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https://github.com/blackboxprogramming/BlackRoad-Operating-System.git
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0108860bff
Create comprehensive research-lab pack structure with mathematical and quantum computing modules from blackroad-prism-console: Math Modules: - hilbert_core.py: Hilbert space symbolic reasoning - collatz/: Distributed Collatz conjecture verification - linmath/: Linear mathematics C library - lucidia_math_forge/: Symbolic proof engine - lucidia_math_lab/: Experimental mathematics Quantum Modules: - lucidia_quantum/: Quantum core - quantum_engine/: Circuit simulation Experiments: - br_math/: Gödel gap, quantum experiments Includes pack.yaml manifest and comprehensive README. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
"""Language–Lindblad step prototype.
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Implements a single Euler–Maruyama update of logits inspired by the
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Language–Lindblad equation. The function is intentionally tiny and
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framework agnostic; callers supply gradients for prompt, memory, kindness
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and harm potentials.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Callable
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import numpy as np
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@dataclass
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class Potentials:
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"""Container for gradient callables.
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Each callable accepts the current logits and returns a gradient with
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the same shape.
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"""
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grad_prompt: Callable[[np.ndarray], np.ndarray]
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grad_memory: Callable[[np.ndarray], np.ndarray]
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grad_kindness: Callable[[np.ndarray], np.ndarray]
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grad_harm: Callable[[np.ndarray], np.ndarray]
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def llb_step(
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logits: np.ndarray,
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potentials: Potentials,
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dt: float = 1e-2,
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noise_scale: float = 1e-3,
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lam_k: float = 1.0,
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lam_h: float = 1.0,
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) -> np.ndarray:
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"""Perform one stochastic update of the logits.
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Parameters
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----------
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logits: current logit vector.
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potentials: gradients of prompt, memory, kindness and harm potentials.
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dt: integration step.
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noise_scale: standard deviation of gaussian noise.
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lam_k: kindness strength.
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lam_h: harm penalty.
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"""
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lap = np.zeros_like(logits) # placeholder for \nabla^2_E logit smoothing
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grad = (
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lap
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- potentials.grad_prompt(logits)
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- potentials.grad_memory(logits)
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+ lam_k * potentials.grad_kindness(logits)
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- lam_h * potentials.grad_harm(logits)
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)
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noise = np.random.normal(scale=noise_scale, size=logits.shape)
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return logits + dt * grad + noise
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__all__ = ["Potentials", "llb_step"]
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