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RealignR — A Lifelong Adaptive Optimization System

RealignR is a framework for creating self‑optimizing AI systems that learn how to learn, adapt what they remember, and evolve how they compute. It fuses a physics‑inspired optimizer (ARP), an inference‑time memory layer, and a planned adaptive routing substrate.

TL;DR: RealignR = ARP Optimizer (training) + ARP Memory (inference) + Substrate (future) — each works alone or together.

Highlights

Multi‑layer adaptivity: training, inference, and compute all adapt dynamically
Physics‑inspired: conductance‑style reinforcement & decay (memristor/synapse analogy)
Composable: use just the optimizer, just the memory, or both
Practical: drop‑in PyTorch optimizer + lightweight attention memory hook

Components

1) ARP Optimizer (training)

Adaptive conductance update:

[ \frac{dG_{ij}}{dt} = \alpha \left|\nabla_{ij}\right| - \mu , G_{ij} ]

Reinforces pathways under high gradient pressure
Decays unused/noisy connections
Improves convergence, robustness, and generalization

Minimal PyTorch reference

python

import torch

class ARP(torch.optim.Optimizer):
    def __init__(self, params, lr=1e-3, alpha=1e-2, mu=1e-3):
        defaults = dict(lr=lr, alpha=alpha, mu=mu)
        super().__init__(params, defaults)

    @torch.no_grad()
    def step(self):
        for group in self.param_groups:
            lr, alpha, mu = group['lr'], group['alpha'], group['mu']
            for p in group['params']:
                if p.grad is None:
                    continue
                state = self.state.setdefault(p, {})
                G = state.get('G', torch.zeros_like(p.data))
                g = p.grad
                # Conductance dynamics
                G.add_(alpha * g.abs() - mu * G)
                state['G'] = G
                # Resistance‑aware update (example scaling)
                p.addcdiv_(g, (1.0 + G), value=-lr)

2) ARP Memory (inference)

A lightweight, signal‑aware memory that tracks token/feature pressure and re‑injects it into attention.

Extends focus beyond fixed context windows
Biases attention toward semantically “pressurized” items
Produces memory heatmaps for interpretability

Illustrative hook for attention bias

python

# attn_scores: [B, H, Q, K]
# memory_vec:  [B, 1, 1, K] accumulated per‑key "pressure"
def apply_arp_memory(attn_scores, memory_vec, beta=0.2):
    return attn_scores + beta * memory_vec

3) ARP Substrate (roadmap)

A real‑time adaptive routing layer that reshapes compute pathways under stress:

Monitors gradient/signal load across the full model
Reroutes to relieve pressure (self‑healing)
Targets energy optimization & hardware‑level learning

Quickstart

Optimizer drop‑in

python

model = ...
opt = ARP(model.parameters(), lr=1e-3, alpha=1e-2, mu=1e-3)

for x, y in loader:
    opt.zero_grad(set_to_none=True)
    out = model(x)
    loss = loss_fn(out, y)
    loss.backward()
    opt.step()

Inference‑time memory (toy example)

python

memory_vec = None
for tokens in stream():
    logits, attn_scores = model.forward_with_scores(tokens)
    pressure = attn_scores.abs().mean(dim=(0,1,2), keepdim=True)
    memory_vec = pressure if memory_vec is None else 0.9 * memory_vec + 0.1 * pressure
    attn_scores = apply_arp_memory(attn_scores, memory_vec, beta=0.2)
    # continue generation…

Benchmarks

Classification (training with ARP) (single‑node runs; see scripts for exact hyperparameters)

Dataset	Top‑1 Acc.
CIFAR‑10	96.0%+
CIFAR‑100	85.0%+
SVHN	94.5%+

Across all datasets, ARP retained performance through multiple switches while maintaining low variance and clean GradNorm.

System Modules

The repository contains several key components:

RealignR.py — main pretrain and ARP‑continue script with dataset switching and CPR logic.
arp_optimizer.py — Adaptive Resistance Principle optimizer (PyTorch).
dataset_schedule.json — JSON file specifying when to switch datasets during training.
switcher/ — module providing load_dataset, load_dataset_schedule, and get_current_dataset functions for dataset switching.
watcher.py — optional live controller that uses TensorBoard metrics and GPT feedback to adjust alpha/mu and trigger CPR resets.

Design Philosophy

Multi‑dimensional adaptivity: training, inference, compute
Neuromorphic readiness: conductance & decay mirror synapses/memristors
Composability: plug in only what you need
Transparency: memory heatmaps & metric trails

Use Cases

Meta‑optimization & optimizer research
Continual/online learning with domain‑specific memory
Reinforcement‑learning–style auto‑tuners
Long‑context language modeling with adaptive focus

Roadmap

Meta‑optimizer: fine‑tune GPT controller on RealignR’s history
Curriculum phases: schedule α/μ/CPR across stages
Live dashboard: memory heatmaps + decision timelines
Substrate: adaptive routing over compute graphs

Limitations & Notes

Metrics are preliminary and may vary with hyper‑params/hardware.
ARP Memory is an additive bias; calibrate beta to avoid instability.
GPT‑assisted control is optional; the optimizer stands alone.
Always validate on your domain—no optimizer is universally best.

Repro Tips

Start with alpha=1e-2, mu=1e-3 for a stable baseline.
Log G_mean and GradNorm; keep G_mean bounded.
Enable CPR when loss plateaus or GradNorm collapses.
Save per‑dataset G_{ij} states for smooth domain switching.

Citation

If you use RealignR or ARP Memory, please cite:

@software{realignr_2025,
  author  = {Ryan McKenna},
  title   = {RealignR: A Lifelong Adaptive Optimization System},
  year    = {2025},
  url     = {https://huggingface.co/RealignR/RealignR},
  note    = {ARP optimizer + inference‑time memory}
}

Contributions

Issues, PRs, and collaborators welcome!
Contact: ryanmckenna26@gmail.com

RealignR is more than an optimizer — it’s a new way to think about intelligence.