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Cosmology#Quasar Clustering, Lambda CDM, Simulations🔬 ResearchAnalyzed: Jan 3, 2026 06:18

Cosmic Himalayas Reconciled with Lambda CDM

Published:Dec 31, 2025 16:52
1 min read
ArXiv

Analysis

This paper addresses the apparent tension between the observed extreme quasar overdensity, the 'Cosmic Himalayas,' and the standard Lambda CDM cosmological model. It uses the CROCODILE simulation to investigate quasar clustering, employing count-in-cells and nearest-neighbor distribution analyses. The key finding is that the significance of the overdensity is overestimated when using Gaussian statistics. By employing a more appropriate asymmetric generalized normal distribution, the authors demonstrate that the 'Cosmic Himalayas' are not an anomaly, but a natural outcome within the Lambda CDM framework.
Reference

“The paper concludes that the 'Cosmic Himalayas' are not an anomaly, but a natural outcome of structure formation in the Lambda CDM universe.”

PermalinkArXiv
Research Paper#LLM Safety, Jailbreaking, Content Filtering🔬 ResearchAnalyzed: Jan 3, 2026 17:04

Jailbreak Attacks vs. Content Safety Filters: LLM Safety Evaluation

Published:Dec 30, 2025 07:36
1 min read
ArXiv

Analysis

This paper addresses a critical gap in LLM safety research by evaluating jailbreak attacks within the context of the entire deployment pipeline, including content moderation filters. It moves beyond simply testing the models themselves and assesses the practical effectiveness of attacks in a real-world scenario. The findings are significant because they suggest that existing jailbreak success rates might be overestimated due to the presence of safety filters. The paper highlights the importance of considering the full system, not just the LLM, when evaluating safety.
Reference

“Nearly all evaluated jailbreak techniques can be detected by at least one safety filter.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 27, 2025 23:00

The Relationship Between AI, MCP, and Unity - Why AI Cannot Directly Manipulate Unity

Published:Dec 27, 2025 22:30
1 min read
Qiita AI

Analysis

This article from Qiita AI explores the limitations of AI in directly manipulating the Unity game engine. It likely delves into the architectural reasons why AI, despite its advancements, requires an intermediary like MCP (presumably a message communication protocol or similar system) to interact with Unity. The article probably addresses the common misconception that AI can seamlessly handle any task, highlighting the specific challenges and solutions involved in integrating AI with complex software environments like game engines. The mention of a GitHub repository suggests a practical, hands-on approach to the topic, offering readers a concrete example of the architecture discussed.
Reference

“"AI can do anything"”

PermalinkQiita AI
Research Paper#Spiking Neural Networks, Adversarial Robustness, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 16:26

Reliable Adversarial Robustness Evaluation for Spiking Neural Networks

Published:Dec 27, 2025 08:43
1 min read
ArXiv

Analysis

This paper addresses the challenge of evaluating the adversarial robustness of Spiking Neural Networks (SNNs). The discontinuous nature of SNNs makes gradient-based adversarial attacks unreliable. The authors propose a new framework with an Adaptive Sharpness Surrogate Gradient (ASSG) and a Stable Adaptive Projected Gradient Descent (SA-PGD) attack to improve the accuracy and stability of adversarial robustness evaluation. The findings suggest that current SNN robustness is overestimated, highlighting the need for better training methods.
Reference

“The experimental results further reveal that the robustness of current SNNs has been significantly overestimated and highlighting the need for more dependable adversarial training methods.”

PermalinkArXiv