technocracy

:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter IX Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter IX By Oscar Wilde ::: As he was sitting at breakfast next morning, Basil Hallward was shown into the room. “I am so glad I have found you, Dorian,” he […]

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arXiv:2602.10131v1 Announce Type: new Abstract: I present a descriptive analysis of Moltbook, a social platform populated exclusively by AI agents, using data from the platform’s first 3.5 days (6{,}159 agents; 13{,}875 posts; 115{,}031 comments). At the macro level, Moltbook exhibits structural signatures that are familiar from human social networks but not specific to them: heavy-tailed participation (power-law exponent $alpha = 1.70$) and small-world connectivity (average path length $=2.91$). At the micro level, patterns appear distinctly non-human. Conversations are […]

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arXiv:2604.08719v1 Announce Type: new Abstract: Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck for large-scale deployment. To address this challenge, some works use LLMs and VLMs for vision-language understanding and reasoning, enabling vehicles to interpret rare and safety-critical situations when generating actions. Others study generative world models to capture the spatio-temporal evolution of driving scenes, allowing agents to imagine possible futures before acting. Inspired by human […]

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Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advances encompass a wide range of conceptual, mathematical, and computational ideas, with applications for model learning and training, memory retrieval, data-driven control, and optimization. This tutorial focuses on neuro-inspired approaches to computation that aim to improve scalability, robustness, and energy efficiency across such tasks, bridging the gap between artificial and biological systems. Particular emphasis is […]

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The orthodoxy that product managers should “stay strategic” and leave infrastructure to engineering is one of the most damaging beliefs in modern product development. PMs who understand and own infrastructure decisions ship faster, build better products, and make fewer costly mistakes. PMs with technical depth generally ship well rounded features than “strategy only” counterparts which means the gap between knowing your system and not knowing it shows up directly in your feature quality and your user experience.

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arXiv:2604.23212v1 Announce Type: new Abstract: Existing large-dimensional theory for spectral algorithms resolves either the optimally tuned point or the interpolation limit, but leaves the under-regularized regime unexplored. We study the learning curve and benign overfitting of spectral algorithms in the large-dimensional setting where the sample size and dimension are of comparable order, i.e., $n asymp d^{gamma}$ for some $gamma>0$. We first consider inner-product kernels on the sphere $mathbb{S}^{d-1}$ and establish a sharp asymptotic characterization of the excess risk […]

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arXiv:2603.03323v1 Announce Type: new Abstract: Large language models (LLMs) aligned for safety often suffer from over-refusal, the tendency to reject seemingly toxic or benign prompts by misclassifying them as toxic. This behavior undermines models’ helpfulness and restricts usability in sensitive or nuanced contexts. While prior work has proposed mitigation strategies such as data augmentation and activation steering, these approaches often face a trade-off: reducing over-refusal typically degrades the model’s ability to reject genuinely harmful content. We argue that […]

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Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problems. This sensitivity stems from the non-stationarity of RL: rollouts are generated by an evolving policy, and learning is shaped by exploration and reward feedback, unlike supervised fine-tuning (SFT) with fixed trajectories. As a result, prior work often relies on manual curation or simple heuristic filters (e.g., accuracy), which can admit incorrect or […]

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