LUCAID is an agentic multimodal AI system designed for precision lung cancer pathology, integrating nine modules that cover the entire routine workflow—from quality control and tumor detection to histological subtyping, microenvironment profiling, cellularity quantification, and biomarker scoring (PD‑L1, MET, TROP‑2). The system generates automated structured reports and allows interactive querying of module outputs. In prospective clinical validation, LUCAID achieved 93.0% concordance with an expert‑panel reference standard for clinically actionable decisions, outperforming five experienced thoracic pathologists who ranged from 68.3% to 81.1% concordance.
By Marie-Lisa Eich, Kai Standvoss, Timo Milbich, Alexander M\"ollers, Miriam H\"agele, Philipp Anders, Lars Tharun, Hanna Kontradiuk, Sebastian Kons, Nader Aldoj, Recepcan Adig\"uzel, Adam Narai, Lukas H\"onig, Jonathan Striebel, Binru Yang, Mihnea P. Dragomir, Marvin Sextro, Philipp Keyl, Philipp Jurmeister, Rosemarie Krupar, Evelyn Ramberger, James Wells, Julika Ribbat-Idel, Andreas Kunft, Hussam Shuaib, Christian Groh\'e, Reinhard B\"uttner, David Horst, Klaus-Robert M\"uller, Lukas Ruff, Maximilian Alber, Frederick Klauschen, Simon Schallenberg
MolEmb is a lightweight framework that adapts multimodal large language models (MLLMs) to serve as general molecular embedding models. By aligning molecular profiles with textual descriptions in a shared embedding space using a bidirectional contrastive objective, MolEmb produces embeddings conditioned on both a molecular profile and a natural‑language semantic context. The model performs competitively on molecular property prediction and enables cross‑modal molecule‑text retrieval, while the newly introduced MolCAR benchmark demonstrates that context‑aware molecular embedding is largely a data property of the supervision.
By Xinjian Zhao, Xiangru Jian, Yaoyao Xu, Xiaozhuang Song, Wei Pang, Lei Bai, Tianshu Yu
The paper evaluates the tablet‑2 long‑term memory engine on multilingual text benchmarks and cross‑lingual photo retrieval without lexical matching. Tablet‑2 achieves high accuracy on LongMemEval‑S (95.7%) and moderate accuracy on BEAM‑1M (67.5%), with minimal variance across runs. In multimodal tests, it outperforms BM25 on image‑cell recall and shows significant language‑dependent performance gaps, especially for low‑resource languages.
By Sunwoo Kim
The paper introduces CAIT, a benchmark of 400 synthetic scenes featuring counter‑intuitive actions that challenge multimodal large language models (MLLMs). Human participants and proprietary models like Claude and Gemini perform well, but standard open‑source instruction‑tuned MLLMs fail, largely due to a strong language prior that overrides contradictory visual evidence. The study shows that Chain‑of‑Thought reasoning can help but introduces new issues, while targeted fine‑tuning and structured prompting can reduce reliance on language priors and improve visual grounding.
By Chen Ling, Tongwei Zhang, Hanqian Li, Nai Ding
arXiv:2602.06268v2 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly integrated into clinical workflows. However, p...
By Junhyeok Lee, Han Jang, Kyu Sung Choi
arXiv:2608.24010v1 Announce Type: new
Abstract: Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class prox...
By Hengjie Zhu, Dayan Wu, Zihao Zhang, Xinze Liu, Jingxuan Yu, Peng Fu, Zheng Lin, Weiping Wang
Prompt learning modifies vision‑language models by optimizing continuous prompt vectors, yet the resulting prompts are hard to interpret in natural language. PromptSpLiCE is a post‑hoc method that rewrites each class‑conditioned text embedding as a sparse mix of concepts from a fixed dictionary, enabling a direct comparison of concept profiles before and after prompt learning. Across 11 image‑classification datasets, the method shows that only about 1.6 of the initial top‑10 concepts remain after learning, and that larger profile changes correlate with higher accuracy gains, while a derived gradient expression offers geometric insight into loss sensitivity.
By Ryo Kamiya, Hiroshi Kera, Kazuhiko Kawamoto
arXiv:2602.18532v3 Announce Type: replace-cross
Abstract: Following the rise of large foundation models, Vision-Language-Action models (VLAs) emerged, leveraging strong visual and language understand...
By Xiao-Ming Wu, Bin Fan, Kang Liao, Jian-Jian Jiang, Runze Yang, Yihang Luo, Zhonghua Wu, Wei-Shi Zheng, Chen Change Loy
Web agents that act from rendered pixels avoid the fragility and heavy token cost of reading a page's HTML or accessibility tree, but training them depends on large amounts of high-quality interaction...
We present LAION-BVD, a large-scale open video dataset for multimodal learning, which contains 1.3B platform-specific video URLs collected from CommonCrawl. From these, we download 80M videos with a t...
While text-based hallucination detection has been extensively studied, spoken hallucination detection remains largely unexplored, particularly for low-resource languages. We present the first multilin...
Imaging factor disentanglement in text-to-image generation aims to independently control image acquisition properties such as types of camera lenses, sensor types, viewpoints, and domains to enable co...
Although recent Multimodal Large Language Models (MLLMs) have advanced general product understanding, they implicitly encode product information into global embeddings, thereby limiting their ability...
Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action semantics depend on coordinated roles and spatial-te...
End-to-end multimodal driving has progressed rapidly by fusing camera and LiDAR streams. Existing pipelines remain fragile under asymmetric sensor degradation, where either an entire modality or only...
The prediction of student engagement from the online tutoring videos is difficult because engagement is a multidimensional construct comprising distinct behavioral, emotional, and cognitive states. A...
Universal multimodal embeddings are becoming a core component of modern AI systems, enabling heterogeneous content to be represented in a shared space for applications such as retrieval, recommendatio...
arXiv:2604.08884v2 Announce Type: replace-cross
Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on RGB image understanding, yet their ability to use spectral evide...
By Xinyu Zhang, Zurong Mai, Qingmei Li, Xiaoya Fan, Zjin Liao, Haoyuan Liang, Yibin Wen, Yuhang Chen, Chan Tsz Ho, Bi Tianyuan, Ruifeng Su, Zihao Qiang, Juepeng Zheng, Jianxi Huang, Yutong Lu, Haohuan Fu
arXiv:2608.22713v1 Announce Type: new
Abstract: Clinical diagnosis requires progressive integration of patient history, physical examination, laboratory findings, medical images, and diagnostic-infor...
By Yufan Wang, Rui Yang, Yi Liu, Yi Lin, Yifan Peng
arXiv:2608.23484v1 Announce Type: new
Abstract: We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a mu...
By Naman Garg, Sarika Jain, George Fazekas