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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
elizabethadams5935/multimodal-generation-corpus — dataset update ↗
# Multimodal Generation Corpus A durability-focused research corpus of **AI-generated creative workflow media** — node-graph session snapshots and their associated rendered assets (thumbnails, stills, and encoded previews) collected from public community galleries for generative content platforms. ## Motivation Media corpora for generative workflows are commonly stored on ephemeral CDNs with high link-rot. This corpus packages a snapshot of such assets in a **sharded, integrity-checked, encrypted archival format** suitable for durability/replication research and offline analysis of generative
- OpenAlex · Published · First archived
Port-Agent: Structured Multimodal Reasoning for Port Disruption Analysis Using AIS, Satellite Imagery, and News ↗
Textual accounts establish essential context for port disruptions but can bias large language model synthesis before independent observations are examined. We present Port Agent, a remote-sensing agent that aligns Automatic Identification System (AIS) trajectories, Sentinel-2 vessel detections, and documents while retaining acquisition time, footprint, processing, and uncertainty. The evaluation follows the evidence chain from physical observation to agent synthesis across 12 views of 11 U.S. event-or-control groups, 24 quality-screened scenes, and 145,570,911 AIS positions. AIS first resolved…
- arXiv · Published · First archived
Walking the Embedding Space: Datastore Extraction from Multimodal RAG ↗
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of Multimodal Large Language Models (MLLMs) into relevant, up-to-date, external knowledge. Despite presenting several benefits, such as reducing hallucinatory behavior, they also introduce new attack surfaces, including leakage of private information and vulnerabilities against data extraction attacks. In this paper, we introduce $\immrag$, an adaptive and automatic data extraction attack procedure operating in a black box setting against \emph{image-…
- GitHub · Repository created · First archived
benmagnifico/DriveJev ↗
An open-source, efficient System I decision model for autonomous driving.
- Hugging Face · Model / dataset date · First archived
hustvl/Multimodal-Flow — model update ↗
# Multimodal Flow **Unified Flow Modeling of Language and Vision in Embedding Spaces** Multimodal Flow (MF) is a fully continuous framework for multimodal understanding and generation. Language and vision remain continuous states, organized as ordered hyperchunks in a shared causal stream. The same model interface supports text continuation, image understanding, and image generation. ## Authors [Hongyuan Tao](https://github.com/Hongyuan-Tao), [Xinggang Wang](https://xwcv.github.io/), [Lianghui Zhu](https://lh-zhu.github.io/index.html), [Yongkang Li](https://scholar.google.com/citations?user=F7
- OpenAlex · Published · First archived
A Multimodal Knowledge Graph Completion Method for Human–Robot Collaborative Maintenance of Industrial Robots Toward Industry 5.0 ↗
Industrial robot maintenance knowledge graphs often contain missing relationships, while semantic differences among maintenance text, graph structure, and operational condition signals complicate multimodal link prediction. Sparsely connected entities further limit the available structural evidence. To address these challenges, this study proposes a prototype-enhanced multimodal knowledge contrastive learning (Pro-MKCL) framework. The framework combines modality-specific encoders, multi-head neighborhood attention, and a cross-modal Transformer to learn integrated entity representations. Ontol…
- arXiv · Published · First archived
Multimodal Flow: Unified Flow Modeling of Language and Vision in Embedding Spaces ↗
We present Multimodal Flow, a fully continuous generative model of language and vision. Most unified multimodal models either model both language and quantized images as discrete tokens or combine discrete language prediction with continuous image generation. The former introduces a visual quantization bottleneck. The latter requires modality-dependent objectives and sampling procedures. Fully continuous modeling avoids these trade-offs and enables a shared generative process, but remains underexplored for multimodal pretraining. Multimodal Flow introduces a unified continuous architecture tha…
- arXiv · Published · First archived
Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis ↗
Multimodal large language models (MLLMs) are rapidly advancing clinical diagnosis, yet their adaptation pipelines remain anchored to accuracy-based objectives. Clinical data are heavily class-imbalanced: a constant-majority predictor can score above 90% accuracy while being clinically useless. We therefore evaluate and optimize for AUROC, a threshold-free score that ranks positives above negatives and is invariant to class balance. We focus on prompt optimization in MLLMs. Reflective methods such as GEPA use a binary scores matrix with one row per evaluation instance and one column per candida…
- arXiv · Published · First archived
WorldAuditBench: Interactive 3D World Auditing with Multimodal Agents ↗
As interactive 3D worlds are increasingly used to study intelligent behavior, it becomes important to develop efficient pipelines for identifying anomalies in these simulated environments, such as floating objects, traversable walls, or objects inconsistent with the surrounding scene. Multimodal AI systems, including vision-language models (VLMs) and vision-language-action models (VLAs), have shown potential for automating this task. However, 3D world auditing is complex, requiring the close coupling of two distinct capabilities: action, to navigate the 3D world and search for anomalies system…
- GitHub · Repository created · First archived
sirkosophia/Where-OPD ↗
Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes. Official implementation.
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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