{"schema_version":"onlylabs.public_signal.v1","title":"Moonshot AI (Kimi) Repo: MoonshotAI/WorldVQA","description":"Moonshot AI (Kimi) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/7509aa2f-f1f9-4e75-935c-923f935a5c79","json_url":"https://onlylabs.fyi/signals/7509aa2f-f1f9-4e75-935c-923f935a5c79/signal.json","generated_at":"2026-06-11T02:53:18.062503+00:00","org":{"slug":"moonshot","name":"Moonshot AI (Kimi)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/moonshot","dossier_json_url":"https://onlylabs.fyi/labs/moonshot/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/7509aa2f-f1f9-4e75-935c-923f935a5c79","signal_json":"https://onlylabs.fyi/signals/7509aa2f-f1f9-4e75-935c-923f935a5c79/signal.json","source":"https://github.com/MoonshotAI/WorldVQA","lab_dossier":"https://onlylabs.fyi/labs/moonshot","lab_dossier_json":"https://onlylabs.fyi/labs/moonshot/dossier.json","analysis":"https://onlylabs.fyi/analysis/moonshot","analysis_json":"https://onlylabs.fyi/analysis/moonshot/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/moonshot/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Moonshot AI (Kimi) published MoonshotAI/WorldVQA (Python). 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This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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Current evaluations often conflate visual knowledge retrieval with reasoning. In contrast, WorldVQA decouples these capabilities to strictly measure \"what the model memorizes.\" The benchmark assesses the atomic capability of grounding and naming visual..."},"evidence_pages":[{"url":"https://github.com/MoonshotAI/WorldVQA","final_url":"https://github.com/MoonshotAI/WorldVQA","title":"MoonshotAI/WorldVQA repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T02:53:18.062503+00:00","bytes":11401,"raw_path":"17ebf15d4673cc0d5a925773cee8e414375bd28654982ca875ff28ef60b25042.json","content_hash":"adb4b9c659ba91994ba3067c463864e8a9838ed4ccbe9c944b0412a548ab21c3","excerpt_chars":1200,"truncated":true,"excerpt":"MoonshotAI/WorldVQA Language: Python Stars: 118 Forks: 3 Open issues: 2 Created: 2026-01-26T07:26:43Z Pushed: 2026-02-04T07:27:04Z Default branch: master Fork: no Archived: no README: WorldVQA WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models [<img src=\"images/kimi_small.png\" width=\"16\" height=\"16\" style=\"vertical-align: middle;\"> HomePage](https://worldvqa2026.github.io/WorldVQA/) | [🤗 Dataset](https://huggingface.co/datasets/moonshotai/WorldVQA) | [<img src=\"images/arxiv_small.svg\" width=\"16\" height=\"16\" style=\"vertical-align: middle;\"> Paper](https://arxiv.org/abs/2602.02537v1) | [<img src=\"images/github_small.svg\" width=\"16\" height=\"16\" style=\"vertical-align: middle;\"> Code](https://github.com/MoonshotAI/WorldVQA/) ![alt text](images/barchart.png) Abstract We introduce WorldVQA, a benchmark designed to evaluate the atomic vision-centric world knowledge of Multimodal Large Language Models (MLLMs). Current evaluations often conflate visual knowledge retrieval with reasoning. 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