arXiv Machine Learning June 2, 2026 By Joakim Edin, Casper L. Christensen, R\'obert Csord\'as, Tuukka Ruotsalo, Zhengxuan Wu, Maria Maistro, Jing Huang, Lars Maal{\o}e

Correcting Gradient-Based Circuit Localization via Interaction-Aware Backpropagation

Read the original on arXiv Machine Learning →

arXiv:2505. 17630v4 Announce Type: replace-cross Abstract: Circuit localization methods aim to identify the subset of model components responsible for specific behaviors in large language models, enabling detailed mechanistic analysis.

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

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

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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 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 Machine Learning

Jul 30

Dense Local Dependencies Induce Attention-Logit Explosion and Training Instability During Long-Sequence Transformer Training

arXiv:2505. 15548v2 Announce Type: replace Abstract: Autoregressive transformer language models frequently exhibit training instability when trained on long sequences, particularly under low-precision arithmetic.

By Suvadeep Hajra

llms

arXiv AI

Jun 12

Language Model Circuits Are Sparse in the Neuron Basis

arXiv:2601. 22594v2 Announce Type: replace-cross Abstract: The high-level concepts that a neural network uses to perform computation need not be aligned to individual neurons (Smolensky, 1986).

By Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt, Sarah Schwettmann

llms benchmarks safety

arXiv Machine Learning

Jun 16

Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

arXiv:2601. 18699v2 Announce Type: replace Abstract: Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities.

By Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov

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

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

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