Introduction: The Reality of the Build: A Post-Mortem We were building a multi-lens introspection system designed to take a user’s inner state and translate it into structured insights. At a certain point in development, the system just stopped producing outputs. It didn’t crash—but it started timing out, again and again. And when it didn’t time out, it went silent: no insights, no partial responses, no recoverable errors. Just empty. We treated it as an engineering problem. We audited […]

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‘’Marketing Doesn’t Work’’ -Sam Ovens, in a Marketing Video from 2018 It might seem like a great idea to charge between 1,000 – 10,000 dollars for info that is already available for free online to everyone. (Yes, I’ve bought a few of these – some of them yummy, some less so…) You just package it nicely and feed it to people who think they need what you promise & are too lazy to even complete the course after […]

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Large language models (LLMs) are increasingly deployed as automated evaluators that assign numeric scores to model outputs, a paradigm known as LLM-as-a-Judge. However, standard Reinforcement Learning (RL) methods typically rely on binary rewards (e.g., 0-1 accuracy), thereby ignoring the ordinal structure inherent in regression tasks; for instance, they fail to recognize that predicting 4 is significantly better than predicting 1 when the ground truth is 5. Conversely, existing regression-aware approaches are often confined to Supervised Fine-Tuning (SFT), limiting […]

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arXiv:2602.02626v1 Announce Type: cross Abstract: Adversarial training attains strong empirical robustness to specific adversarial attacks by training on concrete adversarial perturbations, but it produces neural networks that are not amenable to strong robustness certificates through neural network verification. On the other hand, earlier certified training schemes directly train on bounds from network relaxations to obtain models that are certifiably robust, but display sub-par standard performance. Recent work has shown that state-of-the-art trade-offs between certified robustness and standard performance […]

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Teilhard depicts humanity as the Earth awakening to itself as evolution rises toward ever-greater unity. Love and consciousness converge into a planetary spirit, but this ascent, he insists, cannot endure without religion—our innate need for an absolute that calls us forward. In the universe’s unfolding, God emerges as evolution’s necessary summit and sustaining fire.

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Manual observation and monitoring of individual cows for disease detection present significant challenges in large-scale farming operations, as the process is labor-intensive, time-consuming, and prone to reduced accuracy. The reliance on human observation often leads to delays in identifying symptoms, as the sheer number of animals can hinder timely attention to each cow. Consequently, the accuracy and precision of disease detection are significantly compromised, potentially affecting animal health and overall farm productivity. Furthermore, organizing and managing human resources […]

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Questions for developers: “What’s the one area you’re afraid to touch?” “When’s the last time you deployed on a Friday?” “What broke in production in the last 90 days that wasn’t caught by tests?” Questions for the CTO/EM: “What feature has been blocked for over a year?” “Do you have real-time error visibility right now?” “What was the last feature that took significantly longer than estimated?” Questions for business stakeholders: “Are there features that got quietly turned off […]

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arXiv:2602.03514v2 Announce Type: replace-cross Abstract: Modern learning systems often interpolate training data while still generalizing well, yet it remains unclear when algorithmic stability explains this behavior. We model training as a function-space trajectory and measure sensitivity to single-sample perturbations along this trajectory. We propose a contractive propagation condition and a stability certificate obtained by unrolling the resulting recursion. A small certificate implies stability-based generalization, while we also prove that there exist interpolating regimes with small risk where such […]

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Proximal constraints are fundamental to the stability of the Large Language Model reinforcement learning. While the canonical clipping mechanism in PPO serves as an efficient surrogate for trust regions, we identify a critical bottleneck: fixed bounds strictly constrain the upward update margin of low-probability actions, disproportionately suppressing high-advantage tail strategies and inducing rapid entropy collapse. To address this, we introduce Band-constrained Policy Optimization (BandPO). BandPO replaces canonical clipping with Band, a unified theoretical operator that projects trust regions […]

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arXiv:2602.09234v1 Announce Type: new Abstract: Continual learning has become a trending topic in machine learning. Recent studies have discovered an interesting phenomenon called loss of plasticity, referring to neural networks gradually losing the ability to learn new tasks. However, existing plasticity research largely relies on contrived settings with abrupt task transitions, which often do not reflect real-world environments. In this paper, we propose to investigate a gradually changing environment, and we simulate this by input/output interpolation and task […]

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