arXiv AI July 28, 2026 By Jiali Cheng, Ziheng Chen, Chirag Agarwal, Hadi Amiri

A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning

arXiv:2601. 09624v2 Announce Type: replace-cross Abstract: Machine unlearning is becoming essential for building trustworthy and compliant language models.

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

llms

arXiv AI

Jun 16

Demystifying Variance in Circuit Discovery of LLMs

arXiv:2606. 16920v1 Announce Type: cross Abstract: Circuit discovery is a key technique in mechanistic interpretability to pinpoint the model components that are crucial for performing a given task.

By Frank Zhengqing Wu, Francesco Tonin, Volkan Cevher

llms efficiency benchmarks safety

arXiv AI

Jun 24

Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?

arXiv:2606. 24026v1 Announce Type: new Abstract: Mechanistic interpretability has made substantial progress in automatically localizing circuits, but explaining what localized components do remains labor-intensive and difficult to standardize.

By Ayan Antik Khan, Harsh Kohli, Yuekun Yao, Huan Sun, Ziyu Yao

llms agents benchmarks safety

arXiv AI

Jun 9

Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?

arXiv:2605. 28860v2 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities.

By Jeanmely Rojas Nunez, Viraj Sawant, Nathan Allen, Nomgondalai Amgalanbaatar, Yannis Zongo, Vasu Sharma, Maheep Chaudhary

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Aug 7

CircuitSteer: Geometrically Aligned Multi-Layer Steering via Sparse Autoencoder Circuits

arXiv:2608. 05732v1 Announce Type: new Abstract: Controlling the behavior of large language models (LLMs) remains a critical challenge for AI alignment.

By Mehrshad Saadatinia, Parsa Razmara, Ardalan Aryashad, Ali Abbasi, Seyedarmin Azizi

llms safety

arXiv AI

Jun 16

Scalable Circuit Learning for Interpreting Large Language Models

arXiv:2606. 16939v1 Announce Type: cross Abstract: A prominent research direction in mechanistic interpretability is learning sparse circuits over LLM components to reveal how they jointly produce model behavior.

By Naiyu Yin, Dennis Wei, Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, Yue Yu

llms benchmarks safety

arXiv Machine Learning

Jun 5

Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning

arXiv:2603. 13761v2 Announce Type: replace Abstract: Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gained widespread acceptance.

By Amogh Inamdar, Zhenwei Tang, Ashton Anderson, Richard Zemel

Related stories

Demystifying Variance in Circuit Discovery of LLMs

Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?

Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?

CircuitSteer: Geometrically Aligned Multi-Layer Steering via Sparse Autoencoder Circuits

Scalable Circuit Learning for Interpreting Large Language Models

Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning