arXiv Machine Learning August 4, 2026 By Kunal Kumar Pant, Nithin Nagaraj

ChaosProbe: A Neurochaotic Lens on Frozen Transformer Input-Embedding Spaces

Read the original on arXiv Machine Learning →

arXiv:2608. 01968v1 Announce Type: new Abstract: Transformer models are most often understood through what they do: their benchmark performance, generation quality, or behavior on downstream tasks.

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arXiv Machine Learning

Jun 9

Trajectory Geometry of Transformer Representations Across Layers

arXiv:2606. 09287v1 Announce Type: new Abstract: Understanding how transformer representations evolve across layers, not merely what they encode, remains an open problem in mechanistic interpretability.

By Vishal Pandey, Gopal Singh

llms rag safety

arXiv Machine Learning

4d ago

Representational and Functional Robustness to Electrode Montages in EEG Foundation Models

arXiv:2609.36288v1 Announce Type: new Abstract: EEG foundation models (EEG-FMs) are intended to generalize across different datasets by learning representations that, ideally, are invariant to datase...

By Jakob Steglich, Justus Meyer zu Bexten, Shakiba Moradi, Laure Ciernik, Simon M. Hofmann, Mina Jamshidi Idaji

rag

arXiv Machine Learning

Jun 26

The Geometry of Updates: Fisher Alignment at Vocabulary Scale

arXiv:2606. 27242v1 Announce Type: new Abstract: Training-free source selection for LLM families with shared vocabularies arises in scientific string domains such as SMILES, protein, and genomic sequences, where candidate corpora share a tokenizer but differ in prediction targets.

By John Sweeney

llms rag nlp safety

arXiv Machine Learning

Sep 1

Tracing distinguishability through transformer processing with stochastic LayerNorm

arXiv:2608.30720v1 Announce Type: new Abstract: Representational similarity is foundational to analyses of deep networks, yet distances between point-valued representations are not intrinsically tied...

By Kieran Murphy

llms fine-tuning efficiency safety

arXiv Computation and Language

Sep 11

Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts

The study examined whether brain-language model alignment reflects shared computational mechanisms or merely stable lexical‑semantic correspondences. Using whole‑brain encoding across Mandarin, English, and French, transformer representations predicted activity in a distributed network that overlapped across languages and remained stable across layers. Contextual embeddings and measures of prediction or compression did not outperform static lexical embeddings, suggesting that alignment is robust but not informative about shared computational processes.

By Ni Yang, Rui He, Philipp Homan, Iris Sommer, Davide Staub, Wolfram Hinzen

llms rag safety

arXiv AI

Jun 9

Phantom transitions in language model fine-tuning

arXiv:2606. 07559v1 Announce Type: cross Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently.

By Vaibhav Prakash, Jayasri Dontabhaktuni

llms rag fine-tuning

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