The article reports that large language models can predict and collaboratively modulate human memory search during a semantic fluency task. By tracking and forecasting participants’ semantic retrieval patterns, the models outperform other humans in following these mental trajectories. This suggests that AI can serve as a cognitive tool to extend human abilities in open‑ended conceptual exploration and creative ideation.
By Eric Lacosse, Mariana Duarte, Graham Todd, Peter M. Todd, Daniel C. McNamee
The study investigates how large language models (LLMs) can assist humans in semantic memory search tasks. By using the semantic fluency task (SFT), the researchers evaluate whether LLMs can follow and enhance human mental trajectories during generative semantic retrieval. Results show that an LLM’s ability to track and predict human memory trajectories in this task surpasses that of other humans.
By Eric Lacosse, Mariana Duarte, Graham Todd, Peter M. Todd, Daniel C. McNamee
arXiv:2605. 28969v2 Announce Type: replace-cross Abstract: If an AI agent makes decisions on a person's behalf, those decisions must align with its user.
By Aarik Gulaya
The study investigates whether large language models (LLMs) develop internal representations similar to cognitive maps used by animals for flexible behavior. Using relational reasoning tasks and causal mosaic experiments, the authors found that LLMs beyond ten billion parameters can infer unstated relationships, generalize to synthetic entity names, and form abstract relational structures that combine with entity identities. These relational representations are stored as pairwise links and composed retrospectively, mediated by a sparse, distributed key‑value system that can be perturbed to implant false memories and alter behavior predictably.
By Albert Qin, Jonathan D. Cohen, Gautam Reddy
The paper introduces CAPRI, a dataset of conversations designed to test whether large language models (LLMs) can adapt their responses to users’ cultural backgrounds. Experiments show that while LLMs can infer a user’s cultural context and recall relevant conventions, they frequently fail to apply this knowledge when generating answers unless explicitly prompted to do so. The study also finds that LLMs’ cultural adaptations improve as more cues are provided, yet their default priors are not culture‑neutral and sometimes reflect the model’s country of origin.
By Yisong Miao, Jian Zhu, Vered Shwartz
The paper introduces DisCo, a framework that uses large language models (LLMs) to automatically generate interpretable symbolic cognitive models. DisCo employs evolutionary search guided by two structural components: a Validator that filters out structurally invalid models and a Cleaner that enhances parameter identifiability by removing redundancies. Experiments on three behavioral tasks show that DisCo produces models with performance comparable to task‑specific baselines, and ablation studies highlight the Validator’s role in maintaining psychologically meaningful structure.
By Boyoung Kwon, Jiyoon Seo, Sang Ho Lee
arXiv:2609.24238v1 Announce Type: new
Abstract: We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contrad...
By Guilhem Fouilh\'e, Nicholas Asher, Philippe Muller
arXiv:2601.21702v4 Announce Type: replace
Abstract: We consider Representation Misdirection (RM), a class of large language model (LLM) unlearning methods that achieve forgetting by redirecting the l...
By Tien Dang, The-Hai Nguyen, Dinh Mai Phuong, Nguyen Minh Phuong, Anh Bui, Hoang Thanh-Tung, Le-Minh Nguyen, Naoya Inoue
arXiv:2606.23283v2 Announce Type: replace
Abstract: Memory systems are essential for personalized Large Language Models (LLMs). However, retrieval within these systems primarily relies on semantic si...
By Hongxun Ding, Xiang Yu, Chengbing Wang, Jianfei Xiao, Keqin Bao, Wenjie Wang, Xiangnan He
arXiv:2606. 01223v1 Announce Type: cross Abstract: Despite substantial progress in long-context modeling, existing benchmarks remain confined to factual memory for explicit recall, failing to measure the reflective memory required to synthesize fragmented, multimodal cues into high-level interpretations.
By Jingjie Lin, Bingbing Wang, Zihan Wang, Zhengda Jin, Weiming Qiao, Jing Li, Ruifeng Xu
arXiv:2608.22411v1 Announce Type: new
Abstract: Social interaction increasingly takes place in multicultural settings, where individuals may draw on multiple cultural influences and adapt their commu...
By Chongyuan Dai, Yaling Shen, Shengeng Tang, Hui Ma, Jinpeng Hu
arXiv:2605.28009v2 Announce Type: replace-cross
Abstract: Memory-augmented large language models extend reasoning beyond a fixed context window by maintaining long-term memory across interactions. Ho...
By Hyeonjeong Ha, Jeonghwan Kim, Cheng Qian, Jiayu Liu, William M. Campbell, Yue Wu, Yuji Zhang, Kathleen McKeown, Dilek Hakkani-Tur, Heng Ji