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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
A Multimodal Autonomic Sensing Framework for Objective Assessment of Patient Responses to Dental Pulp Stimulation ↗
Patient responses to dental pulp testing, ranging from no sensation to intense pain, provide important information for assessing pulp status in endodontic diagnosis. However, pain is a subjective sensory and emotional experience that varies considerably across individuals and can be difficult to communicate. We investigated whether complementary autonomic signals could support objective assessment of responses during dental examination. Forty-nine patients underwent cold pulp testing, yielding no-response, mild-response, and intense-response conditions. The framework integrated ECG-derived ski…
- arXiv · Published · First archived
RefineDrive: Reliable Failure-Guided Learning for Vision-Language-Action Driving ↗
Vision-Language-Action (VLA) models for autonomous driving rely heavily on successful expert demonstrations, leaving model-specific failures underexploited. Learning from these failures is hindered by unreliable diagnoses, poorly matched correction targets, and coarse rewards. We propose RefineDrive, a failure-guided post-training framework that learns from self-generated failures through targeted supervision and safety-aware reinforcement learning. Reliable Diagnosis derives structured, verifiable feedback on collisions and drivable-area violations directly from simulator states. Minimum-Corr…
- GitHub · Repository created · First archived
Alpha-Park/genpark-hubx-multimodal-mobile-agent-mcp ↗
Native Model Context Protocol (MCP) server exposing HubX's mobile-first multimodal toolchain: vision diagnostics, voice transcription, photo-realistic generation, and spatial design.
- GitHub · Repository created · First archived
alphaparkinc/genpark-hubx-multimodal-mobile-agent-mcp ↗
Native Model Context Protocol (MCP) server exposing HubX's mobile-first multimodal toolchain: vision diagnostics, voice transcription, photo-realistic generation, and spatial design.
- GitHub · Repository created · First archived
Alpha-Park/genpark-hubx-ai-apps-orchestration-skill ↗
GenPark AI Agent Skill - HubX intelligent AI apps portfolio, multi-model chatbot orchestration, vision diagnostics & generative tools distilled from hubx.co/products.
- arXiv · Published · First archived
UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models ↗
Unified multimodal models combine understanding, generation, and editing within a single network, offering a promising foundation for versatile multimodal applications. However, growing multimodal contexts make KV cache storage and access increasingly costly. Existing KV cache compression methods are typically tailored to specific tasks and single-modality caches, while overlooking changes in cache importance across tasks and timesteps. However, in unified multimodal models, each task involves multiple KV cache types, and both their composition and dynamics differ across tasks. As a result, a…
- GitHub · Repository created · First archived
SoMarkAI/mJev ↗
Jev, with senses.
- arXiv · Published · First archived
SatNav: A Scalable Benchmark for Long-Horizon UAV Vision-Language Navigation from Satellite Imagery ↗
Urban uncrewed aerial vehicle (UAV) vision-language navigation (VLN) requires agents to follow instructions across extended urban spaces, inherently demanding long-term memory and geospatial grounding. However, scaling existing benchmarks remains difficult because of their reliance on costly reconstructed 3D assets, limiting geographic diversity and episode scale. To address this, we introduce SatNav, a scalable, long-horizon UAV VLN benchmark built from high-resolution satellite imagery. SatNav targets city-level navigation missions and uses satellite crops as approximations of UAV nadir view…
- OpenAlex · Published · First archived
Multimodal Evaluation of Exoskeleton Assistance Using Optimized Machine Learning ↗
Repetitive manual material handling (MMH) remains a leading occupational risk factor for work-related musculoskeletal disorders (WMSD), and passive shoulder-support exoskeletons have emerged as promising assistive technologies. Most evaluations rely on group-level statistics from single sensor modalities, leaving open whether their effects can be detected at the individual level. Thirty-five adults performed low-risk (4.54 kg, Lift Index (LI) < 1.0) lifting tasks, derived from the Revised NIOSH Lifting Equation, with and without the passive shoulder exoskeleton support. Surface electromyograph…
- OpenAlex · Published · First archived
Conflict-aware commonsense knowledge distillation with hypergraph reasoning for robust multimodal sentiment analysis ↗
Existing graph-structured multimodal sentiment analysis (MSA) methods generally rely on heuristic topology construction and shallow statistical correlation modeling, making them susceptible to spurious semantic cues and modality bias under implicit sarcasm, semantic reversal, and cross-modal conflict scenarios. To address these limitations, this paper proposes CKD-MHR, a robust framework based on conflict-aware commonsense knowledge distillation and population manifold hypergraph reasoning. Specifically, the framework leverages a multimodal large language model (MLLM) to construct a commonsens…
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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