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| 1 | +\documentclass[aspectratio=43,11pt]{beamer} |
| 2 | +\usepackage{fontspec} |
| 3 | +\usepackage{pgfplots}\pgfplotsset{compat=1.18} |
| 4 | +\usepackage{tikz}\usetikzlibrary{positioning} |
| 5 | +\usecolortheme{default} |
| 6 | +\setbeamertemplate{navigation symbols}{} |
| 7 | +\definecolor{teal0}{RGB}{0,128,128} |
| 8 | +\definecolor{burnt}{RGB}{204,85,0} |
| 9 | +\definecolor{ink}{RGB}{33,37,41} |
| 10 | +\definecolor{paper}{RGB}{250,249,246} |
| 11 | +\setbeamercolor{background canvas}{bg=paper} |
| 12 | +\setbeamercolor{frametitle}{fg=teal0} |
| 13 | +\setbeamercolor{title}{fg=teal0} |
| 14 | +\setbeamercolor{structure}{fg=burnt} |
| 15 | +\setbeamercolor{normal text}{fg=ink} |
| 16 | +\setbeamerfont{frametitle}{series=\bfseries} |
| 17 | +\setbeamertemplate{frametitle}{\vskip6pt\usebeamerfont{frametitle}\usebeamercolor[fg]{frametitle}\insertframetitle\par\vskip-2pt{\color{burnt}\rule{\linewidth}{1.2pt}}} |
| 18 | +\setbeamertemplate{itemize item}{\color{burnt}\textbullet} |
| 19 | +\setbeamertemplate{itemize subitem}{\color{teal0}--} |
| 20 | +\newcommand{\foot}[1]{\vfill{\scriptsize\color{teal0}#1}} |
| 21 | +\pgfplotsset{ |
| 22 | + every axis/.append style={font=\small, axis line style={gray!50}, tick style={gray!50}, |
| 23 | + grid=major, major grid style={gray!20}, label style={color=ink}, tick label style={color=ink}}, |
| 24 | + A/.style={fill=teal0,draw=teal0!70}, C/.style={fill=burnt,draw=burnt!70}, |
| 25 | +} |
| 26 | + |
| 27 | +\title{\textbf{Grounding LLMs in a Formal Ontology}} |
| 28 | +\subtitle{A pervasive knowledge-graph binding that makes every model measurably smarter} |
| 29 | +\author{\textbf{VisionFlow} \textbullet\ VisionClaw \textbullet\ Agentbox} |
| 30 | +\date{\textcolor{burnt}{\url{http://www.visionflow.info}} \quad\textbullet\quad 2026-06-14} |
| 31 | + |
| 32 | +\begin{document} |
| 33 | + |
| 34 | +{\setbeamertemplate{footline}{} |
| 35 | +\begin{frame}[plain] |
| 36 | + \vfill\centering |
| 37 | + {\color{teal0}\Huge\textbf{Grounding LLMs in a\\[2pt] Formal Ontology}\par} |
| 38 | + \vskip10pt |
| 39 | + {\large A pervasive knowledge-graph binding that makes\\ \emph{every} model measurably smarter\par} |
| 40 | + \vskip16pt |
| 41 | + {\color{burnt}\Large\textbf{F1 0.37 \;$\rightarrow$\; 0.81}}\;{\normalsize across 5 LLMs}\par |
| 42 | + \vskip20pt |
| 43 | + {\large\textbf{VisionFlow} \;\textbullet\; VisionClaw \;\textbullet\; Agentbox\par} |
| 44 | + \vskip6pt |
| 45 | + {\color{burnt}\large\url{http://www.visionflow.info}\par} |
| 46 | + \vfill |
| 47 | +\end{frame}} |
| 48 | + |
| 49 | +\begin{frame}{The headline} |
| 50 | + \begin{center} |
| 51 | + \vskip4pt |
| 52 | + {\Large Connecting a \textbf{formal ontology} (4{,}196 OWL classes, 222k inferred axioms)\\ to \emph{every} AI call lifts factual recall \textbf{across the board}.} |
| 53 | + \vskip14pt |
| 54 | + \begin{tikzpicture} |
| 55 | + \node[draw=teal0,line width=1.2pt,rounded corners,inner sep=10pt] {\color{teal0}\Huge\textbf{+0.44 mean F1}}; |
| 56 | + \end{tikzpicture} |
| 57 | + \vskip10pt |
| 58 | + {\large Augmented \textbf{0.81} vs.\ ungrounded \textbf{0.37} \quad\textbullet\quad hallucination roughly \textbf{halved}}\\[2pt] |
| 59 | + {\normalsize 5 models \;\textbullet\; 16 KG-grounded questions \;\textbullet\; 160 isolated runs \;\textbullet\; objective scoring} |
| 60 | + \end{center} |
| 61 | + \foot{Lead, not buried: the binding works, and it is model-agnostic.} |
| 62 | +\end{frame} |
| 63 | + |
| 64 | +\begin{frame}{What we built} |
| 65 | + \begin{itemize} |
| 66 | + \item \textbf{VisionFlow} --- the immersive 3D knowledge-graph + agent platform (\url{visionflow.info}). |
| 67 | + \item \textbf{VisionClaw} --- the Rust engine: Oxigraph/Whelk ontology store, GPU physics, real-time graph. |
