arXiv AI August 14, 2026 By Morteza Salehjahromi, Shayan A. Zadegan, Amgad Muneer, Jia Wu

From Observation to Intervention: Memory in Brains and Large Language Models

arXiv:2608. 12377v1 Announce Type: cross Abstract: Brains and large language models (LLMs) are fundamentally different memory systems, but they can be compared through shared functional questions: where memory-related information is represented, how partial cues recover broader associations, how new information is written or updated, and how memory-related states can be perturbed.

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arXiv Computation and Language

Sep 1

When Errors Become Memories: Causal Pathway Tracing in Multi-Turn Memory-Augmented LLMs

arXiv:2608.30198v1 Announce Type: new Abstract: Long-term memory enables large language models (LLMs) to preserve and reuse information across interactions, but it can also turn localized errors into...

By Shuyao Xiao, Shengling Wang, Xuan Chen, Ke Chao, Ming Cui, Feifei Qian, Fanlin Meng, Chaoyang Mei, Chaoyong Jiang, Qi Ouyang, Junxi Yi

llms

arXiv AI

Sep 16

Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents

arXiv:2609.16053v1 Announce Type: cross Abstract: Long-term memory is essential for LLM-based agents operating over extended interactions. Existing memory systems primarily update memory when new inf...

By Yuanyi Song, Yukai Wang, Xinbei Ma, Zhihui Fu, Jianghao Lin, Weiwen Liu, Jun Wang, Huarong Deng, Yong Yu, Weinan Zhang

llms agents benchmarks

arXiv AI

Aug 11

In-context superposition: human-like working memory interference in large language models

arXiv:2604. 09670v2 Announce Type: replace-cross Abstract: Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments and changing goals.

By Hua-Dong Xiong, Li Ji-An, Jiaqi Huang, Robert C. Wilson, Kwonjoon Lee, Xue-Xin Wei

llms

arXiv AI

Jul 28

Temporal Context Reinstatement Drives Episodic-Like Order Memory in Long-Context Language Models

arXiv:2607. 22575v1 Announce Type: new Abstract: Human episodic memory supports the retrieval of experiences that unfold over extended timescales, yet the computational mechanisms underlying this ability remain debated due to the limited mechanistic accessibility in long-term memory experiments in humans.

By Mathis Pink, Vy Ai Vo, Qinyuan Wu, Jianing Mu, Javier Turek, Uri Hasson, Kenneth A. Norman, Sebastian Michelmann, Alexander Huth, Mariya Toneva

llms safety

arXiv AI

Aug 18

Automatic or Controlled? Repetition Priming Reveals Divergent Processing in Base LLMs, Instruct LLMs, and Humans

arXiv:2608. 14681v1 Announce Type: cross Abstract: Words recur constantly in natural language use, yet it remains unclear whether language models reactivate prior representations or re-evaluate repeated words afresh, and whether post-training changes this default behavior.

By Jinglei Ren, Yuyue Wang

llms

arXiv AI

Aug 7

Small Foundation Models of Human Cognition and Behaviour

arXiv:2608. 05224v1 Announce Type: new Abstract: Large language models fine-tuned on human behavioural data have emerged as general-purpose cognitive proxies, but the scale this requires, and whether these models process task structure or exploit statistical shortcuts, remain open questions.

By Nick Oh, Fernand Gobet

llms fine-tuning

One email a morning, machine-written

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When Errors Become Memories: Causal Pathway Tracing in Multi-Turn Memory-Augmented LLMs

Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents

In-context superposition: human-like working memory interference in large language models

Temporal Context Reinstatement Drives Episodic-Like Order Memory in Long-Context Language Models

Automatic or Controlled? Repetition Priming Reveals Divergent Processing in Base LLMs, Instruct LLMs, and Humans

Small Foundation Models of Human Cognition and Behaviour