AI Model Analysis

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4papers

2.8viability

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Papers

1-4 of 4

Research Paper·Feb 12, 2026

From Atoms to Trees: Building a Structured Feature Forest with Hierarchical Sparse Autoencoders

Sparse autoencoders (SAEs) have proven effective for extracting monosemantic features from large language models (LLMs), yet these features are typically identified in isolation. However, broad eviden...

4.0 viability

Research Paper·Mar 9, 2026

SYNAPSE: Framework for Neuron Analysis and Perturbation in Sequence Encoding

In recent years, Artificial Intelligence has become a powerful partner for complex tasks such as data analysis, prediction, and problem-solving, yet its lack of transparency raises concerns about its ...

3.0 viability

Research Paper·Feb 12, 2026

Cross-Architecture Model Diffing with Crosscoders: Unsupervised Discovery of Differences Between LLMs

Model diffing, the process of comparing models' internal representations to identify their differences, is a promising approach for uncovering safety-critical behaviors in new models. However, its app...

2.0 viability

Research Paper·Jan 29, 2026

Do Reasoning Models Enhance Embedding Models?

State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning models trained via ...

2.0 viability

AI Model Analysis

Proof pending

Papers

From Atoms to Trees: Building a Structured Feature Forest with Hierarchical Sparse Autoencoders

SYNAPSE: Framework for Neuron Analysis and Perturbation in Sequence Encoding

Cross-Architecture Model Diffing with Crosscoders: Unsupervised Discovery of Differences Between LLMs

Do Reasoning Models Enhance Embedding Models?

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