Note: 10

From PDFs to Navigable Knowledge

Code: 120 ()

from functools import partial
from re import findall, MULTILINE
from toolslm.md_hier import create_heading_dict
from functools import reduce
from operator import getitem
from fastcore.all import *

from mistocr.core import ocr_pdf, read_pgs
from mistocr.refine import fix_hdgs, add_img_descs
from mistocr.pipeline import pdf_to_md

Note: 453

This dialog shows how proper PDF-to-markdown conversion enables better downstream LLM tasks. We'll use Yann LeCun's 62-page paper "A Path Towards Autonomous Machine Intelligence" as our example.

The Problem: Long technical documents are difficult to navigate efficiently. Providing the entire document to an LLM bloats the context window and increases costs. From an information retrieval perspective, this approach yields high recall but very low precision—you get everything, but finding what you need is difficult. Traditional RAG systems break documents into chunks, embed them, and retrieve pieces by similarity. While effective for finding semantically related content, this approach treats the document as a collection of disconnected fragments rather than a coherent, intentionally structured whole.

The Approach: By converting PDFs to clean markdown with proper heading hierarchy and image descriptions (using mistocr - a Mistral OCR wrapper + additional goodies), we can build navigation tools that leverage the document's structure using 'toolslm'. This enables:

Hierarchical browsing based on queries
Section-aware information retrieval
Efficient exploration of large documents through LLM tool calling

We'll demonstrate how proper document structure transforms a 60+ page PDF into a navigable knowledge base, showing why clean markdown with hierarchy matters for downstream LLM applications.

Note: 15

1. Converting PDF to clean Markdown

Note: 39

1.1 Converting the pdf with `mistocr`

Code: 15 ()

fname = "jepa-seminal-paper.pdf"

Code: 34 ()

from mistocr.core import ocr_pdf

dst_dir = ocr_pdf(fname, dst='md')[0]

Output: 49

Mistral batch job status: QUEUED
Mistral batch job status: RUNNING

Code: 12 ()

!ls -R {dst_dir}

Output: 625

md/jepa-seminal-paper:
img	    page_19.md	page_29.md  page_39.md	page_49.md  page_59.md
page_1.md   page_2.md	page_3.md   page_4.md	page_5.md   page_6.md
page_10.md  page_20.md	page_30.md  page_40.md	page_50.md  page_60.md
page_11.md  page_21.md	page_31.md  page_41.md	page_51.md  page_61.md
page_12.md  page_22.md	page_32.md  page_42.md	page_52.md  page_62.md
page_13.md  page_23.md	page_33.md  page_43.md	page_53.md  page_7.md
page_14.md  page_24.md	page_34.md  page_44.md	page_54.md  page_8.md
page_15.md  page_25.md	page_35.md  page_45.md	page_55.md  page_9.md
page_16.md  page_26.md	page_36.md  page_46.md	page_56.md
page_17.md  page_27.md	page_37.md  page_47.md	page_57.md
page_18.md  page_28.md	page_38.md  page_48.md	page_58.md

md/jepa-seminal-paper/img:
img-0.jpeg   img-12.jpeg  img-16.jpeg  img-2.jpeg  img-6.jpeg
img-1.jpeg   img-13.jpeg  img-17.jpeg  img-3.jpeg  img-7.jpeg
img-10.jpeg  img-14.jpeg  img-18.jpeg  img-4.jpeg  img-8.jpeg
img-11.jpeg  img-15.jpeg  img-19.jpeg  img-5.jpeg  img-9.jpeg

Note: 57

We get one markdown per page with images/figures referenced on those markdowns downloaded. We can grap all pages using mitocr's read_pgs utils function:

Code: 33 ()

from mistocr.core import read_pgs

md = read_pgs(dst_dir)
print(md[:500])

Output: 195

# A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27 

Yann LeCun<br>Courant Institute of Mathematical Sciences, New York University yann@cs.nyu.edu Meta - Fundamental AI Research yann@fb.com

