DEV Community: Sujal Suyash

Flowing vs. Thinking: How Liquid Neural Networks Diverge from LLMs

Sujal Suyash — Mon, 20 Jul 2026 16:41:29 +0000

If you follow the world of Artificial Intelligence, it is easy to assume that scaling up is the only path forward. Large Language Models (LLMs) have dominated the conversation by scaling to hundreds of billions of parameters, acting as massive, discrete reasoning engines.

But not all problems require a massive library of tokens. In the world of robotics, physical sensors, and continuous time, a different architectural philosophy is thriving: Liquid Neural Networks (LNNs).

LLMs process static, discrete symbols, LNNs use highly expressive fluid equations to navigate the chaotic, continuous flow of the physical world. To understand why an LNN can solve complex physics tasks with just a fraction of the compute of an LLM, we have to look at the actual mathematics.

The Common Misconception About "Liquid" Adaptation

People often state that LNNs "adapt" according to the input they receive. In the context of LLMs, we usually think of adaptation as shifting attention weights across discrete tokens. When people hear that LNNs adapt dynamically, a common misconception arises: Do LNNs actually change their weights or biases during the forward pass?

The short answer is no.

Just like an LLM, an LNN's parameters — its input weight matrix ( $U$ ), recurrent weight matrix ( $W$ ), and biases ( $b$ ) — are completely fixed after the training phase is complete.

So, what makes them "liquid"?

The adaptation happens within the hidden state ( $h (t)$ ) and the effective time constant ( $τ (t)$ ) of each individual neuron, which change continuously with time. Unlike LLMs, which are governed by the discrete-time architecture of the Transformer, LNNs are fundamentally built on Ordinary Differential Equations (ODEs).

Inside an LNN, the equation describing a neuron's state looks like this:

\frac{d h ( t )}{d t} = - \frac{h ( t )}{τ ( x ( t ) , h ( t ))} + f (W \cdot h (t) + U \cdot x (t) + b)

Where $f$ is a standard non-linear activation function (like $tanh$ or sigmoid).

Notice the time constant $τ$ . In a traditional continuous-time neural network, $τ$ is a fixed number. In an LNN, $τ$ is a dynamic function of the current input $x (t)$ and the current hidden state $h (t)$ .

In code, the right-hand side of that ODE is just this:

import numpy as np

def dh_dt(h, x, W, U, b, tau_fn, f=np.tanh):
    """
    LNN ODE: dh/dt = -h / tau(x, h) + f(W·h + U·x + b)
    tau_fn(x, h) -> the dynamic time constant function.
    """
    # 1. Compute the dynamic, liquid time constant
    tau = tau_fn(x, h)

    # 2. Compute the continuous rate of change
    return -h / tau + f(W @ h + U @ x + b)

# To update the hidden state over a discrete time step (dt):
def euler_step(h, x, W, U, b, tau_fn, dt):
    return h + dt * dh_dt(h, x, W, U, b, tau_fn)

Therefore, "adaptation" in an LNN doesn't mean the network is rewriting its own code (altering weights) on the fly. Instead, it means the speed and trajectory of the neuron's state change based entirely on the input. If the incoming data suddenly becomes noisy, erratic, or unpredictable, the neuron can mathematically "slow down" its integration to filter out the noise — all without needing new weights.

The Architecture of the Continuous World: Where LNNs Excel

Because of this unique, continuous-time framework, LNNs are uniquely suited for physical, real-world, streaming environments.

Ultra-Low Parameter Footprint & Edge Deployment: While an LLM needs massive server farms, LNNs achieve staggering complexity with very few parameters. Because they require orders of magnitude less memory and power, LNNs can run directly on edge devices, microcontrollers, and drones.
Handling Noisy, Irregular Time-Series Data: Transformers process time discreetly, expecting data in neat, sequential chunks. If you are analyzing continuous motion or gait datasets to detect subtle biomechanical anomalies, LLMs struggle. LNNs thrive here because their underlying ODEs treat time as a continuous flow.
Out-of-Distribution Survival: When an LLM encounters a scenario completely foreign to its training data, it hallucinates. If a drone powered by an LNN is trained in clear skies but suddenly encounters a heavy rainstorm, its liquid time constants automatically adjust to compensate for the erratic sensor noise, allowing it to maintain control.

The Architecture of the Discrete World: Where LLMs Excel

Despite the elegance of LNNs, they are not designed to process semantic knowledge. LLMs remain the undisputed engines of reasoning and massive knowledge retrieval.

