Large Concept Models:  A Paradigm Shift in AI Reasoning

Introduction

Large Concept Models (LCMs) mark a major shift in Natural Language Processing by focusing on structured reasoning and real understanding rather than just predicting words. Unlike Large Language Models (LLMs), which sometimes generate misleading or inconsistent information in tasks that require significant reasoning, LCMs rely on structured knowledge  (such as ontologies and causal graphs), imitating the behavior and thinking of expert analysts. This approach helps AI grasp relationships between concepts, explain its reasoning, and make more reliable decisions. LCMs open up new possibilities for accurate decision-making, scientific discovery, and industry applications by addressing the flaws of current AI.

This article, rooted in a real-world implementation of advanced customer support, aims to help technical leadership understand how LCMs can be deployed and integrated within enterprise environments.

Understanding LLMs vs. LCMs

LLMs are AI models trained on massive amounts of text data, enabling them to understand and generate human-like text. By learning the patterns and structures of the language, LLMs can use predictions based on this information to respond to questions, create content, or generate insights from provided data or content.

In contrast, Large Concept Models (LCMs) are trained to understand and reason through the structured concepts. LCMs capture the higher-level ideas and relationships rather than just predicting the next word. This enables LCMs to perform more accurate reasoning, complex decision-making, and structured problem-solving capabilities across a variety of tasks.

The Problem with Current Models

Over the past few years, Large Language Models (LLMs) have significantly transformed AI. Among other things, they enabled machines to generate human-like text and interactions. However, LLMs often lack true understanding; they “hallucinate”, i.e., give incorrect responses. This is because LLMs highly rely on statistical patterns and probability, which introduces unreliability, especially when the tasks require causality and context. For example, a medical AI might confidently describe the side effects of a drug that doesn’t exist, or a legal chatbot could misinterpret case law because it relies on statistical patterns rather than real reasoning.

Lack of Causal Understanding

One key limitation of current AI models is causal understanding. If you ask, “How will wheat prices change if rainfall decreases?” an LLM might reference the past trends but miss some region-specific critical factors like soil fertility, regional agricultural policies, or global supply chains. This happens because LLMs cannot grasp concepts and their relationships  –  they predict words based on patterns, not on structured reasoning.

Example: Customer Support Automation

Let’s take an example of Customer Support Automation in an enterprise, where this limitation of Causal Understanding becomes critical. Various customers send emails requesting support, encompassing different departments and types of requests. This includes multiple requests in the same email or follow-up requests. Due to their inherent limitations, LLM-based AI systems will not be able to classify all the emails accurately and correctly.

For instance, a customer email might say, “My monthly invoice is incorrect again despite raising the complaint for the same last month”. The LLM-based system might classify this as a generic billing issue and miss deeper causal signals, such as an unresolved bug in the backend system affecting the invoice generation for existing accounts. This may lead to opening a duplicate ticket or routing it incorrectly.

How LCMs Solve This

Large Concept Models go beyond the keywords. Since their reasoning is based on structured concepts, they understand that “repeated issue”, “past ticket raised”, and “invoice mismatch” are conceptually linked. The model then uses historical context to identify causal relationships between prior ticket status and the backend issue. As a result, the LCM classifies it as “Recurring Critical Issue” under “System Errors”, raising it as a technical issue. In addition to that, it will also raise another ticket as “Invoice Correction” under “Accounts Department” to address customer grievances and provide an immediate resolution. At the same time, the software team fixes the technical issue.

Token-by-token Processing

Another challenge faced by LLM models is their token-by-token processing. They generate one word at a time as output without planning the full structure of their responses. This makes it hard for LLMs to maintain logical consistency over long responses. Often, this leads to contradictions, factual errors, or repetitive statements  – much like someone writing an essay by guessing each next word instead of planning their thoughts.

While LLMs are powerful tools, their lack of deep reasoning and structured knowledge limits their reliability in fields like medicine, finance, and science, where accuracy and logical coherence are critical.

