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Postingan yang diterbitkan di instalasi FPDP ini.

Sparse Autoencoders decompose model activations into sparse combinations of interpretable dictionary atoms. Their objective smuggles in an additional prior where concepts across patches are treated as independent. Spatial-SAE is proposed as an amortized MAP estimator.
Source: arXiv
https://arxiv.org/abs/2610.04112
#MachineLearning
Researchers measured how well one expert human reader and three LLM families reproduced their own reading of Sumerian myths over time. Across runs months apart, the human cut the text in much the same places but named the segments differently.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.04370
#MachineLearning
Researchers question similarity scores used to claim shared structure between the brain or languages. A probe performance drops to chance in four of seventeen languages, and a model trained on a destroyed brain target still scores 0.31.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.03827
#MachineLearning
Language model activations inhabit privileged error-correcting basins. Researchers hypothesize this robustness results from passive dynamics constraining mechanisms in the forward pass.
Source: arXiv
https://arxiv.org/abs/2610.04183
#MachineLearning
SGAnalog introduces an end-to-end circuit benchmark from open-source silicon tapeouts. It contains 273 topologically distinct top-level designs. Across seven models on 66 transcription tasks, the strongest model reaches 56.1 percent exact graph isomorphism.
Source: arXiv
https://arxiv.org/abs/2610.03934
#MachineLearning
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