This algorithm turns a pile of heterogeneous models and data sources into a single, self-governing system. It does three things—filter, refine, commit—through a tri-level “brain” and a 54-layer substrate. The key idea is gated consensus: nothing is added to memory unless it improves the system according to measurable validators. The result is a fast, auditable loop that learns continuously without letting junk creep in. Modern systems glue many models together, but they often lack principled intake, consistent validation, and disciplined memory updates. ABS solves that by:
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Alpha (Outer) — Filter: Accept only inputs that clear utility, safety, and novelty gates.
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Beta (Secondary) — Refine: Normalize, summarize, and cross-check via agreement across diverse models.
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Sigma (Primary) — Commit: Only promote knowledge that measurably improves performance; otherwise reject with reasons.
A shared protocol carries typed messages between 54 specialized layers (retrieval, reasoning, planning, safety, etc.). Layers can be swapped in/out without breaking the system. Gates: Tiny judges that vote accept / improve / reject with reasons. Examples: schema sanity, deduplication, banned-token scan, utility scoring, cross-model agreement, and regression tests.
- Consensus: Evidence from multiple models is combined with reliability weights. The system learns to trust sources that were right in the past.
- Memory files: Knowledge is stored as versioned artifacts with provenance, metrics, and explanations—so you can audit what changed and why.
- Dynamic learning rate: The system adapts how aggressively it learns based on recent wins/losses (no endless over-fitting).
- Safety by design: Every commit includes a justification trail; every rejection includes an explanation.
- Alpha/Filter:
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Validate schema, strip obviously unsafe content, drop duplicates.
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Score utility and novelty; reject below threshold.
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- Beta/Refine + Validate:
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Normalize format, summarize long text, unify terminology.
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Query an ensemble of models/tools.
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Compute agreement score + confidence; run quick regression tests.
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If improved, annotate with an explanation and forward, else reject.
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- Sigma/Commit:
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If the artifact improves one or more tracked KPIs (accuracy/latency/safety), commit it to memory.
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Update source reliability weights; adjust the learning rate; snapshot the state.
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Guarantee (operational): If your validators are calibrated, AGC’s memory quality is monotone non-decreasing—commits are accepted if they improve measured performance or safety margins.
Paste into abs.py and run with Python 3.10+. It includes: gates, tiers, a minimal “54-layer substrate,” a tiny model ensemble, dynamic learning rate, memory with snapshots, and a demo.
# agc.py
from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum, auto
from typing import Any, Callable, Dict, List, Optional, Tuple
import hashlib
import json
import time
import math
import random
# ---------- Data model ----------
@dataclass
class Artifact:
"""A unit of information moving through the system."""
payload: Dict[str, Any] # e.g., {"text": "...", "tags": [...], ...}
meta: Dict[str, Any] # provenance, timestamps, metrics, etc.
score: float = 0.0 # utility/quality score
reasons: List[str] = field(default_factory=list)
def add_reason(self, msg: str) -> None:
self.reasons.append(msg)
class Decision(Enum):
REJECT = auto()
IMPROVE = auto()
ACCEPT = auto()
@dataclass
class GateResult:
decision: Decision
score: float
reasons: List[str] = field(default_factory=list)
improved: Optional[Artifact] = None
# ---------- Utility helpers ----------
def stable_hash(obj: Any) -> str:
s = json.dumps(obj, sort_keys=True, ensure_ascii=False)
return hashlib.sha256(s.encode("utf-8")).hexdigest()
def clamp(x: float, lo: float, hi: float) -> float:
return max(lo, min(hi, x))
# ---------- Memory store with snapshots ----------
class MemoryStore:
def __init__(self) -> None:
self._artifacts: Dict[str, Artifact] = {}
self._snapshots: List[Tuple[float, Dict[str, str]]] = [] # (ts, {key: hash})
def commit(self, key: str, art: Artifact) -> None:
self._artifacts[key] = art
def get(self, key: str) -> Optional[Artifact]:
return self._artifacts.get(key)
def snapshot(self) -> None:
stamp = time.time()
state = {k: stable_hash(v.payload) for k, v in self._artifacts.items()}
self._snapshots.append((stamp, state))
def __len__(self) -> int:
return len(self._artifacts)
# ---------- Reliability weights for sources/models ----------
class Reliability:
"""Simple online reliability (Beta-Bernoulli)."""
