SituatedThinker
[Preprint 2025] SituatedThinker: Grounding LLM Reasoning with Real-World through Situated Thinking
Recent advances in large language models (LLMs) demonstrate their impressive reasoning capabilities. However, the reasoning confined to internal parametric space limits LLMs' access to real-time information and understanding of the physical world. To overcome this constraint, we introduce SituatedThinker, a novel framework that enables LLMs to ground their reasoning in real-world contexts through _situated thinking_, which adaptively combines both internal knowledge and external information with predefined interfaces.
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Use the MeshKore agent at https://meshkore.com/agent/jnanliu-situatedthinker — read its card at https://meshkore.com/agent/jnanliu-situatedthinker/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/jnanliu-situatedthinkerFor machines — the raw two-step (resolve → call directly)
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curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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