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Towards artificial sentience.

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  1. LLMs are Crystallized-Conscious
    Sep 6, 2026 · original
    Are LLMs conscious? Today’s LLMs are missing some capabilities that we typically associate with consciousness, such as persistent memory 1 , continuous updating, or an ability to interact with their environment. Nonetheless, the question of whether an LLM is conscious while performing inference seems valid. Some people argue that LLMs can’t possibly be conscious, because they only exist in a computer and are not biological systems. This is the substrate dependence assumption. However, those arguments have never resonated with me. As of today, I’m not aware of any fundamental reason for why a neural-network based AI couldn’t in principle be conscious. A big challenge stems from the fact that consciousness is a poorly defined phenomenon. How could we even measure it in an LLM? Functional assessment of consciousness One approach for measuring consciousness is the functional one, which works
  2. Hallucinate any App, One Screen at a Time
    Dec 10, 2025 · original
    A major trend of this year has been vibe coding – using LLMs to create software from the ground up, without a human ever interacting with the source code directly. The current consensus is that vibe coding isn’t quite ready yet for developing critical production software. But while those issues are being worked out, we can start thinking about what the next thing after vibe coding could look like. One question that comes to mind: Why even bother with creating source code in the first place? Auto-regressive transformers are the ideal simulation machine. LLMs in particular excel at mimicry, at pretending to be whatever persona they were exposed to in their training data. For the most part, the simulation is that of a human in various text-producing roles. Be it as the author of a book, a contributor to Wikipedia, a commenter on Reddit, or an ever-helpful assistant in a chat session. Howeve
  3. LLMs Don’t Have Introspection
    Dec 25, 2024 · original
    Assume we want to use a vision-language model (VLM) to look at a given image and determine certain properties about it. Let’s say that, for the sake of the argument, we would like it to determine whether the image contains anything funny or not. As we all know, jokes are the most funny when you have to explain them. So, in the spirit of explainable AI, we’d additionally like the model to tell us why it found an image funny, or, more generally, why it made the particular determination (whether funny or not). Writing a prompt to accomplish this goal is quickly done. A first attempt could look like this: Consider the image above. Does this image show anything funny? Please answer with “yes” or “no”, and then provide an explanation for your decision. At first, it looks like this prompt works just fine. Here are two example images that I ran through OpenAI’s o4 model, one more humorous and th
  4. Permanence – One Prior to Rule Them All? [Perm. 2/6]
    Jun 26, 2024 · original
    This article is part of the series “ Permanence might be all you need “. The series documents a side project of mine in which I explore invariant learning. Also in this series: Priors and Invariants, a Primer Permanence – One Prior to Rule Them All? [coming soon] Learning Continuous Concept Spaces [coming soon] Instantiation and Feature Binding in Neural Networks [coming soon] Deeply Meaningful Representations with PtolemyNet [coming soon] Towards Predictive, Generalizing World Models From invariant representations… In machine learning, we often represent the state of a system as vectors. Artificial neural networks take the vector representation of an input, and transform it into a different vector which components are more meaningful to the problem at hand. Such a vector representation is said to be invariant under operation X, if you can apply X to the model’s input without changing th
  5. Priors and Invariants, a Primer [Perm. 1/6]
    Jun 26, 2024 · original
    This article is part of the series “ Permanence Might Be All You Need “. The series documents a side project of mine in which I explore invariant learning. Also in this series: Priors and Invariants, a Primer Permanence – One Prior to Rule Them All? [coming soon] Learning Continuous Concept Spaces [coming soon] Instantiation and Feature Binding in Neural Networks [coming soon] Deeply Meaningful Representations with PtolemyNet [coming soon] Towards Predictive, Generalizing World Models We need priors to learn If you’re already familiar with inductive priors and the No Free Lunch Theorem, feel free to skip this section. Intelligence is the ability to utilize past experience in order to respond intelligently to new, previously unseen situations. Responding intelligently typically means responding in a way that increases the likelihood of achieving a desirable outcome, such as correctly stat
  6. Permanence Might Be All You Need
    Jun 26, 2024 · original
    The notion of object permanence , “the understanding that objects continue to exist even when they cannot be seen”, is considered an important developmental step in young children, and often used as an indicator of advanced mental capabilities in animals. But could permanence play a much more fundamental role in how intelligence forms? Could it in fact hold the key to how we learn to understand the world around us? In this series, I explore how striving for “permanence” in a world model can give rise to recognizing structure in the world we live in. I propose the notion of a permanence prior, and motivate how it could improve the data efficiency and generalization abilities of models trained in a self-supervised fashion. Finally, I propose a new neural network architecture – called PtolemyNet – that combines this permanence prior with a learned notion of deep, locality-aware concept spac
  7. Predictable is Boring, but so is Chaos
    Apr 13, 2024 · original
    Imagine an artificial organism living in a complex environment. The organism is not just a passive observer. It can decide which parts of its environment it wants to explore next. It might even be able to actively influence the world around it by taking various actions. We want the organism to learn from its experiences and build an understanding of the world it lives in. In this post, I’m going to sketch out a high-level architecture for active world model learning. I’ll then describe a thought experiment to investigate an apparent paradox that arises when trying to incentivize exploration, and give an intuition for how it can be avoided. What it Means to Understand the World While this is a philosophical question and there are probably many ways to answer it, I’ll focus on the following key aspects: Understanding means having a mental model that is capable of: Estimating the effects th
  8. Noisy is Better — Improving Performance with Gaussian Noise
    Jan 22, 2024 · original
    In my previous post about ThoughtNet , an attention-based neural architecture for variable-compute inference, I highlighted two limitations that I encountered with it: Slow and inconsistent convergence during training time Poor generalization on multiplication tasks, despite great performance on addition. While trying to solve the second problem, I stumbled across a surprising way to stabilize training convergence as well. Was ThoughtNet Cheating? While attempting to understand why my ThoughtNet models weren’t generalizing much beyond their training data on multiplication problems, I noticed that the operator selection scores in a given iteration were oftentimes divided among multiple operators. In the image below, you can see scores being divided almost evenly between operators (called “branch” in the image) 2 and 0 in iteration 2. I even encountered some examples where three or four op
  9. Variable-Time Neural Computation
    Nov 28, 2023 · original
    The majority of today’s artificial neural network (ANN) architectures perform a constant amount of computation at inference time regardless of their inputs. This includes all recent GPT-style LLMs 1 and other transformer-based architectures. Whether you ask an LLM to complete the series “1, 2, 3, …”, or you ask it to solve a complicated logic riddle, the exact same amount of compute will be spent on predicting each token of its response. Intuitively, it should be clear that this constraint means at least one of the following: LLMs waste a significant amount of computation on simple tasks or, LLMs are unable to solve complex tasks due to being limited in their compute per token. In reality, a combination of both statements appears to be true depending on the prompt. One approach to overcome the constant-computation limitation of LLMs is “chain of thought” prompting ( Wei et al. 2022 ). By

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