technocracy

I have been going through GRPO and PPO today, and from what I understood is that the success heavily depeneded on the implementaiton details and the engineering quirks rather than the algorithm’s theoretical ground. As such I want to ask a question on how the industrial research in RL proceeds, is it majorly empirical results focused, or a flexible technique with decent theoretical rigor and engineering optimization? submitted by /u/Extension-Economy-78 [link] [comments]

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arXiv:2602.12485v1 Announce Type: new Abstract: The success of businesses depends on their ability to convert consumers into loyal customers. A customer’s value proposition is a primary determinant in this process, requiring a balance between affordability and long-term brand equity. Broad marketing campaigns can erode perceived brand value and reduce return on investment, while existing economic algorithms often misidentify highly engaged customers as ideal targets, leading to inefficient engagement and conversion outcomes. This work introduces a two-stage multi-model architecture […]

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arXiv:2602.16810v1 Announce Type: new Abstract: Unlike in TFNP, for which there is an abundance of problems capturing natural existence principles which are incomparable (in the black-box setting), Kleinberg et al. [KKMP21] observed that many of the natural problems considered so far in the second level of the total function polynomial hierarchy (TF$Sigma_2$) reduce to the Strong Avoid problem. In this work, we prove that the Linear Ordering Principle does not reduce to Strong Avoid in the black-box setting, […]

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Six months ago I started building an analytics platform for AI brand visibility. The problem I was solving: when a potential customer asks ChatGPT or Perplexity “what’s the best X for Y,” how does the model decide what to recommend? And can brands influence that? Building the product meant designing a pipeline that runs buyer-intent queries across ChatGPT, Claude, Gemini, Perplexity, and Grok at scale. Thousands of queries, dozens of product categories, multiple languages. Every week, new data. […]

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arXiv:2604.13069v1 Announce Type: new Abstract: Artificial intelligence (AI) control protocols assume that trusted large language model (LLM) monitors reliably assess proposed actions across all deployment contexts. This paper tests that assumption in the geographic dimension. We audit Claude Opus 4.6-the monitor specified in Apart Research’s AI Control Hackathon Track 3 benchmark-for systematic gaps in its factual knowledge of the global AI landscape. We develop the AI Control Knowledge Framework (ACKF), a six-dimension thematic scheme, and operationalise it with […]

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Training frontier AI models is, at its core, a coordination problem. Thousands of chips must communicate with each other continuously, synchronizing every gradient update across the network. When one chip fails or even slows down, the entire training run can stall. As models scale toward hundreds of billions of parameters, that fragility becomes increasingly untenable. Google DeepMind is now proposing a different model entirely. Google DeepMind researchers introduced Decoupled DiLoCo (Distributed Low-Communication), a distributed training architecture that decouples […]

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Persistent homology (PH) encodes global information, such as cycles, and is thus increasingly integrated into graph neural networks (GNNs). PH methods in GNNs typically traverse an increasing sequence of subgraphs. In this work, we first expose limitations of this inclusion procedure. To remedy these shortcomings, we analyze contractions as a principled topological operation, in particular, for graph representation learning. We study the persistence of contraction sequences, which we call Contraction Homology (CH). We establish that forward PH and […]

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arXiv:2601.02378v1 Announce Type: new Abstract: As the application of Embodied AI Agents in avatars, wearable devices, and robotic systems continues to deepen, their core research challenges have gradually shifted from physical environment interaction to the accurate understanding of social interactions. Traditional physical world models (PWM) focus on quantifiable physical attributes such as space and motion, failing to meet the needs of social intelligence modeling. In contrast, the Mental World Model (MWM), as a structured representation of humans’ internal […]

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