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From the 1 of 5 linked papers with an AI index.

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5 papers

cs.GT2026

Error-Aware Reverse Auction Mechanism for Large Language Model Routing

Haolong Chen, Zhengyuan Xin, Liang Zhang +2

Routing each query to a cost-effective large language model (LLM) is critical for balancing quality and cost, yet most routers rely on a centralized task center to predict model pe…

cs.CL2026

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

Yong Yang, Roger Newman-Norlund, Xiang Guan +10

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 behavio…

cs.AI2026

Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns

Yong Yang, Xiang Guan, Sophie Arheix-Parras +7

The study shows that applying targeted lesions or noise to a multimodal language model (LLaVA 1.6) can replicate the picture‑naming error patterns observed in individuals with post…

stat.ME2026

Topological inference on brain networks with application to lesion symptom mapping

Yuan Wang, Jian Yin, Nicholas Riccardi +3

Persistent homology (PH) characterizes the shape of brain networks through persistence features. Group comparison of persistence features from brain networks can be challenging as…

cs.LG2026

Stroke Lesions as a Rosetta Stone for Language Model Interpretability

Julius Fridriksson, Roger D. Newman-Norlund, Saeed Ahmadi +10

Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which model components are truly necessary for language function remain limited. Current i…