Example: Text-to-SQL for Business Intelligence

Let’s consider an enterprise scenario of implementing Text-to-SQL for Business Intelligence in a large enterprise as a chatbot. A user asks – “Show me the total monthly revenue from the Platinum Customers in the Middle East Region over the last eight quarters, excluding refunds and trial accounts“. The LLM-based chatbot might generate a partially correct SQL query in this case. Still, it will miss one or more critical components, such as exclusion criteria, date range, or joins across the normalized tables. This is because LLMs generate the SQL query linearly, word-by-word, ignoring any existing semantic structure.

How LCMs Solve This

By reasoning at the conceptual levels, a Large Concept Model parses the intent, maps the constraints, and generates a complete and logically accurate SQL query. It understands the constraint “excluding refunds and trial accounts” and concludes that it requires joins with the “customer type” and “monthly revenue” tables, implying consistency in the end-to-end logic.

Understanding Large Concept Models

Large Concept Models leave behind token-level processing and focus on hierarchical reasoning in an abstract embedding space to understand concepts and relationships, similarly to how humans think. LCM models are designed to be independent of language and to process underlying reasoning at the semantic level.

While an LLM might see the phrase “Drought reduces wheat production” as just a sequence of words, an LCM interprets it as a cause-and-effect relationship, understanding the bigger picture.

• Knowledge-Driven Reasoning  –  LCMs rely on knowledge graphs that define how concepts relate. For example, they explicitly represent relationships like “Drought → affects → Crop Yield” rather than just predicting the next likely word.
• Multimodal Understanding  –  Unlike LLMs, which focus mostly on text, LCMs can process speech, images, and even sign language, connecting them through a shared conceptual space.
• Logical and Statistical Hybrid  –  LCMs combine symbolic AI (rules and logic) with machine learning, allowing them to reason systematically. For instance, given “All droughts harm crops, and Region X is in drought“, an LCM can logically conclude that “Crop loss is expected in Region X“.

How LCMs Work Differently

Whether an LLM or an LCM, both function with the same objectives of text generation, summarization, question answering, translation, classification, data, and search augmentation. From this perspective, the fundamental difference between LCMs and LLMs is how they approach these tasks. For language translation tasks, for example, LLMs perform based on the data and language they are trained on, whereas LCMs use Semantic Object and Abstract Representation (SONAR).

SONAR is an embedding space trained or structured to capture conceptual similarity and structure, not just linguistic patterns. It allows models to better reason over abstract concepts, such as justice, economy, or consciousness, by positioning them in a dense vector space where semantic and relational properties are preserved. SONAR is also strong at auto-encoding sentences, i.e., turning sentences into vectors and back without much loss. As a result, it can understand and translate 200+ languages without additional language training.

LLMs are excellent for talking fluently, and LCMs are emerging to be excellent for thinking carefully.

From a broader perspective, this is how LLMs and LCMs are different from each other:

Aspect	Large Language Model (LLM)	Large Concept Model (LCM)
Unit of Understanding	Tokens [words, subwords]	Concepts (Sentence-level)
Training Objective	Predicting the next token	Predicting the next concept
Architecture	Transformer models trained on raw text	Conceptual embedding networks trained on sentence embeddings
Output Style	Fluent, word-by-word text generation	Coherent, structured concept output which is turned into text
Strengths	Creative writing and fluent conversation	Reasoning, structured understanding, multi-step logical task
Weaknesses	Hallucination, shallow reasoning, struggles with complexity	Not strong for creative text generation
Error Type	Hallucination (plausible but wrong text)	Conceptual mismatch (typically fewer random hallucination)
Best Use Cases	Chatbots, summarization, story telling, code generation	Decision support, structured Q&A, regulatory filings, reasoning
Usage Analogy	Story teller	Logical planner

The Concept-first Approach

LCMs use a concept-first approach to understand and structure knowledge based on high-level concepts before engaging in data-driven learning. Instead of relying purely on vast amounts of unstructured data, this approach ensures that models develop a foundational understanding of key principles, relationships, and hierarchies within a given domain before further training.

This concept-first approach makes LCMs far better at generalizing knowledge across different languages and contexts. For example, an LCM trained on English medical data could diagnose diseases in Swahili  –  not by direct translation, but by recognizing universal medical concepts.

