From 10 Terabytes to Zero Parameter: The LLM 2.0 Revolution��Vincent Granville, PhD�Chief AI Architect �BondingAI.io�vincent@BondingAI.io� �February 16th, 2026

No-GPU Full Context Secure Multi-LLM

with Real-Time Fine-Tuning & Explainable AI�

MLtechniques.com – xLLM, by Vincent Granville

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Agenda�

MLtechniques.com – xLLM, by Vincent Granville

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1. Why xLLM? What is it?
2. xLLM Innovative Features
3. xLLM Architecture and Evaluation
4. Agents & Case Studies

- Text Clustering, Categorization

- Data Synthesis (best synthetic data + eval)

- Predictive Analytics

- Anomaly Detection

- Medical Data (ECG compression, patterns)

• No-Blackbox DNNs & Alternatives
• References

Part 1�Why xLLM? �What is it?��

MLtechniques.com – xLLM, by Vincent Granville

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Extreme LLM (xLLM) in a Nutshell

• Mixture of experts
• Specialized sub-LLM and/or sub-LLMs for authorized users
• LLM router to manage the sub-LLMs
• User selects sun-LLM, agents, and hyperparameters
• Each sub-LLM built with its own taxonomy and contextual environment

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• No neural network, no training
• Thus, low cost, easy to fine-tune in real-time, in-memory LLM, on-premises
• Self-tuned based on favorite hyperparameters, intuitive parameters
• No GPU, no latency, exhaustive concise results, local implementation

• Concise results
• Multiple sections: links, related content, x-embeddings based on E-PMI metric
• Output with relevancy score attached to each item in each section; User offered choices for deeper or alternate queries
• Great for professional users. Not just a “prompt box”; many options in the UI, like a mini-browser
• Case studies
• Corporate datal lake corpus (AB-Inbev)
• Nvidia PDF repository
• Wolfram corpus: 15k webpages, 5k categories

MLtechniques.com - xLLM, by Vincent Granville

xLLM for Enterprises to build own LLMs faster, at lower cost, with increased accuracy, security, explainable AI

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Prompt Results – Card Format (web API)

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Prompt Results – Card Format (web API)

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Prompt Results – Listing Format (1)

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Prompt Results – Listing Format (2)

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Prompt Results – Structured Text Format

• Text entities retrieved from corpus via contextual chunking / indexation
• Blended with images, datasets, exact URLs/references and so on (multimodal)
• Featuring categories, tags, related content, titles, timestamps, links, and so on
• Structured output based on hierarchical chunking and multi-indexing, augmented with auto-tagging, acronyms / synonyms dictionary, and un-stemming
• Multiple relevancy scores based on multiple types of multi-tokens, then normalized
• Exact vs broad search, negative keywords, variable weights attached to prompt multi-tokens, search by recency.
• Auto-correct, stemmer, stopwords specific to corpus; “real estate” or “San Francisco” are single tokens. Chunks visibility (for any given chunk) depends on user privileges.

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Prompt Results – Response Generation

• Based on structured output (see previous slides)
• Generic template prompts with premade auto-filled response matched against user prompts
• Light, fast proprietary DNN, no TensorFlow, Python or Keras, explainable AI.

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• Proprietary non-Blackbox DNN with explainable AI (patent-pending)
• With equalizer, stabilizer and other proprietary features to accelerate convergence and increase stability
• Chaotic gradient descent with temperature decay
• Sub-epochs, sub-layers, original universal function
• Global optimization or one sub-layer at a time within an epoch
• Pre-tabulated functions, generic partial derivatives

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Part 2 �xLLM Innovative Features��

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Backend Features

• Smart crawling to retrieve embedded structure
• Breadcrumbs (enterprise corpus), concept associations (related links)
• Metadata, tags, taxonomy, long contextual environment
• PDF parser (TOC, index, glossaries, synonyms, titles, tables, images)
• X-embeddings
• Variable-length embeddings stored as sparse nested hashes
• Multi-token: “data~science” on top of single tokens “data” and “science”
• Contextual token: “data^science”, both words in same paragraph but not adjacent
• PMI (pointwise mutual information) instead of dot product / cosine distance
• Parametric weights attached to tokens (no loss function to optimize)

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Retrieved Taxonomy: Wolfram Example

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Retrieved Context: Enterprise Example

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Backend Features (Cont.)

• Home-made libraries
• Issues with Python libraries (singularize, autocorrect, “Feller” changed to “seller”)
• Minimize stemming and text transforms; keep plural if found in corpus
• Important: accented characters, separators (punctuation), capital letters
• Ad-hoc lists: home-made stopwords, do-not-singularize, do-not-autocorrect
• Backend tables (specific to each sub-LLM)
• X-embeddings not the most important table; taxonomy more important
• Compression mechanism: sorted n-grams
• Backend parameters

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Backend Features (Cont.)

• Chunking & Indexing
• Chunks called text entities: webpage, subsection (PDF), or JSON entity
• Indexed for fast retrieval of full content, and for easy content linking
• Chunks of variable length, hierarchical chunking and multi-index, content de-duping
• Auto-tagging. Use relative font size and other elements to generate contextual fields. In short: font intelligence module for contextualization.
• NLP
• Python with workarounds + homemade
• Weighted graph tokens: multi-tokens found in the context/taxonomy elements
• Customized pointwise mutual information (PMI), instead of cosine similarity

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Frontend Features

• User Interface
• Many options, not just a prompt box. Negative search, search by tag/category/recency, exact/broad search
• Chat mode or structured output; suggested prompts (no prompt engineering)
• User can choose agents, sub-LLM, or fine-tuning in real time
• End-user debugging with catch-all parameter set
• Relevancy & trustworthiness multi-scores to rank response chunks
• Goal: too many results to show to user prompt, which ones to display?
• Graph tokens and multi-tokens with 2+ words: boost score
• Text entity with 2+ multi-token intersection with prompt, get higher score
• Rare multi-tokens get extra boost
• Longer text entities get extra boost

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MLtechniques.com - xLLM, by Vincent Granville

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Relevancy scores

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Frontend Features (Cont.)

• Distillation
• If multi-tokens A~B~C and A~B have same count, show results from A~B~C, not A~B
• Acronyms and synonyms
• If A and B are synonyms, A in prompt but not in corpus, and B in corpus, map A to B in the prompt to retrieve B in the corpus (Goal: trying to be exhaustive)
• Self-tuning – Most popular front-end parameters used to build default parameters
• Prompt cleanup with stopwords list / stemmer different from backend list
• Stemming and un-stemming, auto-correct
• Caching

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MLtechniques.com - xLLM, by Vincent Granville

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Distillation

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Proprietary JSON-based PDF Parser

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Part 3�xLLM Architecture and Evaluation��

MLtechniques.com – xLLM, by Vincent Granville

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General Overview

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Backend: Overview

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Frontend: Overview

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Path from Prompt to Results

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Path from Crawl to Backend Tables

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Details: Indexation

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Detail: Relevancy Algorithm

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Detail: Sorted N-Grams

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Database: Nested Hashes (like JSON)

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Evaluation

• User-based (automated)
• Collect favorite hyperparameters chosen by users
• Use smart grid search to set default hyperparameters based on user favorites
• Fine-tune on one or few sub-LLMs (like LoRA) before full optimization on (say) 200 sub-LLMs. You may fine-tune all sub-LLMs in parallel.
• Taxonomy-based (automated)
• Pretend that the taxonomy backend table comes from external sources
• Assign categories to webpages based on this “external” taxonomy
• For each webpage, compare externally assigned to native category

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MLtechniques.com - xLLM, by Vincent Granville

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Evaluation (Cont.)

• Evaluation challenges
• We are dealing with unsupervised learning: there is no perfect output except for trivial cases
• Quality depends on user (professional users and laymen have different criteria)
• How do you measure exhaustivity, depth, and recency?
• Output value versus grammatical capabilities
• How do you integrate xLLM relevancy scores attached to each item, to evaluate output quality? No other LLM return these scores

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Taxonomy-Based Evaluation

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Part 4 �xLLM for Clustering, Data Synthetization, Predictive Analytics,�Anomaly Detection,�Medical Data��

MLtechniques.com – xLLM, by Vincent Granville

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Interlude – Adaptive Loss Function (ALF)

• Adaptive loss function converging to model evaluation metric
• Boosts quality measured using model evaluation, reduces gradient descent failures

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xLLM for Data Synthesis (NoGAN with ALF)

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NoGAN Tabular Data Synthetization

• Real data: 2 concentric circles
• Synthesized, NoGAN synthesizer: blue dots. Constrained synthetization to keep loss above some threshold
• As the loss function gets more granular, the synthesized data gets more similar to the real data (the training set)
• Best evaluation metric: full joint Kolmogorov-Smirnov distance in any dimension (dimension-adjusted)

NoGAN Base Version in a Nutshell

• Train the “model” with training data:
• Bin each feature separately based on empirical quantiles evenly spaced out
• Create static multivariate bins (hyperrectangles), based on above slices
• Let p_k be the proportion of training set observations in hyperrectangle B_k
• Generate N synthetic observations:
• For each k, generate a count n_k attached to hyperrectangle B_k

(n₁, n₂, n₃, …) ~ Multinomial(N; p₁, p₂, p₃, …)

• For each k, generate n_k vectors uniformly distributed on B_k

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The synthetic data is the collection of the above vectors, with n₁+ n₂ + n₃ + … = N.

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MLtechniques.com - NoGAN Synthesizer, by Vincent Granville

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Why is NoGAN so fast?

• The training set data is visited only once, sequentially, as follows.
• For each observation in the training set:
• Create a new static hyperrectangle if the current observation is not in any hyperrectangle built so far, and set the number of observations to 1 for that hyperrectangle
• Otherwise increase by 1 the observation count for the corresponding hyperrectangle
• The hyperrectangle structure is a key-value pair table
• The key is the coordinate vector of the hyperrectangle in the feature space
• The value is the number of training set observations in that hyperrectangle
• Categorical features are efficiently handled with smart encoding

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MLtechniques.com - NoGAN Synthesizer, by Vincent Granville

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GAN: Vendor Comparison

• Circle dataset: all vendors rate their synthetization as excellent

MLtechniques.com - NoGAN Synthesizer, by Vincent Granville

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xLLM for Predictions

• Case study – media industry
• Predicting article performance (pageviews) based on title keywords and category
• 4000 articles; pageview is normalized and time-adjusted
• Evaluation and Loss function (identical)
• Based on comparing predicted with observed quantiles, using 5 quantiles (see code)
• Good proxy to Kolmogorov-Smirnov distance

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MLtechniques.com - xLLM, by Vincent Granville

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xLLM for Predictions – Model

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xLLM for Predictions – Category Encoding

• Create new codes sequentially as you browse the training set.
• Aggregate codes with few observations into bundles.
• Create two key-value mappings. Ex:
• Category_to_Code[‘Blog’, ‘William’] = 5
• Code_to_category[5] = [‘Blog’, ‘William’]
• Replace the categorical features by the newly created feature, “Code”.
• Number of codes ≤ number of obs.

MLtechniques.com - NoGAN Synthesizer, by Vincent Granville

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xLLM for Predictions – Results

• Observed vs predicted normalized pageview count

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xLLM for Clustering

• Case study – media industry
• Identifying patterns / clusters in popular articles based on title keywords
• 4000 articles; pageview is normalized and time-adjusted
• Methodology
• Group multi-tokens into clusters based on a similarity metric, with hierarchical clustering and k-medoids
• Let S(t) be the set of articles containing the multi-token t in the title
• For each multi-token group G, the list L(G) of articles belonging to G is

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MLtechniques.com - xLLM, by Vincent Granville

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xLLM for Clustering (Cont.)

• Similarity between two multi-tokens t₁, t₂

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• Remarks
• Multi-token clusters are non-overlapping, but article clusters may overlap
• Sklearn clustering methods require a distance matrix as input; the matrix (derived from the similarity metric) is huge but extremely sparse.
• In my implementation, s(t₁, t₂) is computed and stored only if it is strictly positive. Using connected components for clustering, it is far more efficient than Sklearn.

MLtechniques.com - xLLM, by Vincent Granville

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xLLM for Clustering – Sample Structure

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xLLM for Clustering – Sample Cluster

• Cluster of popular articles linked to multi-token cluster with 3 elements, including one contextual multi-token: “Machine^vs” (pv stands for normalized pageview)

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MLtechniques.com - xLLM, by Vincent Granville

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Interlude – Fast Nearest Neighbor Search

• Red dot: prompt-derived embeddings
• Blue dot: backend table embedding
• Over time, arrows link red dots to their nearest blue dots
• Alternative to vector search

MLtechniques.com - xLLM, by Vincent Granville

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xLLM for Anomaly Detection (Cybersecurity)

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• Automatically Excel doc processing
• Multiple tabs per spreadsheet
• Automated SQL queries joining fields across multiple tabs
• Time series and geospatial output
• Automated flagging
• Animated US maps

- colored zip codes to show risk level: green, red, orange

- one video frame per time unit

- dot size represents fraud intensity (volume)

• Data source: click logs / web logs

xLLM for Anomaly Detection – Animated Map�

MLtechniques.com - xLLM, by Vincent Granville

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xLLM for Anomaly Detection – Gif version �

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xLLM for Anomaly Detection – Hourly Data�

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xLLM for Medical Data – Compression

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xLLM for Medical Data – Sample ECG

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• Source: PhysioNet.org

xLLM for Medical Data – Pattern Analysis

MLtechniques.com - xLLM, by Vincent Granville

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Part 5 �No-Blackbox DNNs�and Alternatives��

MLtechniques.com – xLLM, by Vincent Granville

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Proprietary DNN (Part 1)

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Proprietary DNN (Part 2)

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Proprietary DNN (GOF on Synth. Data)

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DNN Weight Ghosting and Watermarking (1)

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DNN Weight Ghosting and Watermarking (2)

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DNN – Pred. vs True Weights, Synth. Data

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Alternative to DNN – Rejected Model

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Alternative to DNN – Final Model

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• Input data with 10,000 dimensions
• Origins: exact interpolation
• Works if number of dimensions > number of observations
• Works with > 25% noise in data
• Works with 60% distillation rate
• Bening overfitting
• Works with numerical, categorical and text data

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• 100% correct prediction on “training set”
• No gradient descent, no loss function
• Simple global optimum obtained with closed-form expression
• 96% correct prediction for next token on NVIDIA corpus
• Attention mechanism built in

Alternative to DNN – Nvidia Case Study

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Alternative to DNN – Nvidia Case Study

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Next “Token” Prediction – DNA Sequencing

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• Next token prediction: the mother of all LLMs
• Here: predict next DNA sub-sequence to generate synthetic genomic data
• Alphabet has 4 letters
• Left: Scatterplot comparing observed vs synthetic ECDFs
• Blue = predictive model; Orange = baseline

Part 6 �References��

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References

• 30 Articles Shaping the Future of Enterprise AI in 2026 – NextGen AI Technology

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• New Book: No-Blackbox, Secure, Efficient AI and LLM Solutions – NextGen AI Technology

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