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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 ↗39 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.
- arXiv · Published · First archived
SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data ↗
Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by reconstructing modality features or designing complicated fusion mechanisms. However, these methods still suffer from spurious generation and noisy guidance due to the lack of high-level semantic grounding in partially observed multimodal evidence. To address these issues, we propose SemMSA, a latent semantic-aided framework that constructs rich sentiment-relevant…
- arXiv · Published · First archived
Multimodal Thinking with Renderable Programs ↗
Current vision-language models (VLMs) excel at visual content understanding and text-based reasoning, yet their structure limits the advancement of incorporating images into the reasoning chain. Though Omnimodal models have made efforts in unifying text and image generation, they focus on visual tasks in the open-domain, lacking tractability due to rasterized or latent representations of images. We introduce SVGLM, a framework that uses scalable vector graphics (SVG) primitives to connect text and image in reasoning tasks. We exploit the duality of SVG as both image description and text instru…
- arXiv · Published · First archived
C3M: Cross-Session Multimodal Memory Maintenance for Long-Horizon Tasks ↗
Long-horizon tasks require preserving and later recovering cross-session evidence under a bounded, query-blind memory budget. Existing compression can discard fine-grained visual cues or conflate semantically similar but incompatible observations. We present C3M, a cross-session multimodal memory organization that maintains a bounded active index over persistent source text-image evidence. Relation-aware updates consolidate safe redundancy while preserving complementary and incompatible records. At query time, budgeted routing selects useful index pages and expands their associated source evid…
- arXiv · Published · First archived
A Multimodal Dataset for Survival Prediction in Resected Pancreatic Ductal Adenocarcinoma ↗
Survival research in pancreatic ductal adenocarcinoma (PDAC) is limited by the scarcity of datasets linking whole-slide histology with clinical, molecular, and long-term outcome data. We present a retrospective single-centre cohort of 302 patients who underwent PDAC resection at University Medical Center Gottingen. The dataset comprises 446 H&E whole-slide images, clinicopathological variables, targeted sequencing data for 154 patients, and overall-survival outcomes. During follow-up, 253 patients died, and the median follow-up was 76 months. To establish initial reference values, we evaluated…
- OpenAlex · Published · First archived
Predicting prostate cancer mortality using UK Biobank data: a multimodal machine learning approach ↗
Prostate cancer remains a major cause of cancer-related mortality among men worldwide; however, it remains challenging to predict disease progression in individuals. While numerous tools attempt to forecast the likelihood of developing the cancer, fewer studies focus specifically on mortality prediction using population-scale clinical datasets. UK Biobank data were used to develop population-scale predictive models of prostate cancer mortality. The study assessed medical records, blood work, physical measurements, and genetic information of approximately 167 000 males aged 50 and above. A feed…
- GitHub · Repository created · First archived
shapsider/OmniJev ↗
OmniJev — multimodal finite-choice decision interface and MuJoCo embodied workbench: trajectory replays, decision probes, benchmark panels, 60s walkthrough.
- GitHub · Repository created · First archived
yoheinakajima/glance ↗
Ask an open vision-language model typed questions about an image and get probabilities back, on your own machine.
- GitHub · Repository created · First archived
slelly/awesome-GPT6-for-embodiedAI ↗
Evidence-aware GPT-6 Astra robotics, policy control, real2sim and evaluation resources.
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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