C*-RASP
PulseAugur coverage of C*-RASP — every cluster mentioning C*-RASP across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
-
New research explores transformer compression for efficiency and length generalization · 2 sources tracked
Two new arXiv papers explore methods for compressing transformer models to improve efficiency and length generalization. The first paper, "Dense Structural Compression of Transformers via Gauge-Correct Channel Removal,"…
-
New theory precisely characterizes Transformer length generalization on regular languages
Researchers have developed a new algebraic decomposition theory to precisely characterize which regular languages Transformer-based language models can generalize to longer sequences than they were trained on. This theo…
-
New research explores Transformer expressivity and sample complexity
Researchers have published a theoretical analysis of Transformers, focusing on their expressivity and sample complexity. The work proposes preliminary bounds for learning C-RASP constructions with Transformers, aiming t…
-
New framework C*-RASP analyzes transformer planning abilities
Researchers have developed C*-RASP, an extension of the C-RASP framework, to analyze the capabilities of decoder-only transformer models in AI planning tasks. This new framework aims to provide theoretical guarantees fo…