arXiv AI July 7, 2026 By Zhuoying Zhang, Ishan V. Prasad, Yuanzhe Hu, Zihang Liu, Hengrui Luo, Pu Ren, Yaoqing Yang

Spectral Signatures of Large Language Models

arXiv:2607. 03377v1 Announce Type: cross Abstract: The rapidly growing repository of publicly available large language models (LLMs) presents significant challenges for systematic management and quantification at scale, such as model lineage tracing, licensing, and evaluation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 13

Complexity-Guided Component-wise Initialization for Language Model Pretraining

arXiv:2607. 09204v1 Announce Type: cross Abstract: Pretrained language models often exhibit structured weight spectra, suggesting that training may repeatedly produce similar layerwise and component-wise organization.

By Konstantin Garbers, Nicholas Oh

llms nlp

arXiv Machine Learning

Jun 8

Inferring the Size of Large Language Models From Popular Text Memorization

arXiv:2605. 29223v3 Announce Type: replace Abstract: The parameter counts of the most widely used large language models (LLMs) are often withheld by their developers, leaving model size -- a primary reference point for interpreting capabilities and costs -- largely undisclosed.

By Ivica Nikolic

llms

arXiv AI

Aug 11

Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space

arXiv:2608. 07786v1 Announce Type: new Abstract: Open-weight large language models (LLMs) are increasingly developed through complex, multi-stage pipelines, leading to intricate lineage relationships that reflect model origin, ownership, and evolution.

By Yiwei Chen, Bingqi Shang, Sijia Liu

llms safety

arXiv AI

Jun 2

Uncovering Competency Gaps in Large Language Models and Their Benchmarks

arXiv:2512. 20638v2 Announce Type: replace-cross Abstract: The evaluation of large language models relies heavily on standardized benchmarks.

By Maty Bohacek, Nino Scherrer, Nicholas Dufour, Thomas Leung, Christoph Bregler, Stephanie C. Y. Chan

llms benchmarks

arXiv Machine Learning

Jul 17

Stabilizing Native Low-Rank LLM Pretraining

arXiv:2602. 12429v2 Announce Type: replace Abstract: Foundation models have achieved remarkable success, yet their growing parameter counts pose significant computational and memory challenges.

By Paul Janson, Edouard Oyallon, Eugene Belilovsky

llms rag

arXiv Machine Learning

Jun 8

Data-Constrained Language Model Pretraining: Improved Regularization and Scaling Laws

arXiv:2606. 06888v1 Announce Type: new Abstract: Classical scaling laws for language model pretraining balance model size against training dataset size under a fixed compute budget, assuming abundant data and a single pass over the corpus.

By Zhiwei Xu, Shihao Wu, Hanseul Cho, Wei Hu, Yixin Wang

llms diffusion

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Related stories

Complexity-Guided Component-wise Initialization for Language Model Pretraining

Inferring the Size of Large Language Models From Popular Text Memorization

Who Built This Model? Tracing LLM Lineage via Spectral Fingerprints in Weight Space

Uncovering Competency Gaps in Large Language Models and Their Benchmarks

Stabilizing Native Low-Rank LLM Pretraining

Data-Constrained Language Model Pretraining: Improved Regularization and Scaling Laws