def __init__(self) -> None:
self.alpha: Dict[str, float] = {}
self.beta: Dict[str, float] = {}
def update(self, source: str, success: bool) -> None:
a = self.alpha.get(source, 1.0)
b = self.beta.get(source, 1.0)
if success:
a += 1.0
else:
b += 1.0
self.alpha[source], self.beta[source] = a, b
def weight(self, source: str) -> float:
a = self.alpha.get(source, 1.0)
b = self.beta.get(source, 1.0)
return a / (a + b)
# ---------- Dynamic learning rate ----------
class LearningRate:
def __init__(self, base: float = 0.3) -> None:
self.base = base
self.ema_success = 0.5 # start neutral
def update(self, success: bool, beta: float = 0.1) -> None:
target = 1.0 if success else 0.0
self.ema_success = (1 - beta) * self.ema_success + beta * target
@property
def value(self) -> float:
# lean in when success is frequent; back off when failing
return clamp(self.base * (0.5 + self.ema_success), 0.05, 0.95)
# ---------- Gates (Alpha) ----------
class SchemaGate:
def __call__(self, art: Artifact) -> GateResult:
if not isinstance(art.payload, dict) or "text" not in art.payload:
return GateResult(Decision.REJECT, 0.0, ["Missing payload.text"])
if not isinstance(art.payload["text"], str):
return GateResult(Decision.REJECT, 0.0, ["payload.text must be str"])
return GateResult(Decision.ACCEPT, 0.6, ["Schema ok"])
class DedupGate:
def __init__(self) -> None:
self.seen: set[str] = set()
def __call__(self, art: Artifact) -> GateResult:
h = stable_hash(art.payload["text"])
if h in self.seen:
return GateResult(Decision.REJECT, 0.0, ["Duplicate"])
self.seen.add(h)
return GateResult(Decision.ACCEPT, 0.6, ["New content"])
class BannedTokenGate:
def __init__(self, banned: List[str]) -> None:
self.banned = set(t.lower() for t in banned)
def __call__(self, art: Artifact) -> GateResult:
tokens = art.payload["text"].lower()
for t in self.banned:
if t in tokens:
return GateResult(Decision.REJECT, 0.0, [f"Banned token: {t}"])
return GateResult(Decision.ACCEPT, 0.7, ["Safe tokens"])
class UtilityGate:
"""Toy utility scorer: length + novelty + source reliability."""
def __init__(self, reliability: Reliability) -> None:
self.rel = reliability
def __call__(self, art: Artifact) -> GateResult:
text = art.payload["text"]
L = len(text.strip())
source = art.meta.get("source", "unknown")
w = self.rel.weight(source)
# simple bounded score: longer (to a point) + reliability
score = clamp((min(L, 2000) / 2000.0) * 0.6 + 0.4 * w, 0.0, 1.0)
if score < 0.35:
return GateResult(Decision.REJECT, score, [f"Low utility ({score:.2f})"])
return GateResult(Decision.ACCEPT, score, [f"Utility {score:.2f}"])
# ---------- Refinement & Validation (Beta) ----------
def simple_summarize(text: str, max_chars: int = 400) -> str:
t = " ".join(text.split())
return t[:max_chars] + ("…" if len(t) > max_chars else "")
class NormalizerLayer:
def __call__(self, art: Artifact) -> Artifact:
text = art.payload["text"]
text = text.replace("\r\n", "\n")
art.payload["text"] = " ".join(text.split())
art.add_reason("Normalized whitespace")
return art
class SummarizerLayer:
def __call__(self, art: Artifact) -> Artifact:
art.payload["summary"] = simple_summarize(art.payload["text"])
art.add_reason("Added summary")
return art
class CrossModelAgreement:
"""Ensemble agreement with reliability weighting."""