June 27, 2022


#### Abstract

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and pla

Note: 25

Let's get a quick look at the doc headings as interpreted by Mistral OCR:

Code: 37 ()

get_hdgs = lambda o: findall(r'^#{1,6} .+$', o, MULTILINE)

Code: 7 ()

get_hdgs(md)

Output: 1,480

['# A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27 ',
 '#### Abstract',
 '## 1 Prologue',
 '# 2 Introduction ',
 '# 2.1 Learning World Models ',
 '### 2.2 Humans and Animals learn Hierarchies of Models',
 '# 3 A Model Architecture for Autonomous Intelligence ',
 '# 3.1 Typical Perception-Action Loops ',
 '### 3.1.1 Mode-1: Reactive behavior',
 '# 3.1.2 Mode-2: reasoning and planning using the world model ',
 '# 3.1.3 From Mode-2 to Mode-1: Learning New Skills ',
 '### 3.1.4 Reasoning as Energy Minimization',
 '# 3.2 The Cost Module as the Driver of Behavior ',
 '# 3.3 Training the Critic ',
 '# 4 Designing and Training the World Model ',
 '# 4.1 Self-Supervised Learning ',
 '# 4.2 Handling Uncertainty with Latent Variables ',
 '# 4.3 Training Energy-Based Models ',
 '# 4.4 Joint Embedding Predictive Architecture (JEPA) ',
 '# 4.5 Training a JEPA ',
 '# 4.5.1 VICReg ',
 '# 4.5.2 Biasing a JEPA towards learning "useful" representations ',
 '### 4.6 Hierarchical JEPA (H-JEPA)',
 '# 4.7 Hierarchical Planning ',
 '# 4.8 Handling uncertainty ',
 '# 4.8.1 World Model Architecture ',
 '# 4.9 Keeping track of the state of the world ',
 '# 4.10 Data Streams ',
 '# 5 Designing and Training the Actor ',
 '# 6 Designing the Configurator ',
 '# 7 Related Work ',
 '### 7.1 Trained World Models, Model-Predictive Control, Hierarchical PLanning',
 '# 7.2 Energy-Based Models and Joint-Embedding Architectures ',
 '# 7.3 Human and animal cognition ',
 '# 8 Discussion, Limitations, Broader Relevance ',
 '### 8.1 What is missing from the Proposed Model?',
 '# 8.2 Broader Relevance of the Proposed Approach ',
 '### 8.2.1 Could this Architecture be the Basis of a Model of Animal Intelligence?',
 '# 8.2.2 Could this be a Path towards Machine Common Sense? ',
 '### 8.3 Is it all about scaling? Is reward really enough?',
 '# 8.3.1 Scaling is not enough ',
 '### 8.3.2 Reward is not enough',
 '# 8.3.3 Do We Need Symbols for Reasoning? ',
 '## Acknowledgments',
 '# References ',
 '# Appendix: Symbols and Notations ',
 '# Appendix: Amortized Inference for Latent Variables ',
 '# Appendix: Loss functions for Contrastive Training of EBM ']

Note: 270

Looking at the headings, we can spot several inconsistencies:

Inconsistent heading levels - Some major sections use # (h1) while others that seem equally important use ### (h3) or #### (h4). For example, "Abstract" is h4, but "Prologue" is h2.
Mixed hierarchy logic - Section 2.1 "Learning World Models" uses # (h1), but it should be a subsection (h2 or h3) since it's numbered as 2.1.
Spacing inconsistencies - Some headings have trailing spaces (like "# 2 Introduction ") while others don't.
Level jumps - The document jumps from h1 to h4 ("Abstract") without going through h2 or h3 first, which breaks proper heading hierarchy.

