Introduces the UCSF-PDGM-VQA dataset of 2387 QA pairs from 473 glioma MRI studies and demonstrates that state-of-the-art VLMs exhibit modality collapse on multi-sequence 3D medical images.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
years
2026 5representative citing papers
Proves that RoPE attention loses locality bias and token distinction in long contexts, approaching random behavior independent of content.
VT-Bench aggregates 14 datasets from 9 domains and evaluates 23 models to standardize visual-tabular discriminative and generative tasks.
CTFusion is a live-CTF streaming benchmark that prevents data contamination by forwarding only the first correct flag per challenge under a shared team account.
Hy-MT2 presents three new multilingual translation models that claim to outperform listed open-source and commercial systems on diverse tasks while enabling low-storage on-device use.
citing papers explorer
-
UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation
Introduces the UCSF-PDGM-VQA dataset of 2387 QA pairs from 473 glioma MRI studies and demonstrates that state-of-the-art VLMs exhibit modality collapse on multi-sequence 3D medical images.
-
RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts, Provably
Proves that RoPE attention loses locality bias and token distinction in long contexts, approaching random behavior independent of content.
-
VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning
VT-Bench aggregates 14 datasets from 9 domains and evaluates 23 models to standardize visual-tabular discriminative and generative tasks.
-
CTFusion: A CTF-based Benchmark for LLM Agent Evaluation
CTFusion is a live-CTF streaming benchmark that prevents data contamination by forwarding only the first correct flag per challenge under a shared team account.
-
Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Hy-MT2 presents three new multilingual translation models that claim to outperform listed open-source and commercial systems on diverse tasks while enabling low-storage on-device use.