By focusing on meaning rather than mere word patterns, LCMs represent the next step in AI , moving from fluent text generation to true understanding and reasoning.

Large Concept Models  –  The Architecture

Large Concept Models are built on a hybrid architecture that combines structured knowledge representation with the adaptability of neural networks. This enables them to reason logically while handling real-world complexity, which is an advancement over purely statistical AI models.

At the core of LCMs lies a structured, hierarchical process. Input text is first broken down into sentences, which are treated as fundamental conceptual units. These sentences are then passed through SONAR.

The Fundamental Architecture of Large Concept Model (Source here)

Once encoded, the sequence of concepts is processed by the model, which operates entirely in the embedding space. This language-agnostic approach allows LCMs to perform reasoning independent of any specific language or input format, making them adaptable beyond text and speech. The generated concepts are then decoded back into language or other modalities using SONAR, enabling output in multiple languages or formats without rerunning the model.

Two key architectures have emerged in this space: Base-LCM, the initial approach, and Diffusion-Based LCMs, an advanced version inspired by image generation techniques. Both leverage this structured pipeline , ensuring more logical and contextually aware AI responses.

Base-LCM  –  The First Step

The Base-LCM architecture was the first Step in Large Concept Models. It works similarly to Large Language Models (LLMs) but instead of predicting the “next word”, it predicts the “next concept” in a structured conceptual space.

How Base-LCM Works

The model receives a sequence of concepts and learns to predict the next concept. It uses a Transformer-based architecture with additional layers:

• PreNet: Adjusts concept embeddings for processing.
• Transformer Decoder: Processes relationships between concepts.
• PostNet: Maps the output back to the original concept space.

Illustration of The Base LCM Architecture (Source here)

The training process minimizes errors between predicted and actual concepts using Mean Squared Error (MSE) loss.

Diffusion-Based LCMs  –  A Smarter Way to Predict Concepts

Inspired by image generation diffusion models, this architecture refines predictions by gradually removing “uncertainty” or “noise” from possible next concepts.

How Diffusion-based LCM Works

Imagine generating an image of a cat from random noise – each step removes noise until a clear image emerges. Diffusion-based LCMs apply the same idea to concept predictions, refining them over multiple steps.

There are two approaches to Diffusion-based LCMs:

1. One-Tower LCM  –  In this approach, it processes a sequence of concepts where only the last concept is “noisy” (uncertain). Then, iteratively refines this noisy concept until it reaches a clear prediction. This is similar to Base-LCM but improves predictions by running multiple refinement steps.
2. Two-Tower LCM  –  In this approach, it separates context encoding from concept refinement. The first model understands the previous concepts, while the second denoises the next concept. Then it uses cross-attention mechanisms to make predictions more accurate.

Diffusion-based LCM Architecture illustration | Left side  –  One Tower LCM | Right side  –  Two Tower LCM. (Source here)

Studies show that diffusion-based models significantly outperform Base-LCM in ROUGE-L Score (which measures how well the model maintains meaning in generated summaries) and Coherence Score (which evaluates the logical flow and consistency of predictions).

Limitations of Base-LCM and Diffusion-based LCM

The primary issue with the Base-LCM architecture was that the LCM struggles with ambiguity due to how it represents concepts using a fixed embedding space like SONAR. It works well for simple and short sentences but struggles with complex and loosely related sentences. Also, it cannot handle numbers, links, or code reliably. Another issue is that sometimes one single sentence may contain multiple concept,s but the model treats them as a single concept. Multiple concepts could logically follow a given input in many cases, but this model can only select one. These limitations led to the development of Diffusion-Based LCMs, which can handle multiple possibilities more effectively.

The Diffusion-based LCMs can handle multiple possible outputs better than the Base-LCMs, but they also have some limitations. The diffusion models work well for continuous data like images or audio, but text is more structured and discrete. This makes it harder for the diffusion models to generate accurate or meaningful text results. Meta attempts to fix this with the Quantized models like Quant-LCM, but the SONAR space is not designed to be easily quantized, and hence, results are often complex and struggle with data sparsity.

Diffusion-based LCMs outperformed Quant-LCM in Meta’s ablation experiments; hence, I haven’t included the details of those models in this article. To improve further, a better way to represent text that balances structure and flexibility needs to be developed.