Note: 16

1.2 Fixing the Markdown Headings

Note: 30

Let's try to fix these headings using mistocr's fix_hdgs function:

Code: 52 ()

from mistocr.refine import fix_hdgs

dst_dir_fixed = 'md_fixed/jepa-seminal-paper'
fix_hdgs(dest_dir, dst=dst_dir_fixed)

Code: 21 ()

md = read_pgs(dst_dir_fixed)
print(md[:500])

Output: 201

# A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27  ... page 1

Yann LeCun<br>Courant Institute of Mathematical Sciences, New York University yann@cs.nyu.edu Meta - Fundamental AI Research yann@fb.com

June 27, 2022


## Abstract ... page 1

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reas

Code: 7 ()

get_hdgs(md)

Output: 1,822

['# A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27  ... page 1',
 '## Abstract ... page 1',
 '## 1 Prologue ... page 1',
 '## 2 Introduction  ... page 2',
 '### 2.1 Learning World Models  ... page 3',
 '### 2.2 Humans and Animals learn Hierarchies of Models ... page 3',
 '## 3 A Model Architecture for Autonomous Intelligence  ... page 7',
 '### 3.1 Typical Perception-Action Loops  ... page 9',
 '#### 3.1.1 Mode-1: Reactive behavior ... page 9',
 '#### 3.1.2 Mode-2: reasoning and planning using the world model  ... page 10',
 '#### 3.1.3 From Mode-2 to Mode-1: Learning New Skills  ... page 12',
 '#### 3.1.4 Reasoning as Energy Minimization ... page 12',
 '### 3.2 The Cost Module as the Driver of Behavior  ... page 13',
 '### 3.3 Training the Critic  ... page 15',
 '## 4 Designing and Training the World Model  ... page 16',
 '### 4.1 Self-Supervised Learning  ... page 17',
 '### 4.2 Handling Uncertainty with Latent Variables  ... page 19',
 '### 4.3 Training Energy-Based Models  ... page 20',
 '### 4.4 Joint Embedding Predictive Architecture (JEPA)  ... page 24',
 '### 4.5 Training a JEPA  ... page 25',
 '#### 4.5.1 VICReg  ... page 28',
 '#### 4.5.2 Biasing a JEPA towards learning "useful" representations  ... page 29',
 '### 4.6 Hierarchical JEPA (H-JEPA) ... page 29',
 '### 4.7 Hierarchical Planning  ... page 31',
 '### 4.8 Handling uncertainty  ... page 32',
 '#### 4.8.1 World Model Architecture  ... page 34',
 '### 4.9 Keeping track of the state of the world  ... page 35',
 '### 4.10 Data Streams  ... page 36',
 '## 5 Designing and Training the Actor  ... page 37',
 '## 6 Designing the Configurator  ... page 38',
 '## 7 Related Work  ... page 39',
 '### 7.1 Trained World Models, Model-Predictive Control, Hierarchical PLanning ... page 39',
 '### 7.2 Energy-Based Models and Joint-Embedding Architectures  ... page 41',
 '### 7.3 Human and animal cognition  ... page 42',
 '## 8 Discussion, Limitations, Broader Relevance  ... page 43',
 '### 8.1 What is missing from the Proposed Model? ... page 43',
 '### 8.2 Broader Relevance of the Proposed Approach  ... page 44',
 '#### 8.2.1 Could this Architecture be the Basis of a Model of Animal Intelligence? ... page 44',
 '#### 8.2.2 Could this be a Path towards Machine Common Sense?  ... page 45',
 '### 8.3 Is it all about scaling? Is reward really enough? ... page 45',
 '#### 8.3.1 Scaling is not enough  ... page 46',
 '#### 8.3.2 Reward is not enough ... page 46',
 '#### 8.3.3 Do We Need Symbols for Reasoning?  ... page 47',
 '## Acknowledgments ... page 47',
 '## References  ... page 49',
 '## Appendix: Symbols and Notations  ... page 57',
 '## Appendix: Amortized Inference for Latent Variables  ... page 59',
 '## Appendix: Loss functions for Contrastive Training of EBM  ... page 61']

Note: 138

Much cleaner! The heading hierarchy is now consistent and correct:

major sections use ## (h2),
subsections use ### (h3),
and sub-subsections use #### (h4).