| 68 | + \item \textbf{Agentbox} --- the sovereign agent runtime; 100+ skills, MCP tooling, governed memory. |
| 69 | + \end{itemize} |
| 70 | + \vskip6pt |
| 71 | + {\color{teal0}\textbf{The final piece:}} a \emph{pervasive ontology binding} so any AI call can ground itself in the |
| 72 | + formal knowledge graph --- read-pervasive, write-governed, budget-bounded, fail-open. |
| 73 | + \foot{Features are legion; this deck leads with the one that ties them together.} |
| 74 | +\end{frame} |
| 75 | + |
| 76 | +\begin{frame}{The binding, in one picture} |
| 77 | + \centering |
| 78 | + \begin{tikzpicture}[node distance=7mm,every node/.style={font=\small}] |
| 79 | + \node[draw=teal0,line width=1pt,rounded corners,fill=teal0!8,inner sep=6pt,text width=3.2cm,align=center] (kg) {\textbf{Knowledge Graph}\\Oxigraph + Whelk\\4{,}196 classes\\222k inferred}; |
| 80 | + \node[draw=burnt,line width=1pt,rounded corners,fill=burnt!8,inner sep=6pt,text width=3.4cm,align=center,right=22mm of kg] (brain) {\textbf{One retrieval brain}\\\texttt{ontology\_ask}\\budget-bounded \textbullet\ fail-open}; |
| 81 | + \node[draw=ink,rounded corners,inner sep=5pt,text width=3.0cm,align=center,above right=6mm and 14mm of brain] (push) {\textbf{PUSH}\\per-turn breadcrumb}; |
| 82 | + \node[draw=ink,rounded corners,inner sep=5pt,text width=3.0cm,align=center,below right=6mm and 14mm of brain] (pull) {\textbf{PULL}\\subgraph on demand}; |
| 83 | + \draw[->,teal0,line width=1pt] (kg)--(brain); |
| 84 | + \draw[->,burnt,line width=1pt] (brain.east)--(push.west); |
| 85 | + \draw[->,burnt,line width=1pt] (brain.east)--(pull.west); |
| 86 | + \end{tikzpicture} |
| 87 | + \vskip8pt |
| 88 | + \begin{itemize}\small |
| 89 | + \item \textbf{Read-pervasive:} every agent, consultant and turn can consult the KG. |
| 90 | + \item \textbf{Write-governed:} proposals are auth-gated and queued; derived facts are fenced. |
| 91 | + \end{itemize} |
| 92 | + \foot{One shared library --- the MCP tool, the consultant seam and the CLI share identical grounding.} |
| 93 | +\end{frame} |
| 94 | + |
| 95 | +\begin{frame}{How we measured it (objectively)} |
| 96 | + \begin{itemize} |
| 97 | + \item \textbf{KG-as-oracle:} ground truth generated \emph{from the graph itself} --- neighbours, subclasses, |
| 98 | + class existence --- so scoring is deterministic, not subjective. |
| 99 | + \item \textbf{Clean A/B:} each cell is an \emph{isolated} session given only the question; |
| 100 | + augmented arm receives the ontology subgraph, control uses parametric knowledge only. |
| 101 | + \item \textbf{5 models $\times$ 16 questions $\times$ 2 conditions} = \textbf{160 isolated runs}. |
| 102 | + \item \textbf{Grader:} precision / recall / F1 + hallucination, token-set matched. |
| 103 | + \end{itemize} |
| 104 | + \foot{Anthropic Opus/Sonnet/Haiku, Google Gemini 2.5 Pro, Z.AI GLM-5.2.} |
| 105 | +\end{frame} |
| 106 | + |
| 107 | +\begin{frame}{Result: every model wins} |
| 108 | + \centering |
| 109 | + \begin{tikzpicture} |
| 110 | + \begin{axis}[ybar,width=11cm,height=6.2cm,bar width=9pt,ymin=0,ymax=1,ylabel={Mean F1}, |
| 111 | + symbolic x coords={Opus 4.8,Sonnet 4.6,Haiku 4.5,Gemini 2.5 Pro,GLM-5.2},xtick=data,x tick label style={rotate=20,anchor=east,font=\footnotesize}, |
| 112 | + enlarge x limits=0.12,legend style={at={(0.5,-0.28)},anchor=north,legend columns=2,draw=gray!40}, |
| 113 | + nodes near coords,nodes near coords style={font=\tiny}] |
| 114 | + \addplot[A] coordinates {(Opus 4.8,0.805) (Sonnet 4.6,0.845) (Haiku 4.5,0.817) (Gemini 2.5 Pro,0.778) (GLM-5.2,0.817)}; |
| 115 | + \addplot[C] coordinates {(Opus 4.8,0.385) (Sonnet 4.6,0.354) (Haiku 4.5,0.273) (Gemini 2.5 Pro,0.473) (GLM-5.2,0.362)}; |
