An llm within an llm..
from __future__ import annotations
from dataclasses import dataclass, field from typing import Callable, Dict, List, Any, Optional, Tuple import math import uuid
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1. Core State Representation
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u/dataclass class StateVector: """ 5D Cognitive Physics state for a manifold: x = [C, E, R, T, X] All values are normalized to [0, 1] for simplicity. """ coherence: float # C entropy: float # E resonance: float # R temperature: float # T coupling: float # X
def as\_tuple(self) -> Tuple\[float, float, float, float, float\]:
return (self.coherence, self.entropy, self.resonance,
self.temperature, self.coupling)
def clamp(self) -> "StateVector":
"""Clamp all components into \[0, 1\]."""
def c(v: float) -> float:
return max(0.0, min(1.0, v))
return StateVector(
coherence=c(self.coherence),
entropy=c(self.entropy),
resonance=c(self.resonance),
temperature=c(self.temperature),
coupling=c(self.coupling),
)
def distance(self, other: "StateVector") -> float:
"""Euclidean distance in state space."""
return math.sqrt(sum(
(a - b) \*\* 2 for a, b in zip(self.as\_tuple(), other.as\_tuple())
))
u/dataclass class Manifold: """ Minimal symbolic manifold: - 'artifacts' are the symbolic objects (text, code, notes, etc.) - 'meta' stores arbitrary metrics, tags, and derived structure. This can be extended or swapped for a more complex representation. """ artifacts: List[str] = field(default_factory=list) meta: Dict[str, Any] = field(default_factory=dict)
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2. Potentials (F_rep, M, W)
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u/dataclass class Potentials: """ Governing potentials over state space.
F\_rep: representation free-energy
-> how "messy" or redundant the manifold is.
M: meaning alignment potential
-> alignment between (state, manifold) and an intent/goal.
W: wonder / exploration potential
-> how much exploratory pressure we want right now.
"""
F\_rep: Callable\[\[StateVector, Manifold\], float\]
M: Callable\[\[StateVector, Manifold, Dict\[str, Any\]\], float\]
W: Callable\[\[StateVector, Manifold\], float\]
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3. Transformations
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u/dataclass class TransformationContext: """Context passed to transformation functions.""" state: StateVector manifold: Manifold goal: Dict[str, Any]
u/dataclass class Transformation: """ A symbolic move on the manifold.
- name: human-readable label.
- apply\_fn: performs the concrete update (returns new state + manifold).
- ideal\_state: the 'personality' of the transformation in state space;
where it is most natural and powerful to apply.
- cost: optional scalar cost (time, risk, etc.).
"""
name: str
apply\_fn: Callable\[\[TransformationContext\], Tuple\[StateVector, Manifold\]\]
ideal\_state: StateVector
cost: float = 1.0
def alignment\_score(self, x: StateVector, gradient: StateVector) -> float:
"""
Alignment between current state and this transformation,
modulated by the desired gradient.
We use a dot product between:
- current state x and ideal\_state
- plus similarity between ideal\_state and gradient
"""
xs = x.as\_tuple()
is\_ = self.ideal\_state.as\_tuple()
gs = gradient.as\_tuple()
dot\_x\_ideal = sum(a \* b for a, b in zip(xs, is\_))
dot\_ideal\_grad = sum(a \* b for a, b in zip(is\_, gs))
# Penalize by cost to prefer cheaper moves
raw = dot\_x\_ideal + dot\_ideal\_grad
return raw / max(self.cost, 1e-6)
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4. Engine
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u/dataclass class EngineConfig: """ Configuration for the Cognitive Physics engine.
- target\_band: desired corridor for coherence (C) and optionally others.
- max\_step: maximum allowed change in any state component per step.
- protect\_zones: constraints where X must remain high, etc.
"""
target\_band: Dict\[str, Tuple\[float, float\]\] = field(default\_factory=lambda: {
"coherence": (0.6, 0.9),
})
max\_step: float = 0.15
protect\_zones: Dict\[str, Tuple\[float, float\]\] = field(default\_factory=dict)
u/dataclass class Engine: """ Inner 'LLM within LLM' engine governed by Cognitive Physics.