Notice that page numbers are now appended to each heading. This is useful for downstream LLM tasks—for example, the model can estimate the token count for each section based on how many pages it spans.

Note: 12

1.3 Adding images description

Note: 135

The PDF contains 10 images (figures and diagrams) that are referenced throughout the paper. These visual elements convey important information that would be lost if we only processed the text. The add_img_descs function from mistocr's refine module addresses this by: (i) generating descriptions of each image using vision-language models, and (ii) embedding these descriptions directly into the markdown where the images are referenced.

Code: 58 ()

from mistocr.refine import add_img_descs

dst_dir_final = 'md_final/jepa-seminal-paper'
await add_img_descs(dst_dir_fixed, dst=dst_dir_final)

Output: 82

Describing 20 images...
Saved descriptions to md_fixed/jepa-seminal-paper/img_descriptions.json
Adding descriptions to 62 pages...
Done! Enriched pages saved to md_final/jepa-seminal-paper

Note: 45

See for instance the description of "Figure 1" on page 4 of the pdf version of the paper, described in the final markdown version:

Code: 27 ()

md = read_pgs(dst_dir_final)
print(md[8000:10000])

Output: 790

se trainable world models that can deal with complex uncertainty in the predictions.

### 2.2 Humans and Animals learn Hierarchies of Models ... page 3

Humans and non-human animals learn basic knowledge about how the world works in the first days, weeks, and months of life. Although enormous quantities of such knowledge are acquired quite quickly, the knowledge seems so basic that we take it for granted. In the first few months of life, we learn that the world is three-dimensional. We learn that every

![img-0.jpeg](img-0.jpeg)
AI-generated image description:
___
This is a developmental timeline chart showing the emergence of various cognitive abilities in infants from 0-14 months of age. The chart is organized into five horizontal categories:

1. **Social Communication** (top): Shows progression from "helping vs hindering" (around 6 months), to "pointing" (around 9 months), to "false perceptual beliefs" (around 13 months).

2. **Perception - Actions**: Displays "face tracking" (starting at 0 months), "biological motion" (around 3-5 months), and "rational, goal-directed actions" (around 12 months).

3. **Perception - Physics**: Includes "stability, support" (around 6 months), "gravity, inertia conservation of momentum" (around 9 months), and related concepts.

4. **Perception - Objects**: Shows "Object permanence" (around 2-6 months), "solidity, rigidity" (around 4-6 months), "natural kind categories" (around 5-7 months), and "shape constancy" (around 12-14 months).

5. **Production** (bottom): Displays "proto-imitation" and "emotional contagion" (both starting at 0 months), "crawling" (around 9 months), and "walking" (around 12-13 months).

Each ability is represented by a gray bar indicating the approximate age range when it develops. The chart provides a comprehensive overview of infant cognitive and motor development milestones across multiple domains.
___

Figure 1: This chart, (courtesy of Emmanuel Dupoux), indicates at what age infants generally acquire vari

Note: 15

1.4 The One Liner Version

Note: 120

For clarity, I've demonstrated the three-step pipeline separately: OCR → heading fixes → figure descriptions. However, these steps can actually be executed in a single command:

Copied!

from mistocr.pipeline import pdf_to_md

await pdf_to_md(fname, dst_dir_final)

This convenience function runs the entire pipeline automatically, producing clean, hierarchically-structured markdown with embedded image descriptions.