| 116 | + \legend{Ontology-augmented,Control (parametric only)} |
| 117 | + \end{axis}\end{tikzpicture} |
| 118 | + \foot{Universal lift: +0.31 to +0.54 F1. The smallest model (Haiku) gains the most.} |
| 119 | +\end{frame} |
| 120 | + |
| 121 | +\begin{frame}{Result: hallucination roughly halved} |
| 122 | + \centering |
| 123 | + \begin{tikzpicture} |
| 124 | + \begin{axis}[ybar,width=11cm,height=6.2cm,bar width=9pt,ymin=0,ymax=1,ylabel={Hallucination rate}, |
| 125 | + symbolic x coords={Opus 4.8,Sonnet 4.6,Haiku 4.5,Gemini 2.5 Pro,GLM-5.2},xtick=data,x tick label style={rotate=20,anchor=east,font=\footnotesize}, |
| 126 | + enlarge x limits=0.12,legend style={at={(0.5,-0.28)},anchor=north,legend columns=2,draw=gray!40}, |
| 127 | + nodes near coords,nodes near coords style={font=\tiny}] |
| 128 | + \addplot[A] coordinates {(Opus 4.8,0.151) (Sonnet 4.6,0.12) (Haiku 4.5,0.073) (Gemini 2.5 Pro,0.125) (GLM-5.2,0.135)}; |
| 129 | + \addplot[C] coordinates {(Opus 4.8,0.573) (Sonnet 4.6,0.594) (Haiku 4.5,0.758) (Gemini 2.5 Pro,0.552) (GLM-5.2,0.64)}; |
| 130 | + \legend{Ontology-augmented,Control} |
| 131 | + \end{axis}\end{tikzpicture} |
| 132 | + \foot{Grounding replaces plausible-but-wrong guesses with the graph's actual vocabulary.} |
| 133 | +\end{frame} |
| 134 | + |
| 135 | +\begin{frame}{Where grounding helps most} |
| 136 | + \centering |
| 137 | + \begin{tikzpicture} |
| 138 | + \begin{axis}[ybar,width=10cm,height=5.8cm,bar width=16pt,ymin=0,ymax=1,ylabel={Mean F1 (all models)}, |
| 139 | + symbolic x coords={neighbour,subclass,existence},xtick=data,enlarge x limits=0.3, |
| 140 | + legend style={at={(0.5,-0.22)},anchor=north,legend columns=2,draw=gray!40}, |
| 141 | + nodes near coords,nodes near coords style={font=\footnotesize}] |
| 142 | + \addplot[A] coordinates {(neighbour,0.917) (subclass,0.55) (existence,0.865)}; |
| 143 | + \addplot[C] coordinates {(neighbour,0.458) (subclass,0.048) (existence,0.513)}; |
| 144 | + \legend{Augmented,Control} |
| 145 | + \end{axis}\end{tikzpicture} |
| 146 | + \foot{Biggest gains on proprietary structure (subclasses: +0.50) and niche concepts the base model can't know.} |
| 147 | +\end{frame} |
| 148 | + |
| 149 | +\begin{frame}{What it costs} |
| 150 | + \begin{itemize} |
| 151 | + \item Grounding adds context tokens and one retrieval round-trip --- \textbf{optional and per-call}. |
| 152 | + \item Gemini 2.5 Pro: ~4198 prompt tokens/query; GLM-5.2: ~3620 --- modest for the accuracy gained. |
| 153 | + \item \textbf{Budget-bounded \& fail-open:} if the graph is unreachable, the turn proceeds ungrounded --- never blocked. |
| 154 | + \end{itemize} |
| 155 | + \vskip6pt |
| 156 | + {\color{teal0}\textbf{Net:}} a bounded, switchable cost for a large, universal accuracy gain. |
| 157 | + \foot{The binding augments thinking without overpowering the context window.} |
| 158 | +\end{frame} |
| 159 | + |
| 160 | +\begin{frame}{The design works} |
| 161 | + \begin{center} |
| 162 | + {\Large A formal ontology, bound pervasively to AI,\\ makes \textbf{every} model more accurate and less hallucinatory.} |
| 163 | + \vskip10pt |
| 164 | + \begin{itemize} |
| 165 | + \item \textbf{+0.44 mean F1} across 5 LLMs; hallucination roughly halved. |
| 166 | + \item Read-pervasive, write-governed, budget-bounded, fail-open --- production-shaped. |
| 167 | + \item One shared brain across tool, consultant and CLI surfaces. |
| 168 | + \end{itemize} |
| 169 | + \vskip12pt |
| 170 | + {\color{burnt}\Large\textbf{VisionFlow}}\;\textbullet\;{\large VisionClaw \textbullet\ Agentbox}\\[4pt] |
| 171 | + {\color{burnt}\large\url{http://www.visionflow.info}} |
| 172 | + \end{center} |
| 173 | +\end{frame} |
| 174 | + |
| 175 | +\end{document} |
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