technocracy

arXiv:2604.16950v1 Announce Type: new Abstract: Product attribute extraction in e-commerce is bottlenecked by ontologies that are inconsistent, incomplete, and costly to maintain. We present AutoPKG, a multi-agent Large Language Model (LLM) framework that automatically constructs a Product-attribute Knowledge Graph (PKG) from multimodal product content. AutoPKG induces product types and type-specific attribute keys on demand, extracts attribute values from text and images, and consolidates updates through a centralized decision agent that maintains a globally consistent canonical graph. We also […]

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Introduction: From IDEs to Agentic Command Centers :::info We have officially crossed the event horizon. ::: For decades, the Integrated Development Environment (IDE) was a passive canvas—a glorified typewriter that waited for your keystrokes. :::warning As of February 2026, with the maturity of Google Antigravity, the IDE is dead. ::: We have entered the era of the Agentic Command Center. Antigravity is not a “coding assistant.” :::tip To treat it as such is to buy a Ferrari and push […]

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arXiv:2402.06223v3 Announce Type: replace-cross Abstract: Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable. In practice, real-world multimodal datasets are often collected from heterogeneous generative processes that do not conform to a single DAG. Instead, they may involve multiple, and even opposing, DAG structures with inverse causal directions. To address this gap, in this work, we first propose a novel latent partial causal […]

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arXiv:2603.10091v1 Announce Type: new Abstract: The widespread adoption of thinking mode in large language models (LLMs) has significantly enhanced complex task processing capabilities while introducing new security risks. When subjected to jailbreak attacks, the step-by-step reasoning process may cause models to generate more detailed harmful content. We observe that thinking mode exhibits unique vulnerabilities when processing interleaved multiple tasks. Based on this observation, we propose multi-stream perturbation attack, which generates superimposed interference by interweaving multiple task streams within […]

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I want to fine tuned model with my own dataset so that later when user ask question so he/she able to get answer from provided document. So I am struggling with training model as I tried different models with full and lora fine tuning but accuracy of answer was not good. And there is problem to create jsonl file of Question- Answer pair which is used to fine tuned model. submitted by /u/Vidhi_Patel_8804 [link] [comments]

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arXiv:2601.09019v1 Announce Type: new Abstract: Hamiltonian Monte Carlo (HMC) algorithms are among the most widely used sampling methods in high dimensional settings, yet their convergence properties are poorly understood in divergences that quantify relative density mismatch, such as Kullback-Leibler (KL) and R’enyi divergences. These divergences naturally govern acceptance probabilities and warm-start requirements for Metropolis-adjusted Markov chains. In this work, we develop a framework for upgrading Wasserstein convergence guarantees for unadjusted Hamiltonian Monte Carlo (uHMC) to guarantees in tail-sensitive […]

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arXiv:2604.16714v1 Announce Type: cross Abstract: Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mixture models with negative coefficients, called subtractive mixture models (SMMs), have been proposed as a potentially more expressive alternative. However, how to effectively use SMMs for VI and IS is still an open question as they do not provide latent variable semantics and therefore cannot use sampling schemes for classical […]

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arXiv:2602.04004v1 Announce Type: new Abstract: Understanding dataset semantics is crucial for effective search, discovery, and integration pipelines. To this end, column type annotation (CTA) methods associate columns of tabular datasets with semantic types that accurately describe their contents, using pre-trained deep learning models or Large Language Models (LLMs). However, existing approaches require users to specify a closed set of semantic types either at training or inference time, hindering their application to domain-specific datasets where pre-defined labels often lack […]

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arXiv:2603.24774v1 Announce Type: new Abstract: This article discusses the challenges of testing software systems with increasingly integrated AI and LLM functionalities. LLMs are powerful but unreliable, and labeled ground truth for testing rarely scales. Metamorphic Testing solves this by turning relations among multiple test executions into executable test oracles.

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Event-Driven Webhooks are a push-based notification system that eliminates the need for inefficient polling.

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