Marks et al.
PulseAugur coverage of Marks et al. — every cluster mentioning Marks et al. across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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AI safety advocates call for curated pretraining data to shape model personas
A recent Less Wrong post argues that frontier AI developers should actively filter and curate the data used for pretraining their models. The author suggests removing adversarial AI narratives and instead seeding the da…
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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…