technocracy

Here we go again: you see the notification, and you feel that familiar resistance. Code Review often feels cognitively heavy and non-deterministic: too many paths to follow, too much ambiguity, too many opinions. That uncertainty breeds procrastination and bikeshedding, or, at the other extreme, drive-by approvals. But how do you maximize the value of a review without exhausting the reviewers or trapping the team in endless debates? I’ve distilled a healthy, sustainable review process into an acronym: PERFECT. […]

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arXiv:2601.00125v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit persistent logical failures in complex reasoning due to the lack of an internal axiomatic framework. We propose Mathesis, a neuro-symbolic architecture that encodes mathematical states as higher-order hypergraphs and uses a Symbolic Reasoning Kernel (SRK)–a differentiable logic engine that maps constraints to a continuous energy landscape. By defining a global energy function E(G), where zero energy implies logical consistency, the SRK yields gradient-based signals to train a Hypergraph […]

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arXiv:2601.05487v1 Announce Type: new Abstract: Data-driven reports communicate decision-relevant insights by tightly interleaving narrative text with charts grounded in underlying tables. However, current LLM-based systems typically generate narratives and visualizations in staged pipelines, following either a text-first-graph-second or a graph-first-text-second paradigm. These designs often lead to chart-text inconsistency and insight freezing, where the intermediate evidence space becomes fixed and the model can no longer retrieve or construct new visual evidence as the narrative evolves, resulting in shallow and […]

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arXiv:2601.04361v1 Announce Type: new Abstract: We tackle a common domain adaptation setting in causal systems. In this setting, the target variable is observed in the source domain but is entirely missing in the target domain. We aim to impute the target variable in the target domain from the remaining observed variables under various shifts. We frame this as learning a compact, mechanism-stable representation. This representation preserves information relevant for predicting the target while discarding spurious variation. For linear […]

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arXiv:2601.08896v1 Announce Type: new Abstract: This study develops a robust machine learning framework for one-step-ahead forecasting of daily log-returns in the Nepal Stock Exchange (NEPSE) Index using the XGBoost regressor. A comprehensive feature set is engineered, including lagged log-returns (up to 30 days) and established technical indicators such as short- and medium-term rolling volatility measures and the 14-period Relative Strength Index. Hyperparameter optimization is performed using Optuna with time-series cross-validation on the initial training segment. Out-of-sample performance is […]

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TL;DR I tested 112 Product Hunt startups with 2,240 queries across ChatGPT and Perplexity. The results challenge conventional wisdom about “Generative Engine Optimization” (GEO): The Discovery Gap: ChatGPT recognizes 99% of products directly but recommends only 3% organically (30:1 ratio) GEO doesn’t work (yet): Zero correlation between GEO optimization and ChatGPT discovery Traditional SEO wins: Backlinks (r=+0.32) and Reddit presence (r=+0.40) are the strongest predictors Full paper: arXiv:2601.00912 n Code & Data:GitHub The Problem: ChatGPT Visibility for Startups […]

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arXiv:2601.02530v1 Announce Type: new Abstract: We present Connection-Aware Motif Sequencing (CamS), a graph-to-sequence representation that enables decoder-only Transformers to learn molecular graphs via standard next-token prediction (NTP). For molecular property prediction, SMILES-based NTP scales well but lacks explicit topology, whereas graph-native masked modeling captures connectivity but risks disrupting the pivotal chemical details (e.g., activity cliffs). CamS bridges this gap by serializing molecular graphs into structure-rich causal sequences. CamS first mines data-driven connection-aware motifs. It then serializes motifs via […]

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In this interview, we speak with two representatives from Quantori, a data science and digital transformation company specializing in research and development in the biopharma industry. Yuriy Gankin is a co-founder and chief scientific officer at Quantori. He’s a life science researcher, serial entrepreneur, and inventor and holds a Ph.D. in analytical chemistry from Tufts University and an MBA from MIT Sloan. Maksim Kazanskii is Quantori’s engineering manager in machine learning. With a background in theoretical physics and […]

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