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Earlier sources: Multimodal AI systems
Multimodal AI systems process or generate more than one kind of information, such as text, images, audio, and video. Follow model architectures, cross-modal reasoning, interfaces, and evaluation.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗38 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- Hugging Face · Model / dataset date · First archived
gter1216/glm-reduce-1m-multimodal-fp32 — model update ↗
# GLM Reduce — frozen FP32 weights Author: Charles Xu. 986,520 parameters, frozen 2026-10-09. `model.safetensors` contains the trained FP32 tensors; `config.json` defines the reduced network. This weight-only repository does not include the executable model implementation or web server. Use the complete [CPU inference package](https://huggingface.co/gter1216/glm-reduce-1m-code) for installation and inference. The full package includes these same weights and pinned original Transformers source. `publication.json` identifies the GitLab source commit shared by both publications. Current evaluatio
- OpenAlex · Published · First archived
MSMFusion: A Method for Multiscale Dual-Path State Space Modeling for Multimodal Medical Image Fusion ↗
Multimodal medical image fusion aims to integrate information from different imaging techniques to generate a single fused image that is comprehensive and rich in detail. Structural and functional imaging provide high-resolution anatomical details and tissue metabolic information, respectively. Complementary fusion improves quantitative fusion quality and preserves structural and functional information in one view, which aids clinical interpretation. Currently, convolutional neural network-based methods struggle to efficiently capture global features, whereas Transformer-based models excel at…
- GitHub · Repository created · First archived
lattifai/awesome-EmbeddingGemma ↗
EmbeddingGemma2 resources: local search apps, decision models, inference frameworks, tutorials, and datasets. English / 简体中文.
- OpenAlex · Published · First archived
Multimodal feature importance ranking improves classroom teaching quality evaluation in human and artificial intelligence collaborative education ↗
Classroom Teaching Quality Evaluation (CTQE) constitutes a core component of human-AI collaborative education systems; however, existing methods exhibit significant deficiencies in the systematic importance ranking of multimodal features. This paper proposes the Multimodal Feature Importance Ranking via Multi-model Ensemble framework (MFIR-ME), which integrates five categories of modal features—acoustic, linguistic, visual, temporal, and behavioral—and incorporates a Cross-Modal Attention Fusion module (CMAF) to achieve dynamically weighted adaptive cross-modal information integration. Four co…
- OpenAlex · Published · First archived
TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry ↗
Artificial intelligence (AI) has transformed molecular prediction and design, yet these advances remain only partially integrated with experimental discovery. We define TechBio 3.0 as an emerging paradigm for closed-loop drug discovery that connects multimodal molecular representations, generative chemistry, and automated experimentation. We examine its early preclinical and clinical evidence, identify remaining technical and translational barriers, and argue that this convergence is beginning to define biotechnology's next era.
- arXiv · Published · First archived
OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video ↗
Single-image, multi-image, and video deep research require different visual operations but share a workflow of visual grounding, external retrieval, and fact composition. A key challenge is to preserve the dependencies linking localized visual anchors, entity relations, source-supported facts, and answer-producing operations. We introduce OneSearch-VL, a unified agent centered on the Visually Grounded Evidence Graph (VGEG), which encodes these dependencies as a shared task-level reference for data construction, process supervision, and operation-level evaluation. Our VGEG-based data engine con…
- arXiv · Published · First archived
WOVEN: Weaving Visual World Modeling into Multimodal LLMs ↗
Multimodal large language models (MLLMs) struggle with spatial, embodied, physical, and temporal reasoning. We hypothesize that these failures reflect a shared deficit in visual transition reasoning, and test whether this capability can serve as a shared training primitive, one that different models can learn from different supervision sources and reuse across different tasks, with a systematic training recipe. Existing benchmarks document these deficits separately but do not support controlled comparisons across scenes, actions, and reasoning operations. We therefore introduce WOVEN, a traini…
- Hacker News · Posted on Hacker News · First archived
Liquid AI releases Pareto-frontier, multimodal decision model for the edge ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Multimodal Gene Expression Deep Learning for Predicting Sentinel Lymph Node Macro-metastasis in Early Breast Cancer: Development and Validation in the SCAN-B Cohort ↗
Abstract Background: This study evaluates DL using GEX and preoperatively available clinical data (PreopClinic) to predict SLNM, and explores their potential for guiding axillary surgery and prognostic assessment. Methods: We retrospectively included 6,836 clinically node-negative T1-T2 patients with invasive breast cancer who underwent primary surgery from the SCAN-B. Three DL models—a multilayer perceptron, a pathway-informed sparse neural network, and a transformer—were developed using the development set (n=4,625) and evaluated against XGBoost in the independent test set (n=2,211). Results…
- GitHub · Repository created · First archived
Alpha-Park/genpark-agentic-multimodal-invoice-receipt-reconciler-skill ↗
Autonomous Multi-Modal Invoice & Receipt Reconciler. Parses structured financial receipts, invoices, and bank statements, performs tolerance-based three-way reconciliation, detects tax calculation drift and duplicate claims, and synthesizes double-entry bookkeeping journal entries.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Multimodal AI systems
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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