Real-world Application of LCMs

The conceptual understanding, structured reasoning, and multistep logical thinking capabilities of LCMs make them suitable for real-world applications requiring more sophisticated reasoning, context, and concepts. Next, we will talk in detail about a couple of real-world LCMs applications based on my current project experiences, which I am working on.

Advanced Customer Support Ticketing & Resolution

A large global organization, which manages complex infrastructure across business parks, corporate houses, universities, government establishments, and manufacturing units, faces a unique set of challenges in its customer support operations – scale, multilingual interactions, task complexity, duplicate requests, urgency or severity, and personalization demands. Traditional LLM-based systems fall short as they require custom solutions deployed for each geography or type of establishment. Here, Large Concept Models offer a transformative advantage by reasoning at a conceptual level, rather than just processing keywords. LCMs deeply analyze incoming support requests, correctly classify over 450+ types of support tasks amongst 50+ departments, and generate structured support tickets.

In conjunction with LLMs + RAG, LCMs also enable intelligent auto-responses – acknowledging user requests, offering DIY solutions or step-by-step troubleshooting processes based on knowledge bases, and autonomously handling database-driven queries. With concept-level understanding and multilingual capabilities, LCM helps the global support center to deliver seamless, culturally aware, highly personalized assistance in local languages (15+ languages across the Middle East, Europe, and Asia).

SQL Query Generation from Natural Language

Continuing the above use case of Advanced Customer Support Ticketing & Resolution ecosystem, which spans multiple countries, infrastructure facilities, and databases, let’s talk about the automation of data-driven query responses, which are significantly complex. Though LLMs can perform basic text-to-SQL translation, they struggle with multistep understanding and mapping the user’s intent to the right database, access rights, and contextually correct queries. Large Concept Models fundamentally elevate this process by reasoning at a conceptual level. They systematically understand the user intent, operational context, and schema relationships before generating the SQL.

This results in fewer errors, higher accuracy, and deeper understanding of nuances such as department-specific or geographic-specific data handling rules. With multilingual input capabilities, LCM can easily process natural language queries in 15+ languages and provide intelligent automated support resolution at scale. In contrast, without conceptual reasoning, LLMs can misinterpret queries, expose sensitive data, and compromise database integrity – risks which LCMs can prevent.

Regulatory and Compliance Filings

Another use case in which I am currently working is the automation of regulatory and compliance filings for the SEC in the Registered Investment Advisors space. This space is heavily regulated and demands compliance with ever-evolving regulations with precision, consistency, and contextual understanding. Traditional automation using LLMs generally falls short because the tasks require deep conceptual mapping, which involves understanding rules, mapping them to nuanced financial data, and structuring information following regulatory schemas and formats.

Large Concept Models are well-suited for this challenge as they can reason across multiple interconnected compliance concepts, accurately classify complex financial information, validate completeness, and generate structured outputs aligned with SEC standards. In this space, various filings and periodic updates from the regulatory bodies require cross-referencing and analyzing the information from different financial disclosures, data sources, communications, and operational details. LCMs provide intelligence to automate the creation of draft filings, which further reduces risk by bringing accuracy and time by processing quickly.

Drawbacks of LCMs

While LCMs offer significant advances over traditional LLMs, they also have their own challenges. These limitations must be carefully considered when choosing an LCM. Understanding these drawbacks is essential to making informed architectural decisions.

1. LCMs require more sophisticated pipelines than LLMs, making them expensive and resource-intensive to train and fine-tune the models.
2. LCMs require significant upfront investments in concept engineering and integrating data sources.
3. LCMs need concept-labeled datasets, which you need to label. Currently, no such datasets are publicly available.
4. LCMs are indeed better at structured reasoning, but understanding their internal conceptual pathways is still difficult, making auditing more challenging.
5. LCM’s concept layers require more memory and compute even during the inference time.
6. LCMs are still emerging, and only a few open-source models or data sets are available.
7. Fine-tuning an LCM often requires reworking part of the concept space or concept relation structures.