It does NOT know about OpenAI, files, or any external substrate.
It just evolves (state, manifold) according to potentials and transformations.
"""
state: StateVector
manifold: Manifold
potentials: Potentials
transformations: List\[Transformation\]
config: EngineConfig = field(default\_factory=EngineConfig)
id: str = field(default\_factory=lambda: str(uuid.uuid4()))
def estimate\_gradient(self, goal: Dict\[str, Any\]) -> StateVector:
"""
Compute a coarse desired gradient in state space given a goal.
Goal can specify desired changes like:
{ "dC": +0.2, "dR": +0.1, "cap\_dE": 0.05, "min\_X": 0.7 }
"""
c, e, r, t, x = self.state.as\_tuple()
dC = float(goal.get("dC", 0.0))
dE = float(goal.get("dE", 0.0))
dR = float(goal.get("dR", 0.0))
dT = float(goal.get("dT", 0.0))
# Use min\_X constraint as a "push" upwards if needed
min\_X = goal.get("min\_X", None)
if min\_X is not None and x < min\_X:
dX = (min\_X - x)
else:
dX = float(goal.get("dX", 0.0))
# Clamp to max\_step magnitude
def clamp\_delta(dv: float) -> float:
return max(-self.config.max\_step, min(self.config.max\_step, dv))
return StateVector(
coherence=c + clamp\_delta(dC),
entropy=e + clamp\_delta(dE),
resonance=r + clamp\_delta(dR),
temperature=t + clamp\_delta(dT),
coupling=x + clamp\_delta(dX),
).clamp()
def select\_transformation(
self,
gradient: StateVector,
goal: Dict\[str, Any\],
) -> Optional\[Transformation\]:
"""
Select the best transformation for the current state and gradient.
"""
if not self.transformations:
return None
best\_score = -float("inf")
best\_t: Optional\[Transformation\] = None
for t in self.transformations:
score = t.alignment\_score(self.state, gradient)
# Optional: potential-based gating could go here.
if score > best\_score:
best\_score = score
best\_t = t
return best\_t
def step(self, goal: Dict\[str, Any\]) -> Dict\[str, Any\]:
"""
One closed-loop evolution step:
1. Measure potentials (for diagnostics).
2. Estimate desired gradient.
3. Select best-aligned transformation.
4. Apply transformation to update state + manifold.
5. Enforce invariants and clamp state.
"""
# 1. Evaluate potentials
F\_val = self.potentials.F\_rep(self.state, self.manifold)
M\_val = self.potentials.M(self.state, self.manifold, goal)
W\_val = self.potentials.W(self.state, self.manifold)
# 2. Gradient
gradient = self.estimate\_gradient(goal)
# 3. Transformation
transformation = self.select\_transformation(gradient, goal)
if transformation is None:
return {
"status": "no-op",
"reason": "no\_transformations\_available",
"state": self.state,
"F": F\_val,
"M": M\_val,
"W": W\_val,
}
# 4. Apply
ctx = TransformationContext(
state=self.state,
manifold=self.manifold,
goal=goal,
)
new\_state, new\_manifold = transformation.apply\_fn(ctx)
# 5. Clamp & update
new\_state = new\_state.clamp()
self.state = new\_state
self.manifold = new\_manifold
return {
"status": "ok",
"transformation": [transformation.name](http://transformation.name),
"state": self.state,
"F": F\_val,
"M": M\_val,
"W": W\_val,
}
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5. Default simple potentials
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def default_F_rep(state: StateVector, manifold: Manifold) -> float: """ Toy representation free-energy: - Penalize distance from target coherence band. - Penalize very high entropy when coherence is low. """ C, E, R, T, X = state.as_tuple() # Preferred coherence band [0.6, 0.9] if C < 0.6: band_penalty = 0.6 - C elif C > 0.9: band_penalty = C - 0.9 else: band_penalty = 0.0
entropy\_penalty = max(0.0, E - C) # entropy exceeding coherence
return band\_penalty + entropy\_penalty
