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Research#Agent AI🔬 ResearchAnalyzed: Jan 10, 2026 07:45

Blockchain-Secured Agentic AI Architecture for Trustworthy Pipelines

Published:Dec 24, 2025 06:20
1 min read
ArXiv

Analysis

This research explores a novel architecture combining agentic AI with blockchain technology to enhance trust and transparency in AI systems. The use of blockchain for monitoring perception, reasoning, and action pipelines could mitigate risks associated with untrusted AI behaviors.
Reference

“The article proposes a blockchain-monitored architecture.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 25, 2025 13:10

MicroQuickJS: Fabrice Bellard's New Javascript Engine for Embedded Systems

Published:Dec 23, 2025 20:53
1 min read
Simon Willison

Analysis

This article introduces MicroQuickJS, a new Javascript engine by Fabrice Bellard, known for his work on ffmpeg, QEMU, and QuickJS. Designed for embedded systems, it boasts a small footprint, requiring only 10kB of RAM and 100kB of ROM. Despite supporting a subset of JavaScript, it appears to be feature-rich. The author explores its potential for sandboxing untrusted code, particularly code generated by LLMs, focusing on restricting memory usage, time limits, and access to files or networks. The author initiated an asynchronous research project using Claude Code to investigate this possibility, highlighting the engine's potential in secure code execution environments.
Reference

“MicroQuickJS (aka. MQuickJS) is a Javascript engine targetted at embedded systems. It compiles and runs Javascript programs with as low as 10 kB of RAM. The whole engine requires about 100 kB of ROM (ARM Thumb-2 code) including the C library. The speed is comparable to QuickJS.”

PermalinkSimon Willison
Research#AI Agent🔬 ResearchAnalyzed: Jan 10, 2026 11:40

Factor(U,T): A New Method for Monitoring and Controlling Untrusted AI Agents' Plans

Published:Dec 12, 2025 19:11
1 min read
ArXiv

Analysis

This research paper proposes a novel approach to control untrusted AI agents by monitoring their plans. The paper's contribution lies in its Factor(U,T) method, offering a potential solution to a critical safety and security concern in AI development.
Reference

“The research focuses on the Factor(U,T) method.”

PermalinkArXiv
Research#federated learning🔬 ResearchAnalyzed: Jan 4, 2026 09:34

SpectralKrum: A Spectral-Geometric Defense Against Byzantine Attacks in Federated Learning

Published:Dec 12, 2025 18:12
1 min read
ArXiv

Analysis

The article introduces SpectralKrum, a novel defense mechanism against Byzantine attacks in federated learning. The approach leverages spectral-geometric properties to mitigate the impact of malicious participants. The use of spectral methods suggests a focus on identifying and filtering out adversarial updates based on their spectral characteristics. The geometric aspect likely involves analyzing the spatial relationships of the updates in the model parameter space. This research area is crucial for the robustness and reliability of federated learning systems, especially in environments where data sources are untrusted.

Key Takeaways

    Reference

    “”

    PermalinkArXiv
    Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 12:02

    Factor(T,U): Factored Cognition Strengthens Monitoring of Untrusted AI

    Published:Dec 1, 2025 19:37
    1 min read
    ArXiv

    Analysis

    The article likely discusses a new approach to monitoring and evaluating the behavior of AI systems, particularly those that are not fully trusted. The title suggests a focus on 'factored cognition,' implying a method of breaking down the AI's cognitive processes for better observation and control. The source, ArXiv, indicates this is a research paper, suggesting a technical and potentially complex analysis of the topic.

    Key Takeaways

      Reference

      “”

      PermalinkArXiv
      Safety#Security👥 CommunityAnalyzed: Jan 10, 2026 16:35

      Security Risks of Pickle Files in Machine Learning

      Published:Mar 17, 2021 10:45
      1 min read
      Hacker News

      Analysis

      This Hacker News article likely discusses the vulnerabilities associated with using Pickle files to store and load machine learning models. Exploiting Pickle files poses a serious security threat, potentially allowing attackers to execute arbitrary code.
      Reference

      “Pickle files are known to be exploitable and allow for arbitrary code execution during deserialization if not handled carefully.”

      PermalinkHacker News