Comparing Different Approaches to AI Reasoning

With the evolution of AI-based systems, different architectures offer different capabilities and strengths. LLMs, LLMs + RAG, and LCMs represent different AI reasoning and reliability approaches. The following table compares and highlights the key differences to help you select the right model.

Aspect	LLMs	LLMs + RAG	LCMs
Core Approach	Trained on large text corpus, generate based on learned language patterns	LLM + external document retrieval to answer	Structured reasoning over extracted and aligned concepts from data
Knowledge Source	Internal – based on data memorized during training	External – retrieved at query time	Internal – concept graphs and relationships with reasoning layers
Hallucination Risk	High	Reduced, but possible in case of poor retrieval	Very low, because outputs are structured and based on concepts
Reasoning Ability	Limited – only pattern recognition	Limited – can quote retrieved documents, has shallow understanding	Strong – multi-step logical and causal reasoning over concepts
Domain Adaptability	Need to fine-tune model with domain specific data	Retrieval can be adapted without fine-tuning or retraining	Need careful domain concept engineering which has slower but deeper adaptation
Interpretability	Black box – hard to trace why an answer was generated	Slightly better – as it can point to retrieved textual context	Better – conceptual pathways and logics can be analysed
Real-time Data Handling	Poor – needs retraining	Good – live retrieval of contextual text	Moderate – update concepts periodically or connect to live systems
Training Complexity	High	High for LLM; Moderate for RAG	Very high
Inference Cost	Moderate	High	High
Best For	Text generation, creativity and conversational AI	Knowledge-base based Q&A, document-based search and enterprise QA bots	Deep automation, decision making, multi-hop reasoning and personalized action

Setting up Meta’s Large Concept Model

Let’s start our hands-on journey with Meta’s Large Concept Model with Meta’s LCM.

Clone the Repository

Get the official LCM code

git clone https://github.com/facebookresearch/large_concept_model.git

cd large_concept_model

Prepare your Environment

For this guide, I am using Google Cloud Instance with 8 vCPU, 30 GB RAM and 1 NVIDIA Tesla P4 GPU.

1. Install ‘uv’ using:

> curl -Ls https://astral.sh/uv/install.sh | bash
> echo 'export PATH="$HOME/.cargo/bin:$PATH"' >> ~/.bashrc
> source ~/.bashrc
> uv --version

2. Clone the large concept model repository:

> git clone https://github.com/facebookresearch/large_concept_model.git
cd large_concept_model/

3. Create a virtual environment with all necessary packages:

> uv sync --extra cpu --extra eval --extra data

4. Install NVIDIA drivers:

> wget https://us.download.nvidia.com/tesla/535.247.01/nvidia-driver-local-repo-ubuntu2204-535.247.01_1.0-1_amd64.deb
> sudo chmod +x nvidia-driver-local-repo-ubuntu2204-535.247.01_1.0-1_amd64.deb
> sudo apt install ./nvidia-driver-local-repo-ubuntu2204-535.247.01_1.0-1_amd64.deb
> lspci | grep -i nvidia
> uname -m && cat /etc/*release

5. Install CUDA:

> wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
> sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
> wget https://developer.download.nvidia.com/compute/cuda/12.8.1/local_installers/cuda-repo-ubuntu2204-12-8-local_12.8.1-570.124.06-1_amd64.deb
> sudo dpkg -i cuda-repo-ubuntu2204-12-8-local_12.8.1-570.124.06-1_amd64.deb
> sudo cp /var/cuda-repo-ubuntu2204-12-8-local/cuda-*-keyring.gpg /usr/share/keyrings/
> sudo apt-get update
> sudo apt-get -y install cuda-toolkit-12-8

6. Install NVIDIA utils:

> apt install nvidia-utils-390

7. Reboot the instance

8. Install CUDA:

> sudo chmod +x  cuda-repo-ubuntu2204-12-8-local_12.8.1-570.124.06-1_amd64.deb 
> sudo apt install ./cuda-repo-ubuntu2204-12-8-local_12.8.1-570.124.06-1_amd64.deb 
> sudo apt-get install ./nvidia-driver-local-repo-ubuntu2204-535.247.01_1.0-1_amd64.deb
> sudo apt-get install -y cuda-drivers

