Genolator is presented, a multimodal large language model that integrates embeddings from DNA sequences, amino acid sequences, and protein structures with natural language queries and represents a step towards bridging genomic code and human language through the integration of a multimodal LLM.
M. Danner, Tanhim Islam, M. Begemann et al.· Genome Biology· 0 citations
Evaluating a diverse panel of contemporary models under three administration protocols, it is shown that a naive harness, with a fixed token budget and an unaudited parser, manufactures failing workers out of competent ones, misreading a model that answers essentially every item correctly as badly inaccurate, answer-bi...
Carlos Toxtli, M. Delaflor· Proceedings of the 2026 ACM...· 0 citations
This work proposes symbolic separation: a deep agent reasons freely but may act on data only through an ontology-constrained Virtual Knowledge Graph with deterministic pre-execution validation, like a tool API's interface contract, but which turns a complex question into one validated graph traversal instead of LLM-inf...
Baibek Davletiyarov, Junaid Ahmed Khan, Andrea Bartolini· 0 citations
BrainFocus is proposed, a reliable EEG-guided efficient VLM framework for VQA that improves VQA accuracy and demonstrates that EEG can guide efficient VLM inference even when its semantic decoding is imperfect.
Yi-Hui Peng, Guorui Lu, Qin-Yu Chen· 0 citations
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TecoPrompt, a closed-loop robust prompt-learning framework that revisits optimal transport pseudo-labeling from a temporal perspective, employs an entropic OT plan in the CLIP semantic space to obtain globally consistent label candidates.
Z. Shao, Hao-Wen Hua, Jia-Xin Zhang et al.· 0 citations
Results indicate that current LLMs are substantially more reliable at detecting broad collaboration opportunities than at identifying their specific types and role directions.
Tian Du, Tian-Tong Wu, Ya-Fei Wang et al.· 0 citations
Probe-VAD is proposed, an ordinal binary-probing framework that directly probes severity preferences from a frozen VLM, providing a simple interface for translating frozen VLM visual understanding into continuous, rank-sensitive anomaly scores without task-specific training or caption-based compression.
Jia-Wei Gu, Qi-Lin Zhao, Teng-Kuo Guo et al.· 1 citation
MASA (Multimodal-LLM-Anchored Semantic Adaptation), which complements model-internal evidence with structured semantic descriptions from a frozen multimodal large language model (MLLM) to limit inference cost.
Zhen-Bin Wang, Lei Zhang, Li-Tuan Wang et al.· 0 citations
FHIR has successfully catalyzed widespread technical innovation and serves as the foundation for advanced clinical applications, however, a significant translational gap persists and future efforts should expand beyond technical development.
Wafae Abbaoui, S. Ziti, Sara Retal· International Journal of Med...· 0 citations
It is found that datasets with more missing metadata receive fewer downstream citations and have fewer resolvable connections to other datasets, suggesting that geographic metadata is a more reliable basis for cross-disciplinary interoperability than keyword vocabularies, which often remain discipline-specific.
Differences in brain metastasis development between EGFR L858R and Ex19Del mutations appeared to be mitigated among patients undergoing osimertinib compared with early-generation TKIs, representing an important systems bioengineering approach.
Ameya Patel, Victor Lee, A. H. Nassar et al.· American Journal of Clinical...· 0 citations
A novel training framework that enhances counterfactual contrastive learning for VQA and introduces a three-stage curriculum for stable multi-objective optimization, an enhanced Batch-Contrastive loss for more discriminative feature learning and two novel regularizers.
Truong-Binh Duong, T. Tran, Ngoc-Thao Nguyen et al.· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
