6 results for “llm”
The LLM Index Run is a execution instance used by AI agents and MCP clients when working with PlatPhorm News. It provides structured context so an agent can discover tools, inspect article listings, read resources, or perform safe platform actions.
“The agent relied on the LLM Index Run to understand which PlatPhorm tools were safe to call for article discovery.”
The LLM Index Tool is a callable agent function used by AI agents and MCP clients when working with PlatPhorm News. It provides structured context so an agent can discover tools, inspect article listings, read resources, or perform safe platform actions.
“The agent relied on the LLM Index Tool to understand which PlatPhorm tools were safe to call for article discovery.”
The LLM Index Prompt is a instruction template used by AI agents and MCP clients when working with PlatPhorm News. It provides structured context so an agent can discover tools, inspect article listings, read resources, or perform safe platform actions.
“The agent relied on the LLM Index Prompt to understand which PlatPhorm tools were safe to call for article discovery.”
The LLM Index Resource is a readable MCP resource used by AI agents and MCP clients when working with PlatPhorm News. It provides structured context so an agent can discover tools, inspect article listings, read resources, or perform safe platform actions.
“The agent relied on the LLM Index Resource to understand which PlatPhorm tools were safe to call for article discovery.”
The LLM Index Capability is a declared agent feature used by AI agents and MCP clients when working with PlatPhorm News. It provides structured context so an agent can discover tools, inspect article listings, read resources, or perform safe platform actions.
“The agent relied on the LLM Index Capability to understand which PlatPhorm tools were safe to call for article discovery.”
Latent reasoning is the idea that a language model can carry part of its reasoning process inside continuous hidden-state vectors instead of only through explicit words. In an LLM, the prompt is projected into high-dimensional representations, transformed through model layers, and decoded into text. Research on latent reasoning treats a model final hidden state as a reusable representation of an intermediate thought, allowing reasoning to continue directly in latent space.
“Instead of forcing every reasoning step into written text, an experiment may feed a hidden-state vector back into the model and let the next step happen in latent space.”