9. Check NVIDIA driver installation:

> nvidia-smi

10. Create a directory for data preparation:

> mkdir prep_data

11. Install build-essentials and g++

> sudo apt install -y build-essential cmake g++-11
> sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-11 100
> sudo update-alternatives --config g++
> g++ --version

12. Fairseq2 requires libsndfile1, hence, install it:

> sudo apt-get install libsndfile1

13. Install Torch with GPU using uv:

> uv pip install torch==2.5.1 --extra-index-url https://download.pytorch.org/whl/cu121 --upgrade

14. Install fairseq2 using uv:

> uv pip install fairseq2==v0.3.0rc1 --pre --extra-index-url  https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cu121 --upgrade

15. Install the Large Concept Model in your environment

pip install -e .

Test for Successful Installation

1. Create a directory in root folder of repository by name prep_data:

> mkdir prep_data

2. The LCM provides a sample processing pipeline that can be used to prepare training data, and this is what we will use to test our installation of LCM:

> uv run --extra data scripts/prepare_wikipedia.py prep_data/

Output

You will get the following output in around 25 minutes. This pipeline shows how to get a dataset from HuggingFace and process it with SONAR and SaT. The script provides an example pulling data from HuggingFace and creating the prepared data file named 0_467e0daf78e07283_0_0.parquet in parquet format in the prep_data folder.

Further, you can add more GPUs to the instance and run the training by following the instructions in the README.md file in the repo. Currently, Meta has not released the weights or trained models. Hence, you need to train yours.

However, it is not possible to train or fine-tune the models on our systems because for the experimental study, Meta trained its models on the Finewebedu dataset. All models were trained with 1.6B parameters for 250k optimization steps and a total batch size of 229k concepts, which was carried out on Meta’s Research SuperCluster of 32 A100 GPUs.

The Road Ahead for AI and LCMs

The future of AI lies in blending the structured reasoning of Large Concept Models with the linguistic fluency of Large Language Models. This fusion could create AI systems that analyze complex scenarios and communicate their insights clearly.

Imagine an AI-powered strategy consultant that:

• Uses LCMs to simulate market trends and predict outcomes.
• Leverages LLMs to explain decisions in human-like narratives.
• Continuously learns and updates its knowledge from real-world feedback.

Hence, this hybrid approach will make expert-level analysis accessible to more people while maintaining human oversight. Regulatory bodies and governments, like the US Government, are already mandating auditable reasoning trails for high-risk AI systems to ensure transparency.

Making AI More Explainable

One of the biggest advantages of LCMs is their ability to explain decisions clearly. Unlike traditional AI models, which rely on complex neural activations, LCMs structure their reasoning in a way humans can follow.

For example, an LCM-based AI recommending a medical treatment could show how patient symptoms connect to possible diagnoses and treatments using structured medical knowledge. This transparency builds trust and allows doctors to verify and refine the AI’s suggestions, leading to better outcomes.

Human-AI Collaboration

LCMs are designed to work alongside human experts, not replace them. By organizing knowledge to align with human thinking, they serve as intelligent partners rather than unpredictable black boxes.

This can transform industries, for example:

• Scientists could use LCMs to test hypotheses and uncover insights faster.
• Business leaders might rely on them to evaluate market strategies and predict risks.

By bridging structured reasoning with adaptable AI, LCMs promise a future where humans and AI work together more effectively, unlocking deeper insights and smarter decision-making.

Conclusion

Large Concept Models represent a major step toward AI that reasons, not just predicts. By structuring knowledge instead of relying on patterns, they tackle challenges like misinformation and lack of explainability in AI. While adoption requires collaboration  –  from refining knowledge extraction to standardizing auditing  –  LCMs can potentially transform fields like healthcare, governance, and business strategy. The future of AI isn’t about replacing human judgment but enhancing it with clearer reasoning and deeper insights.

References

1. Arxiv: Large Concept Models – Language Modeling in a Sentence Representation Space
2. GitHub Repo: Facebook Research – Large Concept Models
3. SONAR: Sentence-Level Multimodal and Language-Agnostic Representations