Note: 13

2. Structure-Aware Document Navigation

Note: 418

Reading Documents Like Humans Do

Humans read differently. When approaching a research paper, you scan the title, read the abstract, perhaps jump to the conclusion, then selectively drill into relevant sections. You navigate the document's structure to efficiently extract what you need. Crucially, you can answer questions like "What does the conclusion say about X?" or "Which section discusses Y?" because you understand where information sits in the document's hierarchy.

Hierarchical Navigation with Structured Markdown

With well-structured markdown—complete with a proper heading hierarchy—we can enable similar navigation patterns:

Start with high-level sections (title, abstract, conclusions)
Progressively drill down into relevant subsections
Maintain context about where information sits in the document's logical flow
Navigate by structure, not just semantic similarity

This approach complements traditional semantic search: use structure-aware navigation when you need specific sections or want to browse hierarchically, and use embedding-based retrieval when you need to find concepts scattered across the document.

Interpretability and Evaluation Benefits

This hierarchical approach also improves system transparency. Instead of reporting "the model retrieved chunks 47, 203, and 89," you can trace actions like "the system read the abstract, then navigated to Section 3.2 on Identity Mapping." This clarity benefits both domain experts evaluating the system and developers debugging or refining it.

Note: 16

2.1 Introducing `toolslm`

Note: 64

Answer.ai's toolslm package provides a nice utility function that given a markdown, return its headings in a hierarchical way in a nested dictionary:

Code: 37 ()

md = read_pgs('md_final/jepa-seminal-paper')
hdgs = create_heading_dict(md)
hdgs

Output: 1,833

{'A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27  ... page 1': {'Abstract ... page 1': {},
  '1 Prologue ... page 1': {},
  '2 Introduction  ... page 2': {'2.1 Learning World Models  ... page 3': {},
   '2.2 Humans and Animals learn Hierarchies of Models ... page 3': {}},
  '3 A Model Architecture for Autonomous Intelligence  ... page 7': {'3.1 Typical Perception-Action Loops  ... page 9': {'3.1.1 Mode-1: Reactive behavior ... page 9': {},
    '3.1.2 Mode-2: reasoning and planning using the world model  ... page 10': {},
    '3.1.3 From Mode-2 to Mode-1: Learning New Skills  ... page 12': {},
    '3.1.4 Reasoning as Energy Minimization ... page 12': {}},
   '3.2 The Cost Module as the Driver of Behavior  ... page 13': {},
   '3.3 Training the Critic  ... page 15': {}},
  '4 Designing and Training the World Model  ... page 16': {'4.1 Self-Supervised Learning  ... page 17': {},
   '4.2 Handling Uncertainty with Latent Variables  ... page 19': {},
   '4.3 Training Energy-Based Models  ... page 20': {},
   '4.4 Joint Embedding Predictive Architecture (JEPA)  ... page 24': {},
   '4.5 Training a JEPA  ... page 25': {'4.5.1 VICReg  ... page 28': {},
    '4.5.2 Biasing a JEPA towards learning "useful" representations  ... page 29': {}},
   '4.6 Hierarchical JEPA (H-JEPA) ... page 29': {},
   '4.7 Hierarchical Planning  ... page 31': {},
   '4.8 Handling uncertainty  ... page 32': {'4.8.1 World Model Architecture  ... page 34': {}},
   '4.9 Keeping track of the state of the world  ... page 35': {},
   '4.10 Data Streams  ... page 36': {}},
  '5 Designing and Training the Actor  ... page 37': {},
  '6 Designing the Configurator  ... page 38': {},
  '7 Related Work  ... page 39': {'7.1 Trained World Models, Model-Predictive Control, Hierarchical PLanning ... page 39': {},
   '7.2 Energy-Based Models and Joint-Embedding Architectures  ... page 41': {},
   '7.3 Human and animal cognition  ... page 42': {}},
  '8 Discussion, Limitations, Broader Relevance  ... page 43': {'8.1 What is missing from the Proposed Model? ... page 43': {},
   '8.2 Broader Relevance of the Proposed Approach  ... page 44': {'8.2.1 Could this Architecture be the Basis of a Model of Animal Intelligence? ... page 44': {},
    '8.2.2 Could this be a Path towards Machine Common Sense?  ... page 45': {}},
   '8.3 Is it all about scaling? Is reward really enough? ... page 45': {'8.3.1 Scaling is not enough  ... page 46': {},
    '8.3.2 Reward is not enough ... page 46': {},
    '8.3.3 Do We Need Symbols for Reasoning?  ... page 47': {}}},
  'Acknowledgments ... page 47': {},
  'References  ... page 49': {},
  'Appendix: Symbols and Notations  ... page 57': {},
  'Appendix: Amortized Inference for Latent Variables  ... page 59': {},
  'Appendix: Loss functions for Contrastive Training of EBM  ... page 61': {}}}

