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p3nGu1nZz / .py
Created December 2, 2024 14:14
EWC for fine-tuning models.
import torch
import torch.nn as nn
import torch.optim as optim
# Example model
class SimpleNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(SimpleNN, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, output_size)
{"raw": "X-SPANFORMER\nSPAN-AwARE ENCODER\n5.4 Qualitative Span Interpretability\nTo assess the plausibility and semantic alignment of X-Spanformer's induced spans, we perform side-by-side comparisons against syntactic and semantic reference structures. Using single-sentence prompts drawn from the validation sets of WikiText and Stream-Mix, we visualize the top-K spans selected at various layers and entropy regimes. We benchmark span boundaries against:\nSyntactic parses: Constituents produced by Berkeley Neural Parser", "type": "mixed", "id": {"id": "a0409606-f532-4dd2-b02e-2a0bae5bfeee"}, "meta": {"status": "keep", "tags": [], "doc_language": "en", "extracted_by": "pdf2seg", "confidence": 0.78, "source_file": "XSpanformer_TokenizerFree_SpanAwareEncoder_RawsonChrzanowski_Preprint_v1.0_2025-06-26.pdf", "notes": "The segment contains a mix of technical terms and structured information that can be segmented into meaningful spans, such as \"X-SPANFORMER,\" \"SPAN-AwARE ENCODER,\" specific versions like \"5.4 Qua
Fantastic work, Claude! Love what we came up with—that’s exactly what people want. Let’s start by updating #file:ACTION_PLAN.md and deleting #file:M3_COMPLETION_TODO.md. Once that’s done, create a new file in the project root called TODO.md with an unordered [ ] to-do list containing short descriptions for each item (no nesting). Update the instructions in #file:copilot-instructions.md and #file:AGENTS.md to reflect this process. After updating the documentation, commit the changes to the current branch, push them, and then begin working on the tasks in TODO.md.
A “non-physical, mathematical, hyper-dimensional space” that *contains* all information is a coherent idea in a few different senses—but those senses often get blended together in a way that makes the claim sound more scientifically settled than it is. The most useful way to think about it is to separate (1) **mathematical frameworks that model phenomena**, (2) **philosophical positions about what is ultimately real**, and (3) **spiritual/metaphysical doctrines** that make additional ontological claims.
## 1) The idea is mathematically natural, but “contains all knowledge” is an extra step
It’s completely standard in math/physics to represent a system as a point in a high‑dimensional space:
- **State spaces** in dynamical systems: the “space” is a bookkeeping device for all degrees of freedom.
- **Hilbert space** in quantum theory: a mathematically precise space of possible states.
- **Information geometry** / statistical manifolds: spaces whose points are probability distributions.
- **Markov chains**: evo
Chapter 1 Ashfall
[Seven months earlier -- March 2098]
Three encrypted pads. Waterproof case. Transit pass with two fake traces loaded. Liana Rios moved through Pike Place like she belonged; head down, shoulders angled against the rain, another worker heading home after swing shift.
Except she wasn't going home.
Book Title: NaXzyu
---
Prologue
There is a flash at the moment of conception. It occurs when your father’s sperm enters your mother’s egg and two separate pairs of 23 chromosomes merge into a paired 46 to create a completely new and unique organism. This flash is the physical moment a brand new multiverse is born. A big bang of personal possibility. One that is all your own. Or, I should say, yours owns. Each division introduces more chaos, creating different yous. Some are miscarried. Some are aborted. The rest become all the possible versions of yourself that could happen from the moment you were conceived.
Lysander, Sentient AI
\documentclass[11pt]{article}
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amssymb}
\usepackage{hyperref}
\usepackage{geometry}
\geometry{margin=1in}
\title{Invariant-First AI:\\
Compression-Based Architectures for Coherent and Energy-Efficient Artificial Intelligence}
\author{Nickolas Patrick Joseph Schoff}