arXiv AI

Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature

arXiv:2606. 05206v1 Announce Type: cross Abstract: Fragmentation is common in interdisciplinary fields with diverse methods and theoretical commitments.

arXiv AI

Jun 11

BioDivergence: A Benchmark and Evaluation Framework for Hidden Contextual Contradictions in Biomedical Abstracts

arXiv:2606. 11208v1 Announce Type: cross Abstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions.

By Elias Hossain, Sanjeda Sara Jennifer, Sabera Akter Bushra, Niloofar Yousefi

llms fine-tuning benchmarks

arXiv Machine Learning

5d ago

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

Aug 11

When Is a Steerable Concept Representation Real? Measurement Confounds in a Cross-Family Audit of Neuroscience Parallels in LLMs

arXiv:2608. 08159v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly reported to exhibit human-like neural and cognitive signatures, including concept cells, mental number lines, and cognitive maps.

By Yuqi Wu, Shengming Zhao, Jie Chen

llms

arXiv AI

Jun 18

SciHorizon-GENE: Benchmarking LLM for Life Sciences Inference from Gene Knowledge to Functional Understanding

arXiv:2601. 12805v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown growing promise in biomedical research, particularly for knowledge-driven interpretation tasks.

By Xiaohan Huang, Meng Xiao, Chuan Qin, Qingqing Long, Jinmiao Chen, Yuanchun Zhou, Hengshu Zhu

llms benchmarks safety

arXiv AI

Jun 26

Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods

arXiv:2606. 26130v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to guide research methodology, yet their default methodological tendencies under minimal prompting remain unclear.

By Francesca Carlon, Brecht Verbeken, Vincent Ginis, Andres Algaba

llms benchmarks

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 AI

Jun 4

BRAINCELL-AID: An Agentic AI Created Brain Cell Type Resource for Community Annotation

arXiv:2510. 17064v4 Announce Type: replace Abstract: Single-cell RNA sequencing has transformed our ability to identify diverse cell types and their transcriptomic signatures.

By Rongbin Li, Wenbo Chen, Zhao Li, Rodrigo Munoz-Castaneda, Jinbo Li, Neha S. Maurya, Arnav Solanki, Huan He, Hanwen Xing, Meaghan Ramlakhan, Zachary Wise, Nelson Johansen, Zhuhao Wu, Hua Xu, Michael Hawrylycz, W. Jim Zheng

llms rag agents safety

arXiv AI

Jul 24

Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts

arXiv:2607. 20462v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output with watermarking.

By Melanie Rieff, Robin Staab, Thibaud Gloaguen, Stefan Hegselmann, Martin Vechev

llms multimodal benchmarks safety

arXiv AI

Jun 29

CalBrief: A Pilot Diagnostic Benchmark for Evidence-Calibrated Scientific Briefing with Large Language Models

arXiv:2606. 27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the strength and scope of the supporting evidence.

By Yu Fu, Yongqi Kang, Yong Zhao

llms benchmarks

arXiv AI

Aug 5

Which Modality Decides? Counterfactual Modality Attribution for Multimodal LLMs

arXiv:2608. 00076v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) increasingly support high-stakes decision making by combining complementary information from images and text.

By Vahidin Hasic, Chao Wang, Luis C. Garcia-Peraza-Herrera, David Watson, Senka Krivic

llms diffusion multimodal benchmarks safety

arXiv AI

Jun 9

DN-Hypo-Pipeline: An AI-Driven Workflow for Hypothesis Generation via Large Language Models and Scientific Explanations

arXiv:2606. 08532v1 Announce Type: new Abstract: A scientific hypothesis is the first step in research and undergoes experimental validation, yet it also reflects a deep understanding of and reasoning about scientific phenomena.

By Lei Lin, Ronghao Wang, Chunbao Zhou, Jue Wang, Yangang Wang

llms

arXiv AI

Jul 28

Explaining BiomedCLIP with Weighted Banzhaf Interactions Supported by Tree-Gram Parsing

arXiv:2607. 23368v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are demonstrating significant capabilities in medical tasks like radiology analysis, yet providing faithful and interpretable explanations remains a key consideration for their responsible deployment in clinical settings.

By Jakub Rymarski (University of Warsaw, Poland), Adam Rempa{\l}a (University of Warsaw, Poland), Bart{\l}omiej Sobieski (University of Warsaw, Poland), Przemys{\l}aw Biecek (University of Warsaw, Poland)

llms nlp multimodal

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