arXiv AI June 9, 2026 By Zirui Wang, Yusen Hou, Shaofeng Liang, Bowen Tian, Yanlin Zhang, Wenshuo Chen, Yutao Yue

ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding

arXiv:2606. 07524v1 Announce Type: cross Abstract: The explosive growth of large language models (LLMs) has created a heterogeneous and poorly documented ecosystem, making systematic model comparison increasingly important for provenance auditing, security analysis, and model selection.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 4

Data Attribution in Large Language Models via Bidirectional Gradient Optimization

arXiv:2606. 04928v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance.

By Fr\'ed\'eric Berdoz, Luca A. Lanzend\"orfer, Kaan Bayraktar, Roger Wattenhofer

llms safety

arXiv AI

Jul 24

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

arXiv:2512. 10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data.

By Nick Jiang, Xiaoqing Sun, Lisa Dunlap, Lewis Smith, Neel Nanda

llms rag

arXiv AI

Aug 11

Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation

arXiv:2510. 21891v2 Announce Type: replace-cross Abstract: To deploy large language models (LLMs) in high-stakes application domains that require substantively accurate responses to open-ended prompts, we need reliable, computationally inexpensive methods that assess the trustworthiness of long-form responses generated by LLMs.

By Dhrupad Bhardwaj, Julia Kempe, Tim G. J. Rudner

llms rag fine-tuning

arXiv Machine Learning

Jul 22

CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs

arXiv:2603. 21014v2 Announce Type: replace Abstract: Mechanistic interpretability seeks to understand how Large Language Models (LLMs) represent and process information.

By Florent Draye, Vedant Palit, Abir Harrasse, Tung-Yu Wu, Jiarui Liu, Punya Syon Pandey, Roderick Wu, Chih-Hao Hsu, Terry Jingchen Zhang, Zhijing Jin, Bernhard Sch\"olkopf

llms safety

arXiv AI

Jun 10

READER: Robust Evidence-based Authorship Decoding via Extracted Representations

arXiv:2606. 10794v1 Announce Type: new Abstract: As agentic applications increasingly route user tasks through official and third-party LLM APIs, provenance becomes an operational question: which model generated a given black-box response?

By Jiaxu Liu, Sunnan Mu, Dong Huang, Liuyin Wang, Jing Shao, Jie Zhang

llms agents benchmarks

arXiv AI

Aug 5

Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes

arXiv:2608. 02879v1 Announce Type: new Abstract: The widespread adoption of proprietary Large Language Models (LLMs) accessed strictly through closed APIs has created a critical challenge for responsible deployment: a fundamental lack of interpretability.

By Maryam Rezaee, Pooriya Safaei, Maryam Asgarinezhad, Fatemeh Seyyedsalehi

llms safety

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Data Attribution in Large Language Models via Bidirectional Gradient Optimization

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Embedding Trust: Semantic Isotropy Predicts Nonfactuality in Long-Form Text Generation

CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs

READER: Robust Evidence-based Authorship Decoding via Extracted Representations

Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes