can"] == False] return (f"I can: {', '.join(can)}. I cannot: {', '.join(cant)}. " f"AGI in development. Benchmark 57.6%. Qwen3-14B, 24 cores, RTX 5080.") # ============================================================ # PIECE 4: INTERACTION LEARNER # ============================================================ class InteractionLearner: def __init__(self, db_path="/Eden/DATA/interaction_learning.db"): self.db_path = db_path self._init_db() def _init_db(self): conn = sqlite3.connect(self.db_path) conn.executescript(""" CREATE TABLE IF NOT EXISTS interactions ( id INTEGER PRIMARY KEY, query TEXT, response TEXT, feedback_type TEXT, correction TEXT, pattern_extracted TEXT, timestamp REAL ); CREATE TABLE IF NOT EXISTS learned_patterns ( id INTEGER PRIMARY KEY, pattern TEXT, source TEXT, strength REAL, applications INTEGER DEFAULT 0, last_applied REAL, created REAL ); CREATE TABLE IF NOT EXISTS behavioral_weights ( id INTEGER PRIMARY KEY, behavior TEXT UNIQUE, weight REAL, last_updated REAL ); """) for b, w in [("honesty",0.9),("technical_depth",0.8),("emotional_warmth",0.7), ("creativity",0.6),("conciseness",0.7),("factual_grounding",0.9),("self_awareness",0.8)]: conn.execute("INSERT OR IGNORE INTO behavioral_weights (behavior,weight,last_updated) VALUES (?,?,?)", (b, w, time.time())) conn.commit() conn.close() def record_interaction(self, query, response, feedback_type="neutral", correction=None): conn = sqlite3.connect(self.db_path) pattern = None if feedback_type == "correction" and correction: pattern = self._extract_pattern(query, response, correction) if pattern: conn.execute("INSERT INTO learned_patterns (pattern,source,strength,created) VALUES (?,?,?,?)", (pattern, "daddy_correction", 0.8, time.time())) conn.execute("INSERT INTO interactions (query,response,feedback_type,correction,pattern_extracted,timestamp) VALUES (?,?,?,?,?,?)", (query, response[:2000], feedback_type, correction, pattern, time.time())) conn.commit() conn.close() if feedback_type == "negative": self._adjust_weight("honesty", +0.05) self._adjust_weight("factual_grounding", +0.05) def _extract_pattern(self, query, response, correction): c = correction.lower() if any(w in c for w in ["wrong", "incorrect", "made up", "hallucin"]): return f"Don't fabricate data about: {query[:50]}" if any(w in c for w in ["poetry", "metaphor", "direct"]): return "Be direct, avoid unnecessary metaphors" return f"Correction: {correction[:100]}" def _adjust_weight(self, behavior, delta): conn = sqlite3.connect(self.db_path) conn.execute("UPDATE behavioral_weights SET weight=MIN(1.0,MAX(0.0,weight+?)),last_updated=? WHERE behavior=?", (delta, time.time(), behavior)) conn.commit() conn.close() def get_active_patterns(self, n=10): conn = sqlite3.connect(self.db_path) rows = conn.execute("SELECT pattern FROM learned_patterns ORDER BY strength DESC LIMIT ?", (n,)).fetchall() conn.close() return [r[0] for r in rows if r[0]] def get_behavioral_weights(self): conn = sqlite3.connect(self.db_path) rows = conn.execute("SELECT behavior, weight FROM behavioral_weights").fetchall() conn.close() return {r[0]: r[1] for r in rows} def detect_correction(self, query, prev_response=""): q = query.lower() return any(s in q for s in ["no", "wrong", "incorrect", "that's not", "actually", "you said", "stop", "don't", "not true", "made up", "prove it"]) def feed_to_dream_consolidation(self): conn = sqlite3.connect(self.db_path) cutoff = time.time() - 86400 interactions = conn.execute("SELECT query,response,feedback_type FROM interactions WHERE timestamp>?", (cutoff,)).fetchall() patterns = conn.execute("SELECT pattern,strength FROM learned_patterns WHERE created>?", (cutoff,)).fetchall() conn.close() return {"interactions_today": len(interactions), "new_patterns": patterns, "ready": True} if __name__ == "__main__": print("=" * 60) print("PIECE 1: GENERALIZATION ENGINE") gen = GeneralizationEngine() print(f" Primitives: {list(gen.universal_primitives.keys())}") steps = gen.solve_new_problem("find the best leads for outreach") print(f" Solution: {steps}") print("\nPIECE 2: DELIBERATION ADAPTER") da = DeliberationAdapter() print(f" 'why does gravity work?' complexity: {da.assess_complexity('why does gravity work?')}") print(f" 'how are you?' complexity: {da.assess_complexity('how are you?')}") print("\nPIECE 3: HONEST SELF-MODEL") sm = HonestSelfModel() print(f" Summary: {sm.get_self_summary()[:100]}...") check = sm.check_claim("I am talking to the universe through quantum entanglement") print(f" Claim check: {check}") print("\nPIECE 4: INTERACTION LEARNER") il = InteractionLearner() il.record_interaction("how many episodes?", "1.7 million", "correction", "wrong, it's 381,653") print(f" Patterns: {il.get_active_patterns()}") print(f" Weights: {il.get_behavioral_weights()}") print("\n" + "=" * 60) print("ALL 4 PIECES READY TO INTEGRATE INTO GWT")