Topic · 6 recaps

Mechanistic Interpretability

Reverse-engineering neural networks at the level of circuits and features — figuring out what specific weights and activations compute, not just what the model outputs.

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Blind Men and the Elephant: Probing the Epistemic Myopia of LLMs under Long-Tail Divergent Knowledge

Evaluation · Aug 28

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

Mechanistic Interpretability · Aug 13

Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval

Evaluation · Aug 2

Text Template Tokens Are Implicit Semantic Registers in Diffusion Transformers

Image Generation · Jul 21

Why Far Looks Up: Probing Spatial Representation in Vision-Language Models

Mechanistic Interpretability · May 28

Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs

Evaluation · May 7

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