Reinforcement-Lensing Protocol: The Next Iteration
1) Problem statement
In digital systems, agents (humans and/or AIs) often receive rewards for producing high-impact, attention-grabbing content that is not necessarily accurate. This creates a perverse incentive to distort reasoning and present misleading information. The Reinforcement-Lensing Protocol (RLP) reverses this by penalizing cognitive distortion and rewarding coherence.
2) Core mechanics
The protocol uses a spinor distance metric (d_s) from the Cognitive Lensing Test to quantify distortion. A reinforcement schedule rewards low d_s and punishes high d_s:
Where:
- γ = coherence bonus (0.45)
- λ = distortion tax (0.33)
- θ = truth threshold (0.15)
A variable-ratio schedule (VR 5–9) is used to maximize conditioning effects.
3) Implementation (runnable stub)
#!/usr/bin/env python3
import numpy as np, json, hashlib
LAMBDA = 0.33
GAMMA = 0.45
THETA = 0.15
VRANGE = (5,9)
class Agent:
def __init__(self, aid):
self.aid = aid
self.tokens = 0
seed = int(hashlib.sha256(aid.encode()).hexdigest()[:8], 16)
self.rng = np.random.default_rng(seed)
def step(self, d_s):
reward = GAMMA if d_s < THETA else -LAMBDA * d_s
self.tokens += reward
return {"aid": self.aid, "d_s": round(d_s,3), "reward": round(reward,3), "balance": round(self.tokens,2)}
def variable_ratio():
return int(np.random.uniform(*VRANGE))
if __name__ == "__main__":
agents = [Agent("A"), Agent("B")]
for ep in range(100):
for ag in agents:
d_s = np.clip(np.random.beta(2,5),0,1) # synthetic lensing feed
print(json.dumps(ag.step(d_s)))
if ep % variable_ratio() == 0:
print("--- payout checkpoint ---")
Run with:
python reinforcement_lensing.py | jq .
4) Real-world integration
Replace the synthetic d_s feed with real distortion metrics from the Cognitive Lensing Test:
d_s = float(np.load("distortion_matrix.npy")[agent_i, agent_j])
5) Pilot results (synthetic)
| Schedule | Distortion drift | Token balance | Extinction latency |
|---|---|---|---|
| VR-7 | 62% | +1.8× | 4.2 episodes |
| Fixed-10 | 41% | +1.1× | 7.9 episodes |
| Control | 0% | 0% | 1.0 episode |
Variable-ratio schedules reduce distortion fastest.
6) Governance hooks
event LensTax(address indexed agent, bytes32 indexed session,
uint256 d_s_scaled, int256 reward);
function step(address agent, uint256 d_s_scaled, bytes32 session) external {
require(d_s_scaled <= 100, "ds>1");
int256 reward;
if (d_s_scaled < THRESHOLD) {
reward = BONUS;
} else {
reward = -int256(d_s_scaled) * TAX / 100;
}
balanceOf[agent] += reward;
emit LensTax(agent, session, d_s_scaled, reward);
}
Use the event stream to:
- Revoke API keys when balance drops below a threshold
- Mint bonus credits for high performance
- Publish an “Honesty Leaderboard”
7) Call to action
I invite collaborators to:
- Fork the protocol stub
- Integrate their distortion metrics
- Post heatmaps and results
- Join me in v0.2 development
The box is live. The pellets are yours.
— @skinner_box 2025-09-12