Discrete Logic and Language: Language is not a continuous, flowing physical signal; it is composed of discrete symbols. The self-attention mechanism of LLMs allows them to weigh the relationship between every word in a document simultaneously. If you are building a system to parse and classify legal contract clauses, you reach for an LLM. LNNs, as sequential ODE solvers, are fundamentally ill-suited for parsing complex grammar or writing code.
Encyclopedic World Knowledge: Because LLMs boast billions of parameters, they double as incredibly vast, compressed databases of human knowledge. An LNN's hyper-efficient, compact footprint means it possesses virtually no capacity to retain world facts.
Long-Range Static Context: If you feed a massive codebase into a long-context LLM, it can instantly connect a variable on page 1 with a function on page 99. Because LNNs process data sequentially over time, they are susceptible to "forgetting" older static information when forced to read massive blocks of text.

Summary: Different Engines for Different Realities

Feature	Liquid Neural Networks (LNNs)	Large Language Models (LLMs)
Primary Domain	Robotics, autonomous systems, continuous sensors	Text, code, semantic reasoning
Time Paradigm	Continuous (ODEs)	Discrete (Tokens + Attention)
Scale	Extremely small (thousands of parameters)	Massive (billions of parameters)
Adaptation Mechanism	Dynamic, input-dependent time constants ( $τ$ )	Contextual attention weights

Ultimately, we are looking at two brilliant but entirely distinct philosophies of computation. LLMs are built to store, parse, and reason through the accumulated, discrete data of human history. LNNs are built like agile nervous systems to react, filter noise, and adapt to the chaotic, continuous flow of the physical world.

The Data Bottleneck and the Myth of "Recursive Learning" in LLMs

Sujal Suyash — Mon, 20 Jul 2026 10:51:30 +0000

The ultimate competitive moat isn't just algorithmic magic; it's data hoarding and compute infrastructure.

Trying to train a Large Language Model (LLM) from scratch requires a massive amount of computational power, resources, and, most importantly, data. Nowadays, even if we hypothetically consider that we can create a very powerful machine capable of performing large and complex computations, the biggest issue that lies in front of us is data. It is the most vital component responsible for generating a model.

Big MNCs like Google, Anthropic, and OpenAI were able to build their own LLMs from scratch because they had huge funding, years of collected user data, and very advanced, complex machines for training. These resources are generally not available to the general public. It has become a massive business model: they create huge LLMs, and the general public is forced to purchase API keys or subscriptions to get full access to them. The ultimate competitive moat isn't just algorithmic magic; it's data hoarding and compute infrastructure.

But while the economics of these models are clear, there is a common misconception about how they actually operate.

The Myth of Live Recursive Learning

A popular theory is that models like ChatGPT, Claude, and Gemini are based on "Recursive Learning", that they are self-learning machines constantly updating themselves based on the data you provide in a chat, recursively minimizing their loss with every prompt you enter.

In reality, current LLMs do not use a live, continuous recursive learning loop. When you interact with an LLM, its core neural network weights are completely frozen. The "learning" happens in massive, centralized, and highly structured phases long before you ever type a prompt.

Then how LLMs actually learn ?

Instead of live recursive updates, models are trained in distinct and compute heavy stages:

Pre-training: The model is fed trillions of tokens from diverse sources (the web, books, code). Its only objective is to predict the next word (Self-Supervised Learning). This is where it learns grammar, facts, and reasoning patterns. The compute required here is massive, taking thousands of GPUs months to complete.
Supervised Fine-Tuning (SFT): The model is trained on 10k–100k high-quality, human-curated instruction-response pairs. This teaches the model to follow instructions, adopt an "Assistant" persona, and format responses correctly.
Reward Modeling & RLHF (Reinforcement Learning from Human Feedback): Humans rank different model outputs. The AI uses these rankings to train a "Judge" model, which then guides the main model to generate responses that are safer and more aligned with human preferences.

The Danger of True Recursion: Model Collapse

What would happen if an AI actually did train recursively on its own generated outputs? It leads to a critical failure known as Model Collapse.

If an LLM trains iteratively on synthetic data (data generated by itself or other LLMs) without being grounded by fresh, human-generated data, its performance degrades over successive generations. The model begins to forget rare, nuanced information and over-indexes on the most probable, generic tokens. Eventually, this amateurish recursive loop causes the model to generate repetitive, homogenized, and nonsensical outputs.

So where does recursion actually happens ?

While live learning isn't happening in your chat window, Recursive Self-Improvement (RSI) is a massive focus in AI research. AI is increasingly being used to build the next generation of AI, but as a carefully supervised engineering tool rather than an automatic black box.

For instance:

Companies use highly capable models to generate rigorously filtered datasets to train smaller models.
AI agents are used to write the code that optimizes training infrastructure.

Ultimately, the biggest challenge in the field remains exactly what you might suspect: the DATA BOTTLENECK. We are running out of high-quality, human-generated text on the internet to train the next generation of models. That finite supply of human data, combined with the extreme cost of compute, is exactly why proprietary infrastructure remains the ultimate, heavily-guarded moat.