The study investigates how progressive neuronal loss affects language models by systematically ablating computational units in LSTM and Transformer architectures. LSTMs show a gradual decline in syntactic and semantic abilities, while Transformers exhibit a threshold-like collapse under MLP ablation and a smoother sigmoid-like decline under attention-head ablation. These distinct failure modes mirror different aphasic syndromes, suggesting that architecture-dependent degradation patterns can provide computational insights into neurodegenerative diseases.
arXiv:2608. 12717v1 Announce Type: new Abstract: Mechanistic interpretability of large language models lacks spatially resolved, falsifiable tools for testing whether internal components are specialized for distinct cognitive operations.
arXiv:2608. 06429v1 Announce Type: cross Abstract: Interpretability methods for large language models (LLMs) describe internal state but do not directly test whether that state is causally sufficient to produce the observed behavior.
arXiv:2603. 22473v2 Announce Type: replace-cross Abstract: Hybrid language models combine softmax attention with linear-time sequence mechanisms such as state-space or linear-attention layers, but the functional contribution of each component type remains insufficiently characterized.
The paper introduces a mechanistic interpretability framework inspired by aphasia to probe large language models (LLMs). Using classifier‑guided lesioning and continuous activation steering, the authors induce aphasia‑like breakdowns and find that language functions can be isolated to discrete structural circuits and continuous vectors, yet these functions are densely entangled with significant overlap across different aphasic profiles. While steering generalizes across downstream tasks, structural lesioning shows asymmetric constraints, highlighting the densely entangled architecture of LLMs that can be mapped through clinically‑informed interventions.
arXiv:2603. 00270v3 Announce Type: replace-cross Abstract: Large language models can process millions of tokens, yet how they handle conflicting information within context remains poorly understood.
arXiv:2607. 11621v1 Announce Type: new Abstract: Aphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether general-purpose language models not designed for clinical simulation can reproduce these patterns remains untested.
arXiv:2606. 30646v1 Announce Type: cross Abstract: Speech recruits the same executive, attentional, and working memory processes underlying instrumental activities of daily living, or IADLs, providing a non-invasive proxy for cognitive assessment.
arXiv:2607. 00397v1 Announce Type: cross Abstract: Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and relating them to biological cognition.
arXiv:2608. 12398v1 Announce Type: cross Abstract: We propose IM-LEPP (Integrated Multimodal Latent Energy-based Predictive Processing), a hierarchical, energy-based model of multimodal cognition that extends a previously proposed single-modality model (LEPP) to integrate vision and language.
arXiv:2608.12149v2 Announce Type: replace Abstract: We present the first systematic study of Massive activations (MAs) in layer-interleaved HLA LLMs and uncover two architecture-aligned morphologies:...
arXiv:2609.14764v1 Announce Type: cross Abstract: Early detection of cognitive decline from speech offers a scalable and non-invasive alternative to conventional clinical assessment. Verbal fluency t...
arXiv:2607. 20524v1 Announce Type: new Abstract: Mean cross-positional attention degradation is widely reported in transformer interpretability, yet whether it causally limits contextual retrieval remains untested.