Rth-lm-25b / RTH-LM_TECH_PAPER_STANDALONE.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>RTH-LM Technical Paper</title>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link
href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&family=Libre+Baskerville:ital,wght@0,400;0,700;1,400&family=JetBrains+Mono&display=swap"
rel="stylesheet">
<style>
:root {
--primary: #000;
--secondary: #333;
--text-main: #1a1a1a;
--text-muted: #444;
--bg-paper: #ffffff;
--bg-site: #e0e0e0;
--font-body: 'Inter', sans-serif;
--font-serif: 'Libre Baskerville', serif;
--font-mono: 'JetBrains Mono', monospace;
}
* {
box-sizing: border-box;
}
body {
margin: 0;
padding: 0;
background-color: var(--bg-site);
font-family: var(--font-body);
color: var(--text-main);
line-height: 1.6;
}
/* Page Layout Simulation */
.page {
width: 210mm;
min-height: 297mm;
padding: 30mm 25mm;
margin: 40px auto;
background: var(--bg-paper);
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.15);
position: relative;
}
.header-section {
text-align: center;
margin-bottom: 50px;
}
.logo {
max-width: 180px;
margin-bottom: 30px;
}
h1 {
font-family: var(--font-serif);
font-size: 2rem;
line-height: 1.25;
margin: 0 0 15px 0;
font-weight: 700;
}
.author {
font-size: 1.2rem;
font-weight: 600;
margin-bottom: 5px;
}
.affiliation {
font-size: 1rem;
color: var(--text-muted);
margin-bottom: 5px;
}
.contact {
font-size: 0.9rem;
margin-bottom: 5px;
font-family: var(--font-mono);
}
.sub-meta {
font-size: 0.9rem;
color: #666;
font-style: italic;
margin-top: 10px;
}
/* Typography */
h2 {
font-family: var(--font-serif);
font-size: 1.5rem;
margin-top: 40px;
margin-bottom: 15px;
border-bottom: 1px solid #ccc;
padding-bottom: 5px;
}
h3 {
font-size: 1.15rem;
margin-top: 25px;
margin-bottom: 10px;
font-weight: 700;
}
p {
margin-bottom: 15px;
text-align: justify;
}
ul,
ol {
margin-bottom: 15px;
padding-left: 20px;
}
li {
margin-bottom: 8px;
}
.abstract {
margin: 30px 40px;
padding: 20px 0;
border-top: 1px solid #eee;
border-bottom: 1px solid #eee;
}
.abstract h2 {
border: none;
text-align: center;
font-size: 1.1rem;
margin-top: 0;
text-transform: uppercase;
letter-spacing: 1px;
font-family: var(--font-body);
}
/* Math Styling */
.equation {
display: flex;
justify-content: space-between;
align-items: center;
margin: 20px 0;
padding: 0 20px;
font-family: var(--font-serif);
font-style: italic;
font-size: 1.1rem;
}
.eq-content {
flex-grow: 1;
text-align: center;
}
.eq-num {
font-style: normal;
font-size: 0.9rem;
color: #666;
width: 40px;
text-align: right;
}
/* Table Styling */
table {
width: 100%;
border-collapse: collapse;
margin: 25px 0;
font-size: 0.9rem;
}
th,
td {
border: 1px solid #000;
padding: 10px;
text-align: left;
vertical-align: top;
}
th {
background-color: #f2f2f2;
}
.table-caption {
text-align: center;
font-style: italic;
font-size: 0.85rem;
margin-bottom: 20px;
}
/* Footer/Citation */
.citation-section {
margin-top: 60px;
padding-top: 20px;
border-top: 2px solid #000;
font-size: 0.9rem;
}
.page-num {
position: absolute;
bottom: 20px;
right: 30px;
font-size: 0.8rem;
color: #ccc;
}
@media print {
body {
background: white;
}
.page {
margin: 0;
box-shadow: none;
width: 100%;
page-break-after: always;
}
.no-print {
display: none;
}
}
</style>
</head>
<body>
<!-- PAGE 1 -->
<div class="page">
<div class="header-section">
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" alt="(RTH Logo)" class="logo">
<h1>RTH-LM: A Fractal Temporal Convolutional Language Model</h1>
<div class="author">Christian Quintino De Luca</div>
<div class="affiliation">RTH Italia (Research & Technology Hub), Milan, Italy</div>
<div class="contact">info@rthitalia.com</div>
<div class="sub-meta">February 2026</div>
<div class="sub-meta">License: CC BY-NC 4.0 (Research) / Commercial License (RTH Italia)</div>
<div class="sub-meta">Code: <a href="https://github.com/rthgit/ZetaGrid"
style="color: inherit; text-decoration: none;">https://github.com/rthgit/ZetaGrid</a></div>
</div>
<div class="abstract">
<h2>Abstract</h2>
<p>
We introduce RTH-LM, a Fractal Gated Causal Temporal Convolutional Network (TCN) for language modeling,
designed as an alternative to attention-centric architectures. RTH-LM targets linear-time inference in
sequence length and improved data/compute efficiency under constrained training regimes. The model
family is organized around a modular separation between a compact shared frozen core (the Genome) and
trainable low-rank adapters (the Soul), enabling rapid domain specialization with minimal update
artifacts.
