technocracy

In this tutorial, we build a complete Open WebUI setup in Colab, in a practical, hands-on way, using Python. We begin by installing the required dependencies, then securely provide our OpenAI API key through terminal-based secret input so that sensitive credentials are not exposed directly in the notebook. From there, we configure the environment variables needed for Open WebUI to communicate with the OpenAI API, define a default model, prepare a data directory for runtime storage, and launch […]

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After 20 dead versions and about 2 month of work, my RL agent (NASMU) passed its walk-forward backtest across 2020–2026. But the most interesting part wasn’t the results — it was what the model actually learned. The setup: – PPO + xLSTM (4 blocks), BTC/USDT 4h bars – 35 features distilled from López de Prado, Hilpisch, Kaabar, Chan and others – Triple Barrier labeling (TP/SL/Timeout) – HMM for regime detection (bull/bear/sideways) – Running on a Xeon E5-1650 v2 […]

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arXiv:2604.22755v1 Announce Type: new Abstract: Reliable decision support in nuclear engineering requires traceable, domain-grounded knowledge retrieval, yet safety and risk analysis workflows remain hampered by fragmented documentation and hallucination when use pre-trained large language model (LLM) in specialized nuclear domains. To address these challenges, this paper presents RADIANT-LLM (Retrival-Augumented, Domain-Intelligent Agent for Nuclear Technologies using LLM), a multi-modal retrieval-augmented generation (RAG) framework designed for nuclear safety, security, and safeguards applications. The framework uses a local-first, model-agnostic architecture that […]

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We propose Hamiltonian-SMT, the first MARL framework to replace “guess-and-check” evolution with verified Policy Impulses. By modeling the population as a discrete Hamiltonian system, we enforce physical and logical conservation laws: System Energy (E): Formally represents Social Welfare (Global Reward). Momentum (P): Formally represents Behavioral Diversity. Impulse (∆W): A weight update verified by Lean 4 to be Lipschitz-continuous and energy-preserving. submitted by /u/Regular_Run3923 [link] [comments]

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:::info Authors: Dani Valevski Yaniv Leviathan Moab Arar Shlomi Fruchter ::: Table of Links ABSTRACT 1 INTRODUCTION 2 INTERACTIVE WORLD SIMULATION 3 GAMENGEN 3.1 DATA COLLECTION VIA AGENT PLAY 3.2 TRAINING THE GENERATIVE DIFFUSION MODEL 4 EXPERIMENTAL SETUP 4.1 AGENT TRAINING 4.2 GENERATIVE MODEL TRAINING 5 RESULTS 5.1 SIMULATION QUALITY 5.2 ABLATIONS 6 RELATED WORK 7 DISCUSSION, ACKNOWLEDGEMENTS AND REFERENCES ABSTRACT We present GameNGen, the first game engine powered entirely by a neural model that also enables real-time […]

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World models learn to simulate environment dynamics from experience, enabling sample-efficient reinforcement learning. But what do these models actually represent internally? We apply interpretability techniques–including linear and nonlinear probing, causal interventions, and attention analysis–to two architecturally distinct world models: IRIS (discrete token transformer) and DIAMOND (continuous diffusion UNet), trained on Atari Breakout and Pong. Using linear probes, we find that both models develop linearly decodable representations of game state variables (object positions, scores), with MLP probes yielding only […]

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arXiv:2410.06378v2 Announce Type: replace Abstract: Covering numbers of (deep) ReLU networks have been used to characterize approximation-theoretic performance, to upper-bound prediction error in nonparametric regression, and to quantify classification capacity. These results rely on covering number upper bounds obtained via explicit constructions of coverings. Lower bounds on covering numbers do not appear to be available in the literature. The present paper fills this gap by deriving tight (up to multiplicative constants) lower and upper bounds on the metric […]

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When you type a message to Claude, something invisible happens in the middle. The words you send get converted into long lists of numbers called activations that the model uses to process context and generate a response. These activations are, in effect, where the model’s “thinking” lives. The problem is nobody can easily read them. Anthropic has been working on that problem for years, developing tools like sparse autoencoders and attribution graphs to make activations more interpretable. But […]

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arXiv:2602.15139v1 Announce Type: new Abstract: Accurate QA over classical Islamic texts remains challenging due to domain specific semantics, long context dependencies, and concept sensitive reasoning. Therefore, a new CGRA DeBERTa, a concept guided residual domain augmentation transformer framework, is proposed that enhances theological QA over Hadith corpora. The CGRA DeBERTa builds on a customized DeBERTa transformer backbone with lightweight LoRA based adaptations and a residual concept aware gating mechanism. The customized DeBERTa embedding block learns global and positional […]

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arXiv:2601.12208v1 Announce Type: new Abstract: Evaluating conversational systems in multi-turn settings remains a fundamental challenge. Conventional pipelines typically rely on manually defined rubrics and fixed conversational context$-$a static approach that limits coverage and fails to capture the diverse, emergent behaviors of dialogue models. To address this, we introduce CoReflect (Conversational Evaluation via Co-Evolutionary Simulation and Reflective Rubric Refinement), which unifies dialogue simulation and evaluation into an adaptive, iterative process. CoReflect employs a conversation planner that generates structured templates […]

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