The crowded “Large XXX Model” acronym keeping getting denser. We now have another new concept Large concept model. Introduced my Meta in December 2024 (attached technical paper), there have been a few good discussions on this topic last month.

In technical terms LCM is nothing but models that process information at a higher level of abstraction as compared to other traditional AI models i.e. if LLMs are working at a token level, LCMs reason in terms of concepts which are nothing but ideas or concepts that are representations agnostic of language and modality.

The question that you might have is – “How does it differ from a Large Language model (hashtag#LLM), Large Action Model (hashtag#LAM) and a Large Context Model (hashtag#LCoM)?”

As we get into a muti agentic ecosystem, we will using a wide range of models that are best suited for the specific tasks in an usecase.

I already see that when a customer is trying to take their low hanging usecases agentic; they are breaking it down into task and then analyzing the model that gives the best performance to execute that task.

Precisely the reason why you should go deeper and understand how these 4 concepts (though they look very similar) are very different from each other.

LLM -> They work at token level, language centric and prone to hallucinations
LAM -> Suitable for task automation an agentic AI – e.g. robotics
LCoM -> Perfect for multi step reasoning, document processing etc.
LCM -> Better for long form content and cross lingual reasoning

The future success of a muti-agentic ecosystem will rely on how well to are able to distill the pros and cons of each of these type of models. Though I believe that LLM will become the backbone and do the heavy lifting for a “Multi Agentic” Ecosystem (mainly due to you its generative capabilities), the other models will find their niche areas where they will bring the much needed efficiencies to scale.

Below is a table that I have tried to explain these 4 concepts by taking a very simple usecase of “Planning a family vacation”.

I hope this brings out the strengths and practical applications of where each of these models excel in a real life usecase.