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A new preprint describes an influence-function method intended to identify which training samples shape predictions and estimate spectral errors when ground-truth data is unavailable during space-mission operations.
Transport Topics reports that McLeod Software and Augment have connected Augie, an AI assistant, with McLeod’s PowerBroker system to automate carrier communications, compliance checks and shipment updates.
Researchers propose DELTA, an attention-based encoder that lets a quadruped robot focus on terrain evidence relevant to foot placement while keeping computation fixed as map resolution increases.
A new arXiv paper proposes samplers for discrete diffusion models whose required discretization steps depend on the target distribution’s internal dependence rather than directly on its dimension.
The Japan Times reports that Japanese and Japan-linked firms are using AI, sensors and automation to improve indoor farming as the country’s agricultural workforce shrinks, while high capital and electricity costs continue to threaten profitability.
A new arXiv preprint introduces ConvergeFlow, a flow-based language model designed to end at valid token embeddings without a cross-entropy-trained decoder. The authors prove convergence under stated regularity conditions and report competitive results on OpenWebText, but the abstract does not provide benchmark…
Tom’s Guide reports that Pennsylvania has barred state agencies from signing nondisclosure agreements with data-center developers and removed AI data centers from a fast-track permitting program. The move follows growing concern that communities may not know who is behind projects or how much electricity, water and…
A new arXiv preprint proposes training sparse local linear models around individual predictions while retaining much of a black-box AI system’s accuracy.
A new arXiv preprint presents a mathematical framework for analyzing how feature geometries align inside deep neural networks. Its experiments suggest that alignment depends on layer-specific transport, interactions and cancellation rather than following automatically from training or lower risk.
A Deloitte survey reported by ZDNET found that only 15% of surveyed U.S. organizations had scaled, orchestrated multi-agent deployments. The findings point to data, governance, integration, process redesign and workforce readiness as the main barriers.
A new preprint proposes an ensemble-based approach that measures how strongly language models commit to answers, reporting competitive calibration and hallucination-detection results across three open models and four question-answering datasets.
An arXiv preprint reports that a stochastic knowledge-graph method made three LLMs produce more distinguishable simulated students across 379 SAT Algebra items.
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