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
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
arXiv:2505.19333v2 Announce Type: replace
Abstract: Current evaluations of Large Language Model (LLM) steering techniques focus on task-specific performance, overlooking how well steered representati...
By Zach Studdiford, Timothy T. Rogers, Siddharth Suresh, Kushin Mukherjee
arXiv:2510.01030v2 Announce Type: replace
Abstract: The human ability to translate diverse perceptual and linguistic inputs into structured behavior has been thought to rest on learning robust repres...
By Zach Studdiford, Timothy T. Rogers, Kushin Mukherjee, Siddharth Suresh
The paper extends mechanistic interpretability of large language models by modeling concepts as low‑dimensional non‑linear manifolds rather than linear subspaces. It introduces a concept‑based alignment (CBA) score to compare these manifolds across layers and models, revealing block structures in intermediate layers, a shift from syntax‑dominated to mixed syntactic‑semantic concepts, and training‑dependent multilingual sharing. The study also shows that alignment patterns differ across model families and training stages, with adjacent stages aligning more closely than distant ones.
By Tido Specht, Elias Benedict Krey, Nils Neukirch, Nils Strodthoff
arXiv:2608. 07353v1 Announce Type: cross Abstract: Understanding concepts is fundamental to generalization.
By Karim Radouane, Jose G Moreno, Lynda Tamine
arXiv:2510. 16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory hinder the modeling of long-term, cross-session user states.
By Ao Tian, Yunfeng Lu, Xinxin Fan, Changhao Wang, Lanzhi Zhou, Yeyao Zhang, Yanfang Liu
The paper introduces TUX, a Tacit Understanding Index that measures how similarly humans and large language models (LLMs) place concepts along subjective spectra in a task inspired by the game Wavelength. Using 241 human participants and 200 profile-conditioned LLM agents across four models, the study finds that human–agent pairs with similar traits achieve higher TUX scores, indicating that tacit alignment is linked to person-level characteristics. Regression analyses show that richer predictor sets—including individual traits, decision-making styles, and confidence—improve the explainability of TUX beyond simple trait-distance baselines.
By Yueshen Li, Hanyi Min, Vedant Das Swain, Koustuv Saha
The paper explores how individual text corpora (ITCs) can be integrated into small language models (SLMs) using DoRA fine‑tuning. By training a DoRA adapter for each of 150 participants, the authors show that the adapter can encode a participant’s own ITC into the model’s weights, improving fit to that participant’s held‑out text. However, while the adapter improves log‑loss on a generalized knowledge test, it does not enhance accuracy under a bias‑corrected PMI readout, and adding retrieval does not provide further benefit.
By Christoph Wigbels, Ali Abusaleh, Markus T. Jansen, Alexander Mehler, Markus J. Hofmann
arXiv:2606. 11371v1 Announce Type: cross Abstract: Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content.
By Han-Jen Chang, Yasir \c{C}atal, Angelika Wolman, Agust\'in Ib\'a\~nez, David Smith, I-Wen Su, Kai-Yuan Cheng, Georg Northoff
The paper investigates the intrinsic dimension (ID) of large language model (LLM) representations as an indicator of linguistic complexity. By comparing ID across model layers for coordination vs. subordination, right‑branching vs. center‑embedding, and unambiguous vs. ambiguous attachment, the authors find consistent ID differences that align with established complexity contrasts. Experiments across six LLMs, including representational similarity and layer pruning analyses, confirm that more complex phenomena produce higher ID profiles, with peaks occurring at different layers for each contrast.
By Marco Baroni, Emily Cheng, Iria de-Dios-Flores, Francesca Franzon
The study investigates whether language models tailored to specific cognitive domains better align with corresponding brain systems. By prompting and fine‑tuning large language models into six domain experts—sensory, spatial, numerical, reasoning, social, and abstract—the authors find that each expert’s representations more closely match the brain region associated with its domain than other experts. This domain‑specific alignment holds across multiple base models and fMRI datasets, while overall prediction accuracy remains largely unchanged, indicating that regional alignment can be obscured when summarizing across the brain.
By Zhivar Sourati, Mengxuan Helen Wu, Nona Ghazizadeh, Jonas Kaplan, Morteza Dehghani, Samuel A. Nastase