</p>
<p>
This paper presents: (i) the Fractal TCN backbone and scaling strategy, (ii) the Genome/Soul modular
deployment design, and (iii) initial training signals showing stable convergence under deliberately
restricted data. We also provide a conservative hardware feasibility analysis indicating that a
120B-parameter scaled variant—deployed with 2-bit weight-only quantization—can fit on 80GB-class GPUs,
depending on runtime state and inference-engine overhead. Finally, we outline a practical pathway to a
1T-parameter vision using a compact 8–9× H100 80GB cluster for sharded inference and incremental
expansion workflows.
</p>
</div>
<section id="s1">
<h2>1 Introduction</h2>
<p>
Transformer architectures dominate modern language modeling, but their operational profile often becomes
the limiting factor in real deployments: quadratic attention costs, high memory pressure at long
context, and a prevailing reliance on extremely large pretraining corpora. These constraints raise
barriers for independent research groups and small companies, and they increase energy requirements at
scale.
</p>
<p>
RTH-LM explores a different axis of scaling: architectural structure over brute-force data scaling. The
core hypothesis is that long-range dependency modeling can be achieved using deep causal temporal
convolutions combined with gating and a fractal block expansion strategy, reducing reliance on explicit
attention mechanisms.
</p>
<h3>1.1 Contributions</h3>
<ol>
<li>Fractal Gated Causal TCN Backbone: a deep, dilated, causal convolutional stack with gating and
residual routing for autoregressive language modeling, designed for linear-time inference in
sequence length.</li>
<li>Genome/Soul Modularity: a deployable separation between a shared frozen Genome core and trainable
Soul adapters (LoRA-style), enabling fast specialization with minimal retraining and small update
artifacts.</li>
<li>Constrained-Regime Training Signals: training is intentionally performed on a small curated dataset
to emphasize architectural learning dynamics and feasibility under limited compute/data.</li>
<li>Conservative Memory & Hardware Feasibility: a planning-grade VRAM model for a 120B scaled variant
under 2-bit weight-only quantization, with explicit assumptions and bounded estimates.</li>
</ol>
</section>
<div class="page-num">1</div>
</div>
<!-- PAGE 2 -->
<div class="page">
<section id="s1.2">
<h3>1.2 Scope and Non-Claims</h3>
<p>
This paper focuses on feasibility, training stability, and modular deployment design. It does not claim
parity with frontier-scale instruction-tuned Transformer systems trained on trillions of tokens.
Instead, it addresses: How far can capacity and usability be pushed under tight data/compute constraints
using a non-attention backbone?
</p>
</section>
<section id="s2">
<h2>2 Background and Motivation</h2>
<h3>2.1 Why Replace Attention?</h3>
<p>
Attention provides flexible token-to-token routing but incurs costs that become dominant at long context
and high-throughput serving. Many real-world deployments are constrained not by FLOPs alone but by
memory bandwidth, allocator fragmentation, and context-state growth. RTH-LM aims to reduce these
constraints using temporal mixing via convolution and gating, relying on depth/dilation schedules rather
than all-pairs interactions.
</p>
<h3>2.2 Temporal Convolutions for Sequence Modeling</h3>
<p>
Causal dilated convolutions can cover long receptive fields using dilation schedules. With sufficient
depth, the model can integrate information across large contexts with predictable compute, which is
attractive for streaming and on-premises inference.
</p>
</section>
<section id="s3">
<h2>3 RTH-LM Architecture</h2>
<h3>3.1 Overview</h3>
<p>
RTH-LM consists of: (i) tokenizer/embeddings, (ii) a Fractal Gated Causal TCN backbone, (iii) an output
head, and (iv) optional modular adapters (the Soul) for domain specialization. The backbone is organized
as a stack of Fractal Blocks, each composed of multiple gated causal convolutional layers with residual
pathways and normalization.
</p>
<h3>3.2 Gated Causal Convolutional Layer</h3>
<p>Let <em>x</em> &isin; &reals;<sup><em>T</em> &times; <em>d</em></sup> be the sequence representation.
