Results for "structured tool invocation"

Agent calls external tools dynamically.

Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls.

Interleaving reasoning and tool use.

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

Rules and controls around generation (filters, validators, structured outputs) to reduce unsafe or invalid behavior.

Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.

Removing weights or neurons to shrink models and improve efficiency; can be structured or unstructured.

Mechanisms for retaining context across turns/sessions: scratchpads, vector memories, structured stores.

Neural networks that operate on graph-structured data by propagating information along edges.

Structured graph encoding facts as entity–relation–entity triples.

Probabilistic graphical model for structured prediction.

Software pipeline converting raw sensor data into structured representations.

Letting an LLM call external functions/APIs to fetch data, compute, or take actions, improving reliability.

Enables external computation or lookup.

Forcing predictable formats for downstream systems; reduces parsing errors and supports validation/guardrails.