Note: 82

Then we can easily retrieve hierarchically any bit of content in the following way. For instance to retrieve the content of the '8.3.1 Scaling is not enough ... page 46' sub-sub-section, identify it's path in the headings tree:

Code: 126 ()

path_section = ['A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27  ... page 1', '8 Discussion, Limitations, Broader Relevance  ... page 43', '8.3 Is it all about scaling? Is reward really enough? ... page 45', '8.3.1 Scaling is not enough  ... page 46']

Note: 36

Then to access its content, simply "navigate" the nested hdgs dictionary/tree through the path of interest:

Code: 126 ()

print(hdgs['A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27  ... page 1']['8 Discussion, Limitations, Broader Relevance  ... page 43']['8.3 Is it all about scaling? Is reward really enough? ... page 45']['8.3.1 Scaling is not enough  ... page 46'].text)

Output: 718

#### 8.3.1 Scaling is not enough  ... page 46

Large Language Models (LLMs), and more generally, large-scale transformer architectures trained with a form of generative self-supervised learning, have been astonishingly successful at capturing knowledge present in text. This has led to a debate in the AI community as to whether human-level AI can be attained by scaling up these architectures. My position in this debate is that I do not believe that scaling is enough for two main reasons.

First, current models operate on "tokenized" data and are generative. Every input modality must be turned into a sequence (or a collection) of "tokens" encoded as vectors. While this works well for text, which is already a sequence of discrete tokens, it is less suitable for continuous, high dimensional signals such as video. The type of SSL training used for LLM-style models can be seen as a particular kind of latent-free generative model trained with a particular kind of contrastive method called denoising auto-encoder (Vincent et al., 2010), or in this case masked auto-encoder (Devlin et al., 2018). Hence, they are subject to the limitations of generative models, latent-variable free models, and contrastive methods. Generative models have difficulty representing complex uncertainties in continuous spaces. LLMs simplify the representation of uncertainty in the prediction by only dealing with discrete objects from a finite collection (e.g. words from a dictionary). Representing uncertainty about a word being predicted comes down to producing a vector whose components are scores or probabilities for each word (or discrete token) in the dictionary. But this approach doesn't work for high-dimensional continuous modalities, such as video. To represent such data, it is necessary to eliminate irrelevant information about the variable to be modeled through an encoder, as in the JEPA. Furthermore, the high-dimensionality of the signal precludes the representation of uncertainty through a normalized distribution.

Second, current models are only capable of very limited forms of reasoning. The absence of abstract latent variables in these models precludes the exploration of multiple interpretations of a percept and the search for optimal courses of action to achieve a goal. In fact, dynamically specifying a goal in such models is essentially impossible.

Note: 76

However, looks cumbersome to convert these ['heading 1', 'heading 1.1', ...] to ['heading 1', 'heading 1.1', ...]! We can actually automate this with the following function:

Code: 51 ()

def get_text(ks:list): 
    "Navigate through nested levels using the exact key strings"
    return L(ks).reduce(getitem, hdgs).text

Code: 12 ()

get_text(path_section)[:200]

Output: 97

'#### 8.3.1 Scaling is not enough  ... page 46\n\nLarge Language Models (LLMs), and more generally, large-scale transformer architectures trained with a form of generative self-supervised learning, have '

Note: 37

Now for the exciting part: can we turn this simple get_text function into a tool that Solveit can call?

Note: 31

2.2 Using Solveit's Variable and Tool Features

Note: 112

Now let's explore how Solveit's "sigil" can help us work with structured markdown documents:

Use $`variable_name` to share variable contents directly in prompts
Use &`function_name` to give the assistant access to function calls

This allows us to pass the hierarchical document structure to the assistant and leverage it for intelligent document navigation and querying.

Prompt: 208

I'm reading Yann LeCun's paper 'A Path Towards Autonomous Machine Intelligence' for the first time. I'd like to understand it step by step, starting with the big picture before diving into technical details. Can you help me first grasp the main paradigm shift or key idea that Yann is proposing? To access the content of this paper, you have access to its table of content $hdgs represented as a nested dictionary. Givent a particular section, sub-section, ... path such as $path_section for instance, you can pull its content using &get_text function. Do not pull too much content at once, hence staying focused, progressing iteratively and intentionally.

Assistant: 555

🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠🧠

Great approach! Let's start with the abstract to get the core idea. The abstract is typically where authors distill their main contribution.

Copied!

{
  "id": "toolu_01LHCMQ7ppoCSHTEzAmj22z6",
  "call": {
    "function": "get_text",
    "arguments": {
      "ks": "['A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27  ... page 1', 'Abstract .<TRUNCATED>"
    }
  },
  "result": "'## Abstract ... page 1\\n\\nHow could machines learn as efficiently as humans and animals? How could <TRUNCATED>"
}

Based on the abstract, here's the key paradigm shift LeCun is proposing:

Instead of the current dominant approach (think large language models trained on text), he's advocating for machines that learn more like humans and animals do - through building internal world models that let them:

Predict what will happen
Reason about different possibilities
Plan actions at multiple time scales

The core architecture involves three key concepts:

Configurable predictive world models - internal simulations of how the world works
Intrinsic motivation - learning driven by curiosity/internal goals, not just external rewards
Hierarchical joint embedding architectures - learning representations at multiple levels of abstraction

This is a departure from the "scale up transformers and feed them text" paradigm toward something more aligned with how biological intelligence develops.

Does this resonate? Would you like to explore what he means by "world models" next, or would you prefer to see his critique of current approaches first (which might be in the introduction)?

Prompt: 90

Yann's vision is clear to me, he has actually always pursuing it since its earliest work on CNN, ... And I think I can antipate what are the main critique about the current model. What intrigues me more is what he means by "world models", intrinsic learning-driven motivation.

Assistant: 673

Perfect! Let's explore what LeCun means by "world models" since that's really the foundation of his proposal. Let me pull the introduction section where he likely develops this concept:

Copied!

{
  "id": "toolu_012miRtLqwFJVDzCvmCFPXrN",
  "call": {
    "function": "get_text",
    "arguments": {
      "ks": "['A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27  ... page 1', '2 Introduc<TRUNCATED>"
    }
  },
  "result": "'## 2 Introduction  ... page 2\\n\\nAnimals and humans exhibit learning abilities and understandings o<TRUNCATED>"
}

Excellent! Now we're getting to the meat of it. Here's what LeCun means by world models:

World models are internal predictive models that tell an agent:

What is likely to happen
What is plausible
What is impossible

The key insight: humans learn to drive in ~20 hours because they already have rich world models (intuitive physics, object permanence, cause-and-effect) learned through observation in early life. Current ML systems need millions of trials because they lack this foundation.

The hierarchy aspect is fascinating (see Figure 1 in the text): infants build models layer by layer:

First: 3D space, object permanence (0-6 months)
Then: intuitive physics - gravity, stability, inertia (6-12 months)
Finally: cause-effect, social understanding (12+ months)

Each level builds on the previous one - you can't understand "things fall" until you understand "things exist as persistent objects."

