I Built a Tiny Computer Inside a Transformer
By compiling a simple program directly into transformer weights. Continue reading on Medium »
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Your AI Is Not Thinking. It's Multiplying Numbers. Let Me Show You Exactly How.
Everyone's talking about AI like it's magic. I work with it daily. It's not. Here's what's actually happening inside. I've fine-tuned LLMs. I've published research on them. I've built systems around them. And the single most honest thing I can tell you about large language models is this: At the bottom, it's matrix multiplication. That's it. Not intelligence. Not reasoning. Not understanding. Matrices of floating point numbers being multiplied together, billions of times per second. But here's the uncomfortable part. That doesn't mean nothing interesting is happening. Let me break this down without the hype, without the doomsaying, and without the marketing. What a "Model" Actually Is Forget the word "model." It carries too much baggage. What you're actually dealing with is a file. A very
Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
arXiv:2604.02659v1 Announce Type: cross Abstract: The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular value decomposition (SVD) provides a principled approach for model reduction, but its exact computation is expensive for large weight matrices. Randomized alternatives such as randomized SVD (RSVD) improve efficiency, yet they can suffer from poor approximation quality when the singular value spectrum decays slowly, a regime commonly observed in modern pretrained models. In this work, we address this limitation from both theoretical and empirical perspectives. First, we establish a connection between low-rank approximation error and predictive performance by analyzing softmax perturbations, s
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling
arXiv:2112.07874v2 Announce Type: cross Abstract: We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an ensemble setup consisting of a pretrained Transformer and ground-truth graphs from one of 7 different formalisms, we find that, overall, semantic constituency structures are most useful to language modeling performance -- outpacing syntactic constituency structures as well as syntactic and semantic dependency structures. Further, effects vary greatly depending on part-of-speech class. In sum, our findings point to promising tendencies in neuro-symbolic language modeling and invite future research quantifying the design choices made by different formalisms.
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Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
arXiv:2604.02821v1 Announce Type: new Abstract: This paper presents a learning-based approach for all-pairs motion planning, where the initial and goal states are allowed to be arbitrary points in a safe set. We construct smooth goal-conditioned neural ordinary differential equations (neural ODEs) via bi-Lipschitz diffeomorphisms. Theoretical results show that the proposed model can provide guarantees of global exponential stability and safety (safe set forward invariance) regardless of goal location. Moreover, explicit bounds on convergence rate, tracking error, and vector field magnitude are established. Our approach admits a tractable learning implementation using bi-Lipschitz neural networks and can incorporate demonstration data. We illustrate the effectiveness of the proposed method
Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision
arXiv:2604.02812v1 Announce Type: new Abstract: Vision-language models (VLMs) have recently demonstrated strong capabilities in mapping multimodal observations to robot behaviors. However, most current approaches rely on end-to-end visuomotor policies that remain opaque and difficult to analyze, limiting their use in safety-critical robotic applications. In contrast, classical robotic systems often rely on structured policy representations that provide interpretability, modularity, and reactive execution. This work investigates how foundation models can be specialized to generate structured robot policies grounded in multimodal perception, bridging high-dimensional learning and symbolic control. We propose a neuro-symbolic approach in which a VLM synthesizes executable Behavior Tree polici
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