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#industrial

Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation

Hugging Face Daily Papers · 2026-06-10 Cached

This paper introduces a lightweight approach for remaining useful life estimation using frozen embeddings from the Chronos-2 time-series foundation model combined with a simple regression head, achieving superior performance on industrial sensor data compared to baseline methods.

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#industrial

Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes

Hugging Face Daily Papers · 2026-05-28 Cached

This review reframes industrial visual sim-to-real as a domain-gap problem organized by prior availability, distinguishing CAD-guided, CAD-unavailable, and boundary-prior settings to connect CAD-based detection and 6D pose-estimation literature with industrial anomaly and surface-inspection literature.

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#industrial

How do you handle firmware updates for AI models on devices deployed in places with no reliable connectivity, do you wait for a technician visit or accept the model staying stale?

Reddit r/AI_Agents · 2026-05-19

A detailed examination of the real-world challenges faced when updating AI models on edge devices deployed in remote or disconnected environments, covering strategies like connectivity windows, technician visits, mesh propagation, and accepting staleness.

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#industrial

SPIN: Structural LLM Planning via Iterative Navigation for Industrial Tasks

Hugging Face Daily Papers · 2026-05-13 Cached

SPIN is a planning wrapper that ensures structurally valid DAG plans and uses prefix-based execution control to reduce task steps and tool calls in industrial LLM agent systems, improving plan validity and efficiency.

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#industrial

Cortex 2.0: Grounding World Models in Real-World Industrial Deployment

Hugging Face Daily Papers · 2026-04-22 Cached

Cortex 2.0 introduces a plan-and-act control framework that uses visual latent space trajectory generation to enable reliable long-horizon robotic manipulation in complex industrial environments, outperforming reactive Vision-Language-Action models.

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