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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:04

Interpretable and Steerable Concept Bottleneck Sparse Autoencoders

Published:Dec 11, 2025 16:48
1 min read
ArXiv

Analysis

This article introduces a new type of autoencoder designed for interpretability and control. The focus is on concept bottlenecks and sparsity, suggesting an approach to understanding and manipulating the internal representations of the model. The use of 'steerable' implies the ability to influence the model's behavior based on these interpretable concepts. The source being ArXiv indicates this is a research paper, likely detailing the architecture, training methodology, and experimental results.
Reference

Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 14:09

Causal Concept-Guided Diffusion LLMs: A New Approach

Published:Nov 27, 2025 06:33
1 min read
ArXiv

Analysis

This ArXiv paper introduces C^2DLM, a novel approach to large language models. The integration of causal concepts within a diffusion model framework presents a potentially significant advancement in model interpretability and control.
Reference

The paper focuses on Causal Concept-Guided Diffusion Large Language Models.