technocracy

Next-Generation Reservoir Computing for Dynamical Inference

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arXiv:2509.11338v3 Announce Type: replace Abstract: We present a simple and scalable implementation of next-generation reservoir computing (NGRC) for modeling dynamical systems from time-series data. The method uses a pseudorandom nonlinear projection of time-delay embedded inputs, allowing the feature-space dimension to be chosen independently of the observation size and offering a flexible alternative to polynomial-based NGRC projections. We demonstrate the approach on benchmark tasks, including attractor reconstruction and bifurcation diagram estimation, using partial and noisy measurements. We further show […]

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How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost

How TabPFN Leverages In-Context Learning to Achieve Superior Accuracy on Tabular Datasets Compared to Random Forest and CatBoost

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Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over the years, models based on decision trees, such as Random Forest, XGBoost, and CatBoost, have become the default choice for these tasks. Their strength lies in handling mixed data types, capturing complex feature interactions, and delivering strong performance without heavy preprocessing. While deep learning has transformed areas like computer vision and natural language processing, […]

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The Autonomy Trap: Why Most Organisations struggle at AI Integration (And How to Actually Ship…

The Autonomy Trap: Why Most Organisations struggle at AI Integration (And How to Actually Ship…

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The Autonomy Trap: Why Most Organisations struggle at AI Integration (And How to Actually Ship Reliable AI Agents) — An Engineer’s guide From writing deterministic programs towards probabilistic approaches From Deterministic to Probabilistic: Navigating the Agentic Shift Only a few years ago, predictability and determinism were the bedrocks of the software industry. We designed systems to produce the exact same output regardless of how many times a piece of code was executed. This reliability allowed engineers to ship products with confidence; […]

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The Fifth Graph Normal Form (5GNF): A Trait-Based Framework for Metadata Normalization in Property Graphs

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arXiv:2603.06703v1 Announce Type: new Abstract: Graph databases are widely used in systems that manage rich metadata, yet current modelling practices often embed descriptive attributes directly in nodes, leading to redundancy and inconsistent semantics. This paper introduces the Fifth Graph Normal Form (5GNF), a trait-based normalization framework for property graphs that represents recurring metadata as canonical Trait Nodes connected through HAS_TRAIT relationships. We formalize trait functional dependencies (tFDs) and present the TraitExtraction5GNF algorithm for identifying and extracting reusable traits. […]

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A signal separation view of classification

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arXiv:2509.24140v2 Announce Type: replace-cross Abstract: The problem of classification in machine learning has often been approached in terms of function approximation. In this paper, we propose an alternative approach for classification in arbitrary compact metric spaces which, in theory, yields both the number of classes, and a perfect classification using a minimal number of queried labels. Our approach uses localized trigonometric polynomial kernels initially developed for the point source signal separation problem in signal processing. Rather than point […]

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Why Human.tech Just Killed the Crypto Wallet (And What It Built Instead)

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What happens when an AI agent can trade your portfolio, pay for API access, and deploy smart contracts, but there is no guaranteed mechanism ensuring you remain in charge? That question has moved from philosophical to urgent. Daily active on-chain AI agents crossed 250,000 in early 2026, showing more than 400 percent growth compared to 2025, and more than 68 percent of new DeFi protocols launched in the first quarter of 2026 included at least one autonomous AI […]

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Living With the Lethal Trifecta: A Guide to Personal AI Agent Security

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I gave my OpenClaw AI agent the name Aris, access to my health data, family Telegram chat, calendar, and GitHub. OpenClaw is an open-source agent framework for building and running personal AI assistants that can interact with various apps and data sources. Simon Willison would call this insane, and he is probably right. Here’s what a Tuesday morning looks like. At 7:30, Aris sends my morning briefing: sleep score from Apple Watch, resting heart rate trending up, recovery […]

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Part 1: Instruction Fine-Tuning: Fundamentals, Architecture Modifications, and Loss Functions

Part 1: Instruction Fine-Tuning: Fundamentals, Architecture Modifications, and Loss Functions

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TL;DR Instruction fine-tuning (IFT) refines pre-trained large language models (LLMs) to follow specific task instructions by training on prompt-response pairs. At the core of IFT is a dual-objective loss function that balances instruction-following with general language modeling capabilities. Each IFT training sample consists of a task, a context, and a target response. Datasets can be augmented through automated approaches to increase task diversity and difficulty. Modifications to an LLM’s input layer, attention mechanism, and output layer improve instruction-following […]

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Created a dataset system for training real LLM behaviors (not just prompts

Created a dataset system for training real LLM behaviors (not just prompts

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Most LLM dataset discussions still revolve around size, coverage, or “high-quality text,” but in practice the real failure mode shows up later when you actually plug models into workflows. Things like: tool calls breaking structured outputs drifting multi-step reasoning collapsing models losing grounding over longer runs We ran into this repeatedly while building LLM systems, and it became pretty clear that the issue wasn’t just model capability, it was how the data was structured. That’s what led us […]

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Scaling up image segmentation across data and tasks

Scaling up image segmentation across data and tasks

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Scaling up image segmentation across data and tasks Novel architecture that fuses learnable queries and conditional queries improves a segmentation models ability to transfer across tasks. Computer vision Pei Wang Stefano Soatto June 12, 12:25 PM June 12, 12:25 PM The first draft of this blog post was generated by Amazon Nova Pro, based on detailed instructions from Amazon Science editors and multiple examples of prior posts. In a paper we’re presenting at the 2025 Conference on Computer […]

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