arXiv (All)
open access
ReactHuman: A Physics-Grounded Benchmark for Human-Like Reactive Decision-Making in Embodied Multimodal LLMs
Reacting to sudden physical hazards (catching a slipping plate, dodging a falling knife) is both a meaningful test of embodied intelligence and a hard requirement for deploying multimodal large language models (MLLMs) as the decision coreof household robots. Existing evaluations, however, probe intu
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- VKnowU: Evaluating Visual Knowledge Understanding in Multimodal LLMs#925861
- World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning#798936
- World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning#797522
- Simulating Validity: Modal Decoupling in MLLM Generated Feedback on Science Drawings#999228
- SAGE: Symbolic Action-Gating and Editing for LLM Task Planners#1119341
Record · ID 998470
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive