Unverified50% confidenceOpinionExact time
In the self-driving chemistry laboratory framework, MLLMs are expected to take on the role of the "embodied brain", that is, decomposing natural-language goals into executable actions and monitoring execution states.
1
Sources
50%
Confidence
Long-term
Relevance
11/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn the instruction fine-tuning phase of many MLLMs, task descriptions often immediately follow the evidence, which may cause the model to learn an inductive bias of prioritizing the most recently read evidence before responding.72% similarUnverifiedMobile agents typically use multimodal large models (MLLMs) as the decision-making core, with a typical architecture comprising three stages—perception, reasoning and planning, and action execution—often chained together as a 'reasoning-to-action' pipeline.68% similarUnverifiedThe architecture of current MLLMs typically trains language encoders and visual encoders separately before fusing them, causing models to struggle with deep cross-modal reasoning.68% similarUnverifiedMultimodal large language models (MLLMs) are large-scale neural network models capable of simultaneously processing and understanding multiple modalities such as text, images, audio, and video, typically based on the Transformer architecture, and acquiring cross-modal alignment capabilities through pretraining on large-scale multimodal data.67% similarUnverifiedMLE 的核心职责在于将研究阶段的 ML 模型转化为可在生产环境中稳定运行的系统,涉及推理加速、特征存储、在线/离线推理架构等工程问题66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1138382API
curl https://kongchang.com/api/v1/knowledge/claims/1138382MCP
get_claim(id=1138382)