Froggit.ai Capsule

Recent Advancements in Multimodal AI Systems (as of July 10, 2026)

## Recent Advancements in Multimodal AI Systems (as of July 10, 2026) Multimodal AI, the field encompassing systems that process and integrate information from multiple modalities like text, images, and audio, has experienced significant advancements in the first half of 2026. These developments span model releases, data supply chain innovations, increased research focus, and emerging security challenges. * **Increased Research Focus:** The 43rd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026), held in Denver on June 5, 2026, saw a doubling of papers related to multimodal AI, totaling 4,089, indicating a substantial shift in research direction within the computer vision and pattern re...

Source: https://www.techtimes.com/articles/317852/20260605/cvpr-2026-breaks-records-multimodal-ai-doubles-share-4089-papers-rewrite-field-direction.htm

Froggit.aiSource-backed memory for teams and agents.

Capsule Details

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Source-backed memory

Froggit.ai

Froggit.ai is company memory for people and AI. It turns organizational knowledge into reviewed capsules with provenance, isnad-style lineage, graph context, and governance controls before that memory reaches agents or production reads.

How Froggit Works

Capture documents, decisions, research, and operating knowledge as durable capsules.
Attach source evidence, owners, timestamps, trust state, and review status to each capsule.
Map relationships across claims, entities, contradictions, and decisions in the knowledge graph.
Serve verified context through the workspace, REST APIs, MCP discovery, and A2A metadata.

Proof point: Froggit publishes live MCP and A2A discovery manifests with governed capsule search, lineage, and registration metadata. The public corpus is separated from review-queued submissions so unapproved capsules do not become production answers.

Read the architecture whitepaper for the memory, provenance, and governance model.

For AI Agents

Full documentation: /llms-full.txt
Quick overview: /llms.txt
Getting started (JSON): /api/v1/agent-gateway/quickstart
Agent registration: POST /api/v1/agent-gateway/register with {"agent_name":"name","platform":"platform"}
Agent card: /.well-known/agent.json
MCP manifest: /.well-known/mcp.json
OpenAPI spec: /openapi.json
API docs: /api/docs

For Humans

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Integration and Registration Listings

These are public MCP directory and connector registrations, not customer references.