def default_M(state: StateVector, manifold: Manifold, goal: Dict[str, Any]) -> float: """ Simple meaning alignment: - Higher when state components point in the same direction as requested changes in the goal. """ # Interpret goal deltas as a pseudo-target state c, e, r, t, x = state.as_tuple() target = StateVector( coherence=c + float(goal.get("dC", 0.0)), entropy=e + float(goal.get("dE", 0.0)), resonance=r + float(goal.get("dR", 0.0)), temperature=t + float(goal.get("dT", 0.0)), coupling=x + float(goal.get("dX", 0.0)), ).clamp()
# Alignment = inverse of distance
dist = state.distance(target)
return 1.0 / (1.0 + dist)
def default_W(state: StateVector, manifold: Manifold) -> float: """ Wonder / exploration potential: - High when entropy is moderate and temperature is not too low. """ C, E, R, T, X = state.as_tuple() # Prefer mid-range entropy and mid-high temperature entropy_term = 1.0 - abs(E - 0.5) temp_term = 1.0 - abs(T - 0.6) return max(0.0, (entropy_term + temp_term) / 2.0)
def make_default_potentials() -> Potentials: return Potentials( F_rep=default_F_rep, M=default_M, W=default_W, )
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6. Example transformations
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def refine_for_coherence(ctx: TransformationContext) -> Tuple[StateVector, Manifold]: """ Example transformation: - Increase coherence and resonance. - Slightly reduce entropy and temperature. - In practice, you would also modify ctx.manifold.artifacts to make them more structured/organized. """ s = ctx.state C, E, R, T, X = s.as_tuple()
new\_state = StateVector(
coherence=C + 0.1,
entropy=E - 0.05,
resonance=R + 0.08,
temperature=T - 0.03,
coupling=X,
)
# Here we simply annotate the manifold; real code would rewrite artifacts.
m = ctx.manifold
m.meta.setdefault("log", \[\]).append("refine\_for\_coherence applied")
return new\_state, m
def explore_entropy(ctx: TransformationContext) -> Tuple[StateVector, Manifold]: """ Example transformation: - Increase entropy (explore more possibilities). - Slightly increase temperature. - Risk a small drop in coherence. """ s = ctx.state C, E, R, T, X = s.as_tuple()
new\_state = StateVector(
coherence=C - 0.03,
entropy=E + 0.12,
resonance=R,
temperature=T + 0.07,
coupling=X,
)
m = ctx.manifold
m.meta.setdefault("log", \[\]).append("explore\_entropy applied")
return new\_state, m
def make_default_transformations() -> List[Transformation]: return [ Transformation( name="refine_for_coherence", apply_fn=refine_for_coherence, ideal_state=StateVector( coherence=0.7, entropy=0.4, resonance=0.8, temperature=0.5, coupling=0.8, ), cost=1.0, ), Transformation( name="explore_entropy", apply_fn=explore_entropy, ideal_state=StateVector( coherence=0.5, entropy=0.7, resonance=0.5, temperature=0.7, coupling=0.6, ), cost=1.2, ), ]
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7. Factory for a default engine
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def make_default_engine( initial_state: Optional[StateVector] = None, initial_artifacts: Optional[List[str]] = None, ) -> Engine: if initial_state is None: initial_state = StateVector( coherence=0.72, entropy=0.48, resonance=0.78, temperature=0.52, coupling=0.83, ) if initial_artifacts is None: initial_artifacts = []
manifold = Manifold(artifacts=initial\_artifacts, meta={})
potentials = make\_default\_potentials()
transformations = make\_default\_transformations()
return Engine(
state=initial\_state,
manifold=manifold,
potentials=potentials,
transformations=transformations,
)
if __name__ == "__main__": # Example usage in a cognitive space: engine = make_default_engine( initial_artifacts=[ "Draft: Physics-Guided Programming on Symbolic Manifolds", "Notes: C/E/R/T/X mapping for codebases", ] )
goal = {
"dC": +0.15, # increase coherence
"dR": +0.1, # increase resonance
"dE": -0.05, # gently lower entropy
"min\_X": 0.8, # keep coupling high
}
result = engine.step(goal)
print("Step result:", result)
print("New state:", engine.state)
print("Manifold log:", engine.manifold.meta.get("log"))
Posted Dec 8, 2025, 6:58 AM UTC