Each layer performs:</p>
<div class="equation">
<div class="eq-content">h = Conv1D<sub>causal, dilated</sub>(x)</div>
<div class="eq-num">(1)</div>
</div>
<div class="equation">
<div class="eq-content">[h<sub>g</sub>, h<sub>v</sub>] = split(W h)</div>
<div class="eq-num">(2)</div>
</div>
<div class="equation">
<div class="eq-content">y = &sigma;(h<sub>g</sub>) &odot; &phi;(h<sub>v</sub>)</div>
<div class="eq-num">(3)</div>
</div>
<div class="equation">
<div class="eq-content">x' = x + Dropout(W<sub>o</sub>y)</div>
<div class="eq-num">(4)</div>
</div>
<div class="equation">
<div class="eq-content">x&#770; = Norm(x')</div>
<div class="eq-num">(5)</div>
</div>
</section>
<div class="page-num">2</div>
</div>
<!-- PAGE 3 -->
<div class="page">
<section id="s3.3">
<h3>3.3 Fractal Block Expansion</h3>
<p>The fractal property refers to a scalable block composition strategy:</p>
<ul>
<li>A base model is built from repeated block templates (a repeated micro-architecture).</li>
<li>Larger models are formed by mirroring/replicating block groups and re-initializing only minimal
routing/scaling parameters.</li>
<li>Scaling is followed by brief stabilization training and/or adapter re-optimization.</li>
</ul>
<h3>3.4 The Frozen Genome</h3>
<p>
RTH-LM defines a compact shared core parameter artifact called the Genome, reused across instances. In
the reference configuration motivating this paper, the Genome is engineered to be storage-efficient
(single-digit GB class depending on serialization and quantization).
</p>
<h3>3.5 The Liquid Soul (Adapters)</h3>
<p>
Domain adaptation is performed via trainable low-rank adapters, called the Soul. Souls are small
relative to the full model footprint and can be swapped to change domain behavior (coding, technical
writing, creative) without full retraining.
</p>
</section>
<section id="s4">
<h2>4 Training Methodology</h2>
<h3>4.1 Objective</h3>
<p>RTH-LM is trained using autoregressive next-token prediction:</p>
<div class="equation">
<div class="eq-content">L = - &sum; log p<sub>&theta;</sub>(x<sub>t</sub> | x<sub>&lt;t</sub>)</div>
<div class="eq-num">(6)</div>
</div>
<h3>4.2 Data Regime</h3>
<p>Training is intentionally constrained to emphasize architectural learning dynamics:</p>
<ul>
<li>Dataset size: &sim;1.5GB curated text (scientific + narrative mix)</li>
<li>Rationale: feasibility under minimal data, not maximal benchmark parity</li>
</ul>
<h3>4.3 Compute Setup (Reference Run)</h3>
<ul>
<li>Hardware: single NVIDIA A40</li>
<li>Precision: mixed precision (FP16/BF16, engine-dependent)</li>
<li>Stability tools: gradient accumulation, checkpointing, norm management</li>
<li>Duration: &sim;24 hours end-to-end training loop</li>
</ul>
</section>
<div class="page-num">3</div>
</div>
<!-- PAGE 4 -->
<div class="page">
<section id="s5">
<h2>5 Results and Observations (Initial Signals)</h2>
<h3>5.1 Convergence Snapshot</h3>
<p>At the end of the referenced constrained-regime run, we report the following training snapshot:</p>
<ul>
<li>Step: 15,000</li>
<li>Training loss: &approx; 1.0</li>
<li>Perplexity: &approx; 2.8</li>
</ul>
<p>These values are reported as an empirical training signal for feasibility; broader generalization
evaluation is outlined in Appendix A.</p>
</section>
<section id="s6">
<h2>6 Transformer Baseline: Practical Cost & Time Comparison</h2>
<p>To contextualize RTH-LM, we provide a practical comparison against an “economical” Transformer baseline,
focusing on deployment scaling drivers: context-state growth, long-context overhead, and planning-grade
cost/time implications.</p>
<table>
<thead>
<tr>
<th>Dimension</th>
<th>Economical Transformer (typical)</th>
<th>RTH-LM (Fractal TCN)</th>
</tr>
</thead>
<tbody>
<tr>
<td>Long-context memory</td>
<td>KV cache grows with context and layers; often FP16/BF16</td>
<td>No classical KV cache; runtime state can be engineered as streaming buffers</td>
</tr>
<tr>
<td>Inference scaling</td>
<td>Full attention can be <em>O</em>(<em>N</em><sup>2</sup>); optimized variants mitigate</td>
<td>Backbone compute designed for linear-time in sequence length</td>
