AI Architectures: LLMs, LAMs, LCMs, and LFMs

Artificial Intelligence (AI) has seen a rapid evolution, giving rise to a variety of architectures tailored to address specific challenges and applications. In this article, we dive deep into the comparison of four cutting-edge AI architectures: Large Language Models (LLMs), Large Agentic Models (LAMs), Large Concept Models (LCMs), and Liquid Foundation Models (LFMs). Each of these architectures represents a significant milestone in AI development, designed to push the boundaries of reasoning, contextual understanding, and multimodal capabilities.

What Sets These AI Architectures Apart?

Here’s how these architectures compare across critical aspects:

1. Large Language Models (LLMs)

• Core Function: Language understanding and generation.
• Primary Strength: Generating coherent, contextually relevant text.
• Reasoning Ability: Single-step reasoning based on language patterns.
• Contextual Understanding: Good at internal textual context; limited in applying external knowledge.
• Problem-Solving: Providing information or answering questions based on existing data.
• Learning Approach: Pattern recognition from large datasets.
• Application Scope: Content creation, translations, simple Q&A, and chatbots.
• Scale & Memory: Larger memory requirements, limited long-context efficiency.
• Towards AGI: A step in the journey towards AGI, but limited.
• Multimodal Capabilities: Limited to language (primarily text-based).
• Notable Limitations: Weak multi-hop reasoning; limited in domain-specific decision-making.
• Unique Feature: Token-level input-output processing.

2. Large Agentic Models (LAMs)

• Core Function: Language understanding, generation, complex reasoning, and actions.
• Primary Strength: Advanced reasoning, multi-hop thinking, generating actionable outputs.
• Reasoning Ability: Multi-step reasoning for handling interconnected tasks and goals.
• Contextual Understanding: Superior understanding of textual and external context.
• Problem-Solving: Proposing solutions, strategic planning, decision-making, and autonomous actions.
• Learning Approach: Self-assessment and reasoning with advanced learning algorithms.
• Application Scope: Autonomous systems requiring advanced planning, research, and task execution.
• Scale & Memory: Higher computational resources; designed for agentic reasoning.
• Towards AGI: A leap towards AGI, integrating reasoning and action.
• Multimodal Capabilities: Focus on reasoning and action but primarily text-based.
• Notable Limitations: High computational overhead; constrained by external and policy-driven data integration.
• Unique Feature: Multi-hop reasoning and agentic action generation.

3. Large Concept Models (LCMs)

• Core Function: Language modeling at higher abstraction (concepts), focusing on semantic-level sentence representation.
• Primary Strength: Handling high-level semantic representation using SONAR for text and speech, supporting 200 languages.
• Reasoning Ability: Autoregressive sentence prediction in embedding space; limited to concepts.
• Contextual Understanding: High-level abstraction via concept embeddings; language-agnostic.
• Problem-Solving: Semantic understanding of multi-lingual text and speech.
• Learning Approach: Training on sentence embeddings using autoregressive methods (e.g., MSE regression, diffusion-based generation).
• Application Scope: Multilingual generalization, summarization, and summary expansion.
• Scale & Memory: Supports 1.6B–7B models trained on trillions of tokens.
• Towards AGI: Concept-based reasoning introduces modular AGI possibilities.
• Multimodal Capabilities: Language and modality-agnostic; supports text and speech.
• Notable Limitations: Dependency on SONAR embedding for semantic representation; limited innovation in generative tasks.
• Unique Feature: Concept-driven modeling with language-agnostic embeddings.

4. Liquid Foundation Models (LFMs)

• Core Function: General-purpose AI with dynamical systems design, supporting sequential multimodal data processing for reasoning and decision-making.
• Primary Strength: State-of-the-art efficiency in memory and inference with dynamic, adaptive learning rooted in signal processing and numerical linear algebra.
• Reasoning Ability: Strong reasoning, efficient long-context understanding, suitable for advanced reasoning in multimodal domains.
• Contextual Understanding: Effective for long-context tasks (32k tokens); superior for document analysis, summarization, and Retrieval-Augmented Generation (RAG).
• Problem-Solving: Handling diverse sequential data, supporting various fields (finance, biotech, consumer electronics); offers adaptive and cost-effective deployment.
• Learning Approach: Deep learning rooted in dynamical systems and numerical methods; custom computational units enhance performance across data modalities.
• Application Scope: Highly efficient AI for text, audio, video, time-series, and signals; long-context tasks on edge devices; strong in reasoning and multimodal capabilities.
• Scale & Memory: Efficient memory footprint with long-context processing (up to 32k tokens); reduced memory and inference overhead.
• Towards AGI: Expands the Pareto frontier of AI; designed to optimize cost-performance tradeoff, scaling across industries like finance, biotech, and consumer electronics.
• Multimodal Capabilities: Supports multiple modalities: video, audio, text, time-series, and other sequential data.
• Notable Limitations: Zero-shot coding challenges, suboptimal numerical calculations, and limited human preference optimizations; models not open-sourced.
• Unique Feature: Dynamically adaptive architecture leveraging signal processing, with efficient resource utilization for edge deployment.

With this fast growing AI domain, we have seen almost exponential growth in past few years and I believe there is lot more to come which will be available to end-users. The above information is my understanding after reading about these complex architectures. There can be plus-minus in my undertsnading which I am happy to learn and discuss.

Which architecture resonates most with your work? Let’s discuss in the comments below!

REFERENCES:

1. https://ai.meta.com/research/publications/large-concept-models-language-modeling-in-a-sentence-representation-space/
2. https://www.liquid.ai/liquid-foundation-models
3. https://www.salesforce.com/blog/large-action-models/
4. https://arxiv.org/abs/2409.03215
5. https://arxiv.org/abs/1706.03762

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