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business#agent🔬 ResearchAnalyzed: Jan 20, 2026 16:31

ERP Reimagined: Ushering in the Agentic AI Era for Business

Published:Jan 20, 2026 16:14
1 min read
MIT Tech Review

Analysis

This article explores the exciting evolution of Enterprise Resource Planning (ERP) systems, highlighting their adaptation to the latest technological advancements. It's a fantastic look at how businesses are constantly refining their operations to leverage the power of new technologies and drive efficiency! We're on the cusp of truly transformative changes.
Reference

“The story of enterprise resource planning (ERP) is really a story of businesses learning to organize themselves around the latest, greatest technology of the times.”

PermalinkMIT Tech Review
product#llm📝 BlogAnalyzed: Jan 18, 2026 07:30

Claude Code v2.1.12: Smooth Sailing with Bug Fixes!

Published:Jan 18, 2026 07:16
1 min read
Qiita AI

Analysis

The latest Claude Code update, version 2.1.12, is here! This release focuses on crucial bug fixes, ensuring a more polished and reliable user experience. We're excited to see Claude Code continually improving!
Reference

“"Fixed message rendering bug"”

PermalinkQiita AI
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:10

Learning continually with representational drift

Published:Dec 26, 2025 14:48
1 min read
ArXiv

Analysis

This article likely discusses a research paper on continual learning in the context of AI, specifically focusing on how representational drift impacts the performance of learning models over time. The focus is on addressing the challenges of maintaining performance as models are exposed to new data and tasks.

Key Takeaways

    Reference

    “”

    PermalinkArXiv
    Safety#LLM🔬 ResearchAnalyzed: Jan 10, 2026 11:46

    Persistent Backdoor Threats in Continually Fine-Tuned LLMs

    Published:Dec 12, 2025 11:40
    1 min read
    ArXiv

    Analysis

    This ArXiv paper highlights a critical vulnerability in Large Language Models (LLMs). The research focuses on the persistence of backdoor attacks even with continual fine-tuning, emphasizing the need for robust defense mechanisms.
    Reference

    “The paper likely discusses vulnerabilities in LLMs related to backdoor attacks and continual fine-tuning.”

    PermalinkArXiv
    Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:31

    Unforgotten Safety: Preserving Safety Alignment of Large Language Models with Continual Learning

    Published:Dec 10, 2025 23:16
    1 min read
    ArXiv

    Analysis

    This article from ArXiv focuses on the critical challenge of maintaining safety alignment in Large Language Models (LLMs) as they are continually updated and improved through continual learning. The core issue is preventing the model from 'forgetting' or degrading its safety protocols over time. The research likely explores methods to ensure that new training data doesn't compromise the existing safety guardrails. The use of 'continual learning' suggests the study investigates techniques to allow the model to learn new information without catastrophic forgetting of previous safety constraints. This is a crucial area of research as LLMs become more prevalent and complex.
    Reference

    “The article likely discusses methods to mitigate catastrophic forgetting of safety constraints during continual learning.”

    PermalinkArXiv