February 2026

The sharper the mind, the more elaborate the justification for staying wrong. Depth of thought becomes the mechanism of loss when it serves identity instead of truth. There is a version of intelligence that protects you in markets. It reads conditions, adjusts frameworks, and knows when to step aside. Then there is another version, far more common, that does the opposite. It builds elaborate cases for positions that should have been closed days ago. The distinction matters more […]

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I keep hearing the same question at every engineering offsite, Slack thread, and investor pitch: “What’s the best model right now — GPT, Claude, or Gemini?” I spent the last several months building and debugging agent-based systems, and I think this is the wrong question entirely. The evidence is now overwhelming: what determines whether an AI agent succeeds in production is not the model underneath it, but the infrastructure wrapped around it. I am going to lay out my hypothesis, test […]

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Table of Links Abstract Introduction Related work Problem statement [Methods]() Experiments Limitations [Conclusion]() RELATED WORK A. Mobile Robot Mapping Early robotic scene mapping research focused on the development of the core competencies in the metric [20], [21], [22] and topological [23], [24] knowledge spaces, extensively centered around the question of map and knowledge representation. For successful task execution, data-rich 3D scene representation and self-localization are critical, enabled by Simultaneous Localization and Mapping (SLAM) algorithms [25], [26], [27]. 3D […]

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arXiv:2602.18958v2 Announce Type: replace-cross Abstract: We propose an optimal algorithm for estimating conditional average treatment effects (CATEs) when response functions lie in a reproducing kernel Hilbert space (RKHS). We study settings in which the contrast function is structurally simpler than the nuisance functions: (i) it lies in a lower-complexity RKHS with faster eigenvalue decay, (ii) it satisfies a source condition relative to the nuisance kernel, or (iii) it depends on a known low-dimensional covariate representation. We develop a […]

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arXiv:2602.17554v2 Announce Type: replace-cross Abstract: Training large-scale generative models is resource-intensive and relies heavily on heuristic dataset weighting. We address two fundamental questions: Can we train Large Language Models (LLMs) modularly-combining small, domain-specific experts to match monolithic performance-and can we do so robustly for any data mixture, eliminating heuristic tuning? We present a theoretical framework for modular generative modeling where a set of pre-trained experts are combined via a gating mechanism. We define the space of normalized gating […]

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arXiv:2510.18114v2 Announce Type: replace-cross Abstract: Discrete diffusion models have emerged as a powerful class of models and a promising route to fast language generation, but practical implementations typically rely on factored reverse transitions that ignore cross-token dependencies and degrade performance in the few-step regime. We propose Latent-Augmented Discrete Diffusion (LADD), which introduces a learnable auxiliary latent channel and performs diffusion over the joint (token, latent) space. The latent variables provide an intermediate representation that can express joint structure […]

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arXiv:2507.04448v2 Announce Type: replace-cross Abstract: We develop a theory of transfer learning in infinitely wide neural networks under gradient flow that quantifies when pretraining on a source task improves generalization on a target task. We analyze both (i) fine-tuning, when the downstream predictor is trained on top of source-induced features and (ii) a jointly rich setting, where both pretraining and downstream tasks can operate in a feature learning regime, but the downstream model is initialized with the features […]

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arXiv:2505.11602v2 Announce Type: replace-cross Abstract: Deep selective State-Space Models (SSMs), whose state-space parameters are modulated online by a selection signal, offer significant expressive power but pose challenges for stability analysis, especially under discontinuous gating. We study continuous-time selective SSMs through the lenses of passivity and Input-to-State Stability (ISS), explicitly distinguishing the selection schedule $x(cdot)$ from the driving (port) input $u(cdot)$. First, we show that state-strict dissipativity ($beta>0$) together with quadratic bounds on a storage functional implies exponential decay […]

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