technocracy

arXiv:2601.02574v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in applications requiring factual accuracy, yet their outputs often contain hallucinated responses. While fact-checking can mitigate these errors, existing methods typically retrieve external evidence indiscriminately, overlooking the model’s internal knowledge and potentially introducing irrelevant noise. Moreover, current systems lack targeted mechanisms to resolve specific uncertainties in the model’s reasoning. Inspired by how humans fact-check, we argue that LLMs should adaptively decide whether to rely on internal […]

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The deployment of reconfigurable intelligent surfaces (RISs) introduces new challenges for resource allocation in multi-cell wireless networks, particularly when user loads are uneven across base stations. In this work, we consider RISs as shared infrastructure that must be dynamically assigned among competing base stations, and we address this problem using a simultaneous ascending auction mechanism. To mitigate performance imbalances between cells, we propose a fairness-aware collaborative multi-agent reinforcement learning approach in which base stations adapt their bidding strategies […]

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In this edition of our Haskell in Production series, we feature NoRedInk – an EdTech product that helps students become better writers through its online, adaptive writing curriculum. The questions in the article were answered collaboratively by four representatives of NoRedInk – Blake Thomas, Christoph Hermann, Richard Feldmann, and Juliano Solanho. Read further to learn more about how they use Haskell at NoRedInk, why they decided to adopt it, and what is their approach to trying out new […]

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arXiv:1810.10987v5 Announce Type: replace-cross Abstract: In this paper we investigate panel regression models with interactive fixed effects. We propose two new estimation methods that are based on minimizing convex objective functions. The first method minimizes the sum of squared residuals with a nuclear (trace) norm regularization. The second method minimizes the nuclear norm of the residuals. We establish the consistency of the two resulting estimators. Those estimators have a very important computational advantage compared to the existing least […]

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When an open-source AI project gains 60,000 GitHub stars in 72 hours, triggers a trademark dispute, and becomes a $16 million crypto scam—something extraordinary is happening. The tech world witnessed one of the most dramatic rise-and-fall stories compressed into three weeks in January 2026. A developer built his perfect AI assistant, only to watch it spark legal threats, security warnings, and widespread scams. The assistant’s name changed three times—from Clawdbot to Moltbot to OpenClaw—but the vision remained consistent. […]

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arXiv:2604.17013v1 Announce Type: new Abstract: With the development of robotics, skeleton-based action recognition has become increasingly important, as human-robot interaction requires understanding the actions of humans and humanoid robots. Due to different sources of human skeletons and structures of humanoid robots, skeleton data naturally exhibit heterogeneity. However, previous works overlook the data heterogeneity of skeletons and solely construct models using homogeneous skeletons. Moreover, open-vocabulary action recognition is also essential for real-world applications. To this end, this work studies […]

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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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End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledge transfer across diverse scenarios, and spurious correlations between unobservable confounders and true driving intents. To address these issues, we propose DeLL, a Deconfounded Lifelong Learning framework that integrates a Dirichlet process mixture model (DPMM) with the front-door adjustment mechanism from causal inference. The DPMM is employed to construct two dynamic knowledge spaces: a trajectory knowledge space for clustering explicit driving behaviors and […]

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The Job Shop Scheduling Problem (JSSP) is commonly formulated as a disjunctive graph in which nodes represent operations and edges encode technological precedence constraints as well as machine-sharing conflicts. Most existing reinforcement learning approaches model this graph as homogeneous, merging job-precedence and machine-contention edges into a single relation type. Such a simplification overlooks the intrinsic heterogeneity of the problem structure and may lead to the loss of critical relational information. To address this limitation, we propose the Heterogeneous […]

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Tool: Merge State Visualizer Bram Cohen wrote about his coherent vision for the future of version control using CRDTs, illustrated by 470 lines of Python. I fed that Python (minus comments) into Claude and asked for an explanation, then had it use Pyodide to build me an interactive UI for seeing how the algorithms work. Tags: vcs, pyodide, bram-cohen, crdt

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