</tr>
<tr>
<td>Domain adaptation</td>
<td>Full fine-tune costly; adapters common (LoRA)</td>
<td>Genome/Soul split: compact Souls enable fast specialization</td>
</tr>
<tr>
<td>Evidence under small data</td>
<td>Small data often insufficient for broad generality</td>
<td>Stable snapshot at 15k steps (loss &approx; 1.0, ppl &approx; 2.8)</td>
</tr>
</tbody>
</table>
<div class="table-caption">Table 1: Planning-grade comparison.</div>
</section>
<section id="s7">
<h2>7 Memory Model and Hardware Feasibility</h2>
<h3>7.1 Weight Storage Under 2-bit Quantization (120B)</h3>
<p>Idealized raw size:</p>
<div class="equation">
<div class="eq-content">120 &times; 10<sup>9</sup> &times; 2/8 = 30.0 GB</div>
<div class="eq-num">(7)</div>
</div>
<p>Conservative planning range: Weights (2-bit, weight-only): &sim;30&ndash;36 GB VRAM
(implementation-dependent).</p>
</section>
<div class="page-num">4</div>
</div>
<!-- PAGE 5 -->
<div class="page">
<section id="s7.2">
<h3>7.2 Runtime State: “KV-Equivalent” Upper Bound</h3>
<p>For conservative comparison, we report a Transformer-style KV-cache equivalent (FP16) upper bound under
reference assumptions L = 36, Gkv = 8, dh = 64. FP16 uses 2 bytes per element:</p>
<div class="equation">
<div class="eq-content">KV/token &approx; 2 &times; L &times; (G<sub>kv</sub> &times; d<sub>h</sub>)
&times; 2 bytes &approx; 72 KB/token</div>
<div class="eq-num">(8)</div>
</div>
<p>KV-equivalent state (upper bound): 8k &sim;0.59 GB; 32k &sim;2.36 GB; 128k &sim;9.44 GB.</p>
<h3>7.3 Total VRAM Estimate (Batch=1)</h3>
<p>Total (128k, batch=1): &sim;45&ndash;57 GB VRAM (weights + upper-bound state + runtime overhead). This is
compatible with 80GB-class GPUs (H100/A100 class), subject to engine details.</p>
</section>
<section id="s8">
<h2>8 Cluster Feasibility: The 1T-Parameter Vision on 8–9× H100</h2>
<p>A 1T-parameter model quantized to 2-bit weight-only has raw size:</p>
<div class="equation">
<div class="eq-content">1 &times; 10<sup>12</sup> &times; 2/8 = 250 GB</div>
<div class="eq-num">(9)</div>
</div>
<p>We report a conservative range of &sim;250&ndash;300 GB for weights across GPUs. While 4 &times; 80GB is
the theoretical minimum for weights-only, production inference benefits from 6 &times; H100 80GB as a
baseline (weights + overhead + long-context state), and 8&ndash;9 GPUs for throughput, replicas, and
multi-tenant Soul serving, subject to engine implementation and sharding strategy.</p>
</section>
<section id="appendix-a">
<h2>A Evaluation Plan and Minimal Evidence Pack</h2>
<p>Minimal reviewer-proof evidence bundle:</p>
<ul>
<li>Held-out perplexity on a fixed validation split.</li>
<li>1&ndash;2 lightweight benchmarks or subsets to detect collapse/overfit.</li>
<li>Fixed qualitative prompt suite (10&ndash;20 prompts) with deterministic decoding.</li>
</ul>
</section>
<div class="page-num">5</div>
</div>
<!-- PAGE 6 -->
<div class="page">
<section id="appendix-b">
<h2>B Fractal Expansion Protocol (Planning-Grade)</h2>
<p>
Replication/mirroring of block groups followed by re-initialization of only stabilization parameters
(norm gains, routing scalars, minimal mixing coefficients) and a short stabilization run. Adapter
training (Souls) performs domain specialization while keeping the expanded Genome stable for deployment.
</p>
</section>
<section id="appendix-c">
<h2>C Reproducibility Checklist</h2>
<p>
Commit hash + environment; tokenizer hash; dataset manifest + split IDs; full training config (seq,
batch, optimizer, LR schedule); logs (step-time, loss, ppl, memory) and checkpoint hashes.
</p>
</section>
<section class="citation-section">
<div class="author" style="font-size: 1rem;">Citation</div>
<p>
De Luca, C. Q. (2026). RTH-LM: A Fractal Temporal Convolutional Language Model. RTH Italia. Repository:
https://github.com/rthgit/ZetaGrid
</p>
<div class="author" style="font-size: 1rem; margin-top: 20px;">Contact</div>
<p>info@rthitalia.com | RTH Italia</p>
</section>
<div class="page-num">6</div>
</div>
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