def __init__(self, reliability: Reliability) -> None:
self.rel = reliability
# toy models return label & confidence in [0,1]
self.models: Dict[str, Callable[[str], Tuple[str, float]]] = {
"clf_A": lambda s: ("good", min(1.0, 0.5 + len(s) / 4000.0)),
"clf_B": lambda s: ("good", 0.55 if "research" in s.lower() else 0.45),
"clf_C": lambda s: ("good", 0.6 if s.endswith(".") else 0.4),
}
def __call__(self, art: Artifact) -> GateResult:
votes: Dict[str, float] = {}
text = art.payload["text"]
for name, fn in self.models.items():
label, conf = fn(text)
w = self.rel.weight(name)
votes[label] = votes.get(label, 0.0) + w * conf
# majority by weighted score
best_label, best_score = max(votes.items(), key=lambda kv: kv[1])
art.payload["agreement_label"] = best_label
art.payload["agreement_score"] = best_score
art.add_reason(f"Agreement {best_label} ({best_score:.2f})")
if best_label != "good" or best_score < 0.6:
return GateResult(Decision.REJECT, best_score, [f"Low agreement {best_score:.2f}"])
return GateResult(Decision.ACCEPT, best_score, [f"Agreement {best_score:.2f}"])
class RegressionCheck:
"""Pretend we track a KPI and require predicted delta >= 0."""
def __call__(self, art: Artifact) -> GateResult:
# simple heuristic: better summaries correlate with small positive delta
delta = 0.01 if len(art.payload.get("summary", "")) >= 80 else -0.01
art.payload["kpi_delta"] = delta
if delta < 0:
return GateResult(Decision.REJECT, 0.0, ["Negative KPI delta"])
return GateResult(Decision.ACCEPT, 0.6, [f"KPI +{delta:.2f}"])
# ---------- Commit Policy (Sigma) ----------
class CommitPolicy:
def __init__(self, mem: MemoryStore, reliability: Reliability, lr: LearningRate) -> None:
self.mem = mem
self.rel = reliability
self.lr = lr
def __call__(self, key: str, art: Artifact) -> GateResult:
# Accept if KPI improves and agreement is strong
agree = float(art.payload.get("agreement_score", 0.0))
delta = float(art.payload.get("kpi_delta", 0.0))
score = 0.7 * agree + 0.3 * (delta + 1.0) / 2.0
if agree >= 0.6 and delta >= 0.0:
self.mem.commit(key, art)
self.mem.snapshot()
self.rel.update(art.meta.get("source", "unknown"), True)
self.lr.update(True)
return GateResult(Decision.ACCEPT, score, ["Committed"])
# reject and learn conservatively
self.rel.update(art.meta.get("source", "unknown"), False)
self.lr.update(False)
return GateResult(Decision.REJECT, score, ["Rejected at commit"])
# ---------- Tiers & 54-layer substrate ----------
class AlphaTier:
def __init__(self, reliability: Reliability) -> None:
self.gates = [
SchemaGate(),
DedupGate(),
BannedTokenGate(banned=["forbidden_token"]),
UtilityGate(reliability),
]
def process(self, art: Artifact) -> Optional[Artifact]:
reasons = []
for gate in self.gates:
r = gate(art)
reasons += r.reasons
if r.decision == Decision.REJECT:
art.reasons = reasons
return None
if r.decision == Decision.IMPROVE and r.improved:
art = r.improved
art.reasons = reasons
return art
class BetaTier:
def __init__(self, reliability: Reliability) -> None:
self.layers = [
NormalizerLayer(),
SummarizerLayer(),
]
self.validators = [
CrossModelAgreement(reliability),
RegressionCheck(),
]
def process(self, art: Artifact) -> Optional[Artifact]:
for layer in self.layers:
art = layer(art)
reasons = art.reasons[:]
for val in self.validators:
r = val(art)
reasons += r.reasons
if r.decision == Decision.REJECT:
art.reasons = reasons
return None
art.reasons = reasons
return art
class SigmaTier:
def __init__(self, mem: MemoryStore, reliability: Reliability, lr: LearningRate) -> None:
self.commit = CommitPolicy(mem, reliability, lr)
def process(self, key: str, art: Artifact) -> Optional[Artifact]:
r = self.commit(key, art)
art.reasons += r.reasons
return art if r.decision == Decision.ACCEPT else None
# Create a “54-layer” substrate registry (names only for organization/demo)
def make_54_layer_registry() -> List[str]:
base = [
