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Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 14:33

Dissecting Multilingual Reasoning: Step and Token Level Attribution in CoT

Published:Nov 19, 2025 21:23
1 min read
ArXiv

Analysis

This research dives into the critical area of explainability in multilingual Chain-of-Thought (CoT) reasoning, exploring attribution at both step and token levels. Understanding these granular attributions is vital for improving model transparency and debugging complex multilingual models.
Reference

The research focuses on step and token level attribution.

Research#KANs👥 CommunityAnalyzed: Jan 10, 2026 15:27

Kolmogorov-Arnold Networks: Enhancing Neural Network Interpretability

Published:Sep 12, 2024 10:14
1 min read
Hacker News

Analysis

This article discusses the potential of Kolmogorov-Arnold Networks (KANs) to improve the understanding of neural networks, a crucial area for broader adoption and trust. The implications for model transparency and debuggability are significant, suggesting a shift towards more explainable AI.
Reference

The context highlights the potential of KANs, though no specific facts are mentioned, indicating the need for further investigation of the technology's application.