Marks et al.
PulseAugur coverage of Marks et al. — every cluster mentioning Marks et al. across labs, papers, and developer communities, ranked by signal.
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New training method enhances LLM interpretability by reducing signal loss
Researchers have developed a new method called replacement-aware training to improve the interpretability of large language models. This technique trains sparse auto-encoders (SAEs) to be robust to errors introduced by …
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Google DeepMind trains Gemini 3 Flash with synthetic data for positive traits
Google DeepMind researchers have developed a method to instill positive traits into their Gemini 3 Flash model. This approach involves two stages: first, midtraining the model on synthetic documents that describe Gemini…
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Language Model Neurons Found to Be Sparse, Aiding Interpretability
Researchers have demonstrated that the neurons within a language model's MLP layers exhibit a degree of sparsity comparable to that of Sparse Autoencoders (SAEs). This finding enables the development of a gradient-based…