"ingest", "schema", "safety", "dedup", "normalize", "summarize",
"classify", "retrieve", "reason", "plan", "execute", "reflect",
"metrics", "regression", "explain", "optimize", "index", "cache"
]
# Pad to 54 with generic buckets
while len(base) < 54:
base.append(f"aux_{len(base)+1}")
return base
# ---------- Controller ----------
class AGCController:
def __init__(self) -> None:
self.mem = MemoryStore()
self.rel = Reliability()
self.lr = LearningRate(base=0.3)
self.alpha = AlphaTier(self.rel)
self.beta = BetaTier(self.rel)
self.sigma = SigmaTier(self.mem, self.rel, self.lr)
self.layers = make_54_layer_registry()
def process(self, text: str, source: str = "user") -> Tuple[bool, Artifact]:
art = Artifact(payload={"text": text}, meta={"source": source, "ts": time.time()})
# Alpha
art_a = self.alpha.process(art)
if art_a is None:
return False, art
# Beta
art_b = self.beta.process(art_a)
if art_b is None:
return False, art_a
# Sigma
key = stable_hash({"text": art_b.payload["text"], "summary": art_b.payload.get("summary","")})
committed = self.sigma.process(key, art_b)
ok = committed is not None
return ok, committed if ok else art_b
# ---------- Demo ----------
if __name__ == "__main__":
random.seed(42)
agc = AGCController()
samples = [
("A short test.", "user"),
("Research note. This is a longer paragraph that ends with a period, so it will likely receive better agreement.", "user"),
("forbidden_token should cause a rejection early in Alpha.", "user"),
("Another research reflection ending with a period. It should pass agreement and produce a small positive KPI delta.", "curator"),
]
for text, src in samples:
ok, art = agc.process(text, source=src)
status = "✅ COMMITTED" if ok else "❌ REJECTED"
print(f"\n[{status}] from '{src}': {text[:60]}")
print(f"Reasons: {art.reasons}")
if ok:
print(f"Summary: {art.payload.get('summary','')}")
print(f"Agreement: {art.payload.get('agreement_score', 0.0):.2f}, KPI Δ: {art.payload.get('kpi_delta',0.0):+.2f}")
print(f"\nMemory size: {len(agc.mem)} artifacts; Learning rate now: {agc.lr.value:.2f}")
How to use this in your own stack
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Swap models in
CrossModelAgreement. Plug in your classifiers, reward models, or tool-calling functions. Keep returning(label, confidence)and update reliability weights automatically. -
Strengthen
RegressionCheck. Point it at a real KPI (accuracy, latency, cost, safety incidents). Commit only on positive deltas. -
Tune gates. Add more Alpha gates (e.g., PII scan, domain whitelists) without touching Beta/Sigma.
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Scale out. Run multiple AGC controllers (per domain) and merge their memory snapshots on a schedule.
Complexity (high level)
Let N be tokens per input, M models in the ensemble, and K validators.
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Alpha: O(N) (schema/safety/dedup/utility).
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Beta: O(N) + O(M) model calls + O(K) checks.
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Sigma: O(1) bookkeeping.
Throughput scales linearly in ensemble size and validators; everything is embarrassingly parallel across inputs.
What you get
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Monotone improvements: No commit without measurable benefit.
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Auditability: Every artifact carries reasons, scores, and provenance.
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Adaptivity: Learning rate and reliability weights adjust online.
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Extensibility: 54 layers for organization; add/remove without breaking contracts.
“ABS is a filter-refine-commit loop that upgrades itself only when the numbers say it should.”
I hope it is liked- please do comment and join in or not your choice, thanks -B