Regarding intrinsic motivation: Notice in Figure 2 there's a "Cost" module with an "Intrinsic cost" component. This seems to be the drive mechanism. Should we look at section 3.2 "The Cost Module as the Driver of Behavior" to understand how intrinsic motivation works in his architecture?

Prompt: 69

Before diving in section 3.2, let's step back a bit. What is Yann's plan to transform his vision into reality? Is there mention of a research agenda for the coming decade? What are the key technical challenges?

Assistant: 709

Great question! Let's look at the discussion section where he likely addresses the research agenda and challenges. Let me check section 8:

Copied!

{
  "id": "toolu_01GRYSfXBdit7Ckg1JVfm1Aw",
  "call": {
    "function": "get_text",
    "arguments": {
      "ks": "['A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27  ... page 1', '8 Discussi<TRUNCATED>"
    }
  },
  "result": "'## 8 Discussion, Limitations, Broader Relevance  ... page 43\\n\\nConstructing the cognitive architec<TRUNCATED>"
}

Excellent! This gives us the full picture. Here's LeCun's honest assessment:

The Reality Check: "It will probably take many years to work them all out" - he's upfront that this is a multi-year research agenda, not a near-term solution.

Key Technical Challenges (in section 8.1):

Can Hierarchical JEPA actually be trained from video? This is the foundational question - can we learn abstract concept hierarchies just by watching?
How to regularize latent variables? Should they be discrete, low-dimensional, sparse, or stochastic? Still an open question.
Action inference problem: When action spaces are discrete or cost functions are non-smooth, gradient-based optimization breaks down. May need alternative search methods (MCTS, dynamic programming, etc.)
The Configurator module is "the most mysterious" - how does it identify subgoal sequences and configure the agent? Not specified.
Short-term memory architecture - how to represent beliefs about world state is "somewhat fuzzy"

His positioning on current debates (sections 8.3.x):

Scaling is NOT enough - LLMs can't handle continuous high-dimensional signals like video
Reward is NOT enough - model-free RL is too sample-inefficient

Does this clarify his research roadmap? Which challenge intrigues you most - the JEPA training problem, the configurator mystery, or something else?

Note: 7

And so on ...!

Note: 403

3. Conclusion

This dialog demonstrates how proper PDF-to-markdown conversion enables more structured document interaction with LLMs. By combining:

Clean, hierarchically-structured markdown (via mistocr) with embedded image descriptions
Structure-aware navigation tools (via toolslm)
Tool calling capabilities (via Solveit)

we can navigate documents hierarchically rather than dumping entire PDFs into context windows or relying solely on semantic chunk retrieval.

Practical benefits:

Efficiency: Navigate 60+ page papers without bloating context windows
Precision: Access specific sections by structure, not just semantic similarity
Interpretability: Track which sections were consulted (e.g., "read Abstract, then Section 4.5") rather than opaque "retrieved chunks 47, 203, 89"
Natural exploration: Start broad (abstract, conclusions), then drill down into relevant subsections

This approach is most useful for longer technical documents where structure matters—papers, reports, documentation. It complements traditional RAG: use hierarchical navigation when you know where to look, and semantic search when you need to find concepts scattered across the document.

The core idea: preserving document structure enables more intentional navigation patterns.

From PDFs to Navigable Knowledge

1. Converting PDF to clean Markdown

1.1 Converting the pdf with mistocr

1.2 Fixing the Markdown Headings

1.3 Adding images description

1.4 The One Liner Version

2. Structure-Aware Document Navigation

2.1 Introducing toolslm

2.2 Using Solveit's Variable and Tool Features

3. Conclusion

1.1 Converting the pdf with `mistocr`

2.1 Introducing `toolslm`