arXiv AI July 14, 2026 By Yong Yang, Xiang Guan, Sophie Arheix-Parras, Saeed Ahmadi, Roger Newman-Norlund, Leonardo Bonilha, Christopher Rorden, Julius Fridriksson, Rutvik H. Desai, Srihari Nelakuditi

Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 10

Recovering Lesion Parameters from Aphasic Picture Naming Error Profiles in Large Language Models

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.

By Yong Yang, Roger Newman-Norlund, Xiang Guan, Saeed Ahmadi, Regan Willis, Nadra Salman, Kalil Warren, Sophie Arheix-Parras, Srihari Nelakuditi, Leonardo Bonilha, Christopher Rorden, Rutvik H. Desai, Julius Fridriksson

llms safety

arXiv Machine Learning

Aug 14

Perturbation-based Regional Interpretability through Subtraction Mapping (PRISM): naming-error dissociations in language models and post-stroke aphasia

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.

By Xiang Guan, Roger D. Newman-Norlund, Yong Yang, Saeed Ahmadi, Regan Willis, Nadra Salman, Kalil Warren, Srihari Nelakuditi, Chris Rorden, Leonardo Bonilha, Julius Fridriksson

llms safety

arXiv AI

Jun 16

Do LLMs Reliably Identify Correct Information Units in Aphasic Discourse?

arXiv:2606. 15696v1 Announce Type: new Abstract: Correct Information Units (CIUs) are central to discourse assessment in aphasia because they quantify communicative informativeness rather than linguistic form alone.

By Jason M Pittman, Yesenia Medina-Santos, Anton Phillips Jr., Brielle C. Stark

llms agents

arXiv AI

Jun 19

The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation

arXiv:2603. 28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts.

By Doan Nam Long Vu, Simone Balloccu

llms multimodal safety

arXiv Machine Learning

Aug 31

Prompts Without Evidence: How Neuroimaging Mentions Shift Clinical Vision-Language Model Predictions

arXiv:2603.28387v3 Announce Type: replace-cross Abstract: Trustworthy clinical AI must use real evidence and avoid relying on surface-level artifacts. We evaluate 12 open-weight vision-language model...

By Doan Nam Long Vu, Simone Balloccu

llms multimodal safety

arXiv Computation and Language

Sep 14

Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking

The study introduces a scalable acoustic‑masking method to quantify how much each consonant contributes to word intelligibility. By silencing individual consonants in isolated words and measuring misrecognition rates with three ASR models, the authors define a mask‑induced misrecognition rate (MMR). Across English, Spanish, German, and Czech, MMR negatively correlates with phoneme frequency and positively with functional load, revealing that consonant importance varies by language.

By Eunjung Yeo, Kwanghee Choi, Krupaben Kothadia, Visar Berisha, Julie M. Liss, David R. Mortensen, David Harwath

llms multimodal

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Recovering Lesion Parameters from Aphasic Picture Naming Error Profiles in Large Language Models

Perturbation-based Regional Interpretability through Subtraction Mapping (PRISM): naming-error dissociations in language models and post-stroke aphasia

Do LLMs Reliably Identify Correct Information Units in Aphasic Discourse?

The Scaffold Effect: How Prompt Framing Drives Apparent Multimodal Gains in Clinical VLM Evaluation

Prompts Without Evidence: How Neuroimaging Mentions Shift Clinical